The current state and future outlook of rescue robotics

The influence of digital technologies on resilient performance: Contributions, drawbacks, and a research agenda

The growing use of digital technologies (DTs) has a myriad of implications to socio-technical systems, which are not yet fully recognised. This paper investigates the contributions and drawbacks of DTs to resilient performance (RP), an aspect that so far has received less attention in comparison to others such as efficiency. To this end, a survey questionnaire was applied to 79 academics and practitioners linked to resilience engineering. Data analysis involved descriptive statistics and a thematic analysis of the open-text responses to the survey. Mixed impacts were identified, with 10 themes related to contributions and 16 to drawbacks. Regarding the contributions, the results highlighted the use of DTs for monitoring and anticipating system performance. Machine learning seems to be the most promising approach for this purpose. A key drawback is the need for developing new skills across the workforce so that they can make sense of the outputs of DTs and are aware of their strengths and weaknesses. The human role is expected to remain crucial for RP, which makes the current coordination difficulties with DTs even more important to address. A research agenda composed of five topics is proposed, encompassing description, prescription, and assessment. The agenda emphasizes the need for mapping the attributes or functionalities of DTs onto resilience concepts, models, and frameworks.

Development of a Highly Adaptive Miniature Piezoelectric Robot Inspired by Earthworms

Miniature resonant piezoelectric robots have the advantages of compact structure, fast response, high speed, and easy control, which have attracted the interest of many scholars in recent years. However, piezoelectric robots usually suffer from the problem of poor adaptability due to the micron-level amplitude at the feet. Inspired by the fact that earthworms have actuation trajectories all around their bodies to move flexibly under the ground, a miniature piezoelectric robot with circumferentially arranged driving feet to improve adaptability is proposed. Notably, a longitudinal-vibration-compound actuation principle with multilegged collaboration is designed to achieve the actuation trajectories around the robot, similar to the earthworms. The structure and operating principle are simulated by the finite element method, and the prototype is fabricated. The robot weighs 22.7 g and has dimensions of 35.5 × 36.5 × 47 mm3. The robot is tethered to an ultrasonic power supply, and the experimental results show that the speed reaches 179.35 mm s-1  under an exciting signal with a frequency of 58.5 kHz and a voltage of 200 Vp-p. High adaptability is achieved by the proposed robot, it can move on flat, fold, concave, and convex surfaces, and even in an inclined or rotating tube.

Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots

Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierarchical path planning approach driven by dynamic 3D point clouds is proposed in this article. The developed path planning model is essentially constituted of two layers: a global path planning layer, and a local path planning layer. At the global path planning layer, a new method is proposed for calculating the terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision areas and a strategy for temporary target point selection are developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experimental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maximum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is shortened by 20%, and more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).

Synergistic morphology and feedback control for traversal of unknown compliant obstacles with aerial robots

Animals traverse vegetation by direct physical interaction using their entire body to push aside and slide along compliant obstacles. Current drones lack this interaction versatility that stems from synergies between body morphology and feedback control modulated by sensing. Taking inspiration from nature, we show that a task-oriented design allows a drone with a minimalistic controller to traverse obstacles with unknown elastic responses. A discoid sensorized shell allows to establish and sense contacts anywhere along the shell and facilitates sliding along obstacles. This simplifies the formalization of the control strategy, which does not require a model of the interaction with the environment, nor high-level switching conditions for alternating between pushing and sliding. We utilize an optimization-based controller that ensures safety constraints on the robot's state and dampens the oscillations of the environment during interaction, even if the elastic response is unknown and variable. Experimental evaluation, using a hinged surface with three different stiffness values ranging from 18 to 155.5 N mm rad-1, validates the proposed embodied aerial physical interaction strategy. By also showcasing the traversal of isolated branches, this work makes an initial contribution toward enabling drone flight across cluttered vegetation, with potential applications in environmental monitoring, precision agriculture, and search and rescue.

Meaningful human control and variable autonomy in human-robot teams for firefighting

Introduction: Humans and robots are increasingly collaborating on complex tasks such as firefighting. As robots are becoming more autonomous, collaboration in human-robot teams should be combined with meaningful human control. Variable autonomy approaches can ensure meaningful human control over robots by satisfying accountability, responsibility, and transparency. To verify whether variable autonomy approaches truly ensure meaningful human control, the concept should be operationalized to allow its measurement. So far, designers of variable autonomy approaches lack metrics to systematically address meaningful human control. Methods: Therefore, this qualitative focus group (n = 5 experts) explored quantitative operationalizations of meaningful human control during dynamic task allocation using variable autonomy in human-robot teams for firefighting. This variable autonomy approach requires dynamic allocation of moral decisions to humans and non-moral decisions to robots, using robot identification of moral sensitivity. We analyzed the data of the focus group using reflexive thematic analysis. Results: Results highlight the usefulness of quantifying the traceability requirement of meaningful human control, and how situation awareness and performance can be used to objectively measure aspects of the traceability requirement. Moreover, results emphasize that team and robot outcomes can be used to verify meaningful human control but that identifying reasons underlying these outcomes determines the level of meaningful human control. Discussion: Based on our results, we propose an evaluation method that can verify if dynamic task allocation using variable autonomy in human-robot teams for firefighting ensures meaningful human control over the robot. This method involves subjectively and objectively quantifying traceability using human responses during and after simulations of the collaboration. In addition, the method involves semi-structured interviews after the simulation to identify reasons underlying outcomes and suggestions to improve the variable autonomy approach.

Animal robots in the African wilderness: Lessons learned and outlook for field robotics

In early 2016, we had the opportunity to test a pair of sprawling posture robots, one designed to mimic a crocodile and another designed to mimic a monitor lizard, along the banks of the Nile River in Uganda, Africa. These robots were developed uniquely for a documentary by the BBC called Spy in the Wild and fell at the intersection of our interests in developing robots to study animals and robots for disaster response and other missions in challenging environments. The documentary required that these robots not only walk and swim in the same harsh, natural environments as the animals that they were modeled on and film up close but also move and even look exactly like the real animals from an aesthetic perspective. This pushed us to take a fundamentally different approach to the design and building of biorobots compared with our typical laboratory-residing robots, in addition to collaborating with sculpting artists to enhance our robots' aesthetics. The robots needed to be designed on the basis of a systematic study of data on the model specimens, be fabricated rapidly, and be reliable and robust enough to handle what the wild would throw at them. Here, we share the research efforts of this collaboration, the design specifications of the robots' hardware and software, the lessons learned from testing these robots in the field first hand, and how the eye-opening experience shaped our subsequent work on disaster response robotics and biorobotics for challenging amphibious scenarios.

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

"Are we in this together?": embedding social identity detection in drones improves emergency coordination

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders' decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors' talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.

Generating Topologically Consistent BIM Models of Utility Tunnels from Point Clouds

The development and utilization of urban underground space is an important way to solve the "great urban disease". As one of the most important types of urban underground foundations, utility tunnels have become increasingly popular in municipal construction. The investigation of utility tunnels is a general task and three-dimensional laser scanning technology has played a significant role in surveying and data acquisition. However, three-dimensional laser scanning technology suffers from noise and occlusion in narrow congested utility tunnel spaces, and the acquired point clouds are imperfect; hence, errors and redundancies are introduced in the extracted geometric elements. The topology of reconstructed BIM objects cannot be ensured. Therefore, in this study, a hierarchical segmentation method for point clouds and a topology reconstruction method for building information model (BIM) objects in utility tunnels are proposed. The point cloud is segmented into facades, planes, and pipelines hierarchically. An improved mean-shift algorithm is proposed to extract wall line features and a local symmetry-based medial axis extraction algorithm is proposed to extract pipelines from point clouds. A topology reconstruction method that searches for the neighbor information of wall and pipeline centerlines and establishes collinear, perpendicular, and intersecting situations is used to reconstruct a topologically consistent 3D model of a utility tunnel. An experiment on the Guangzhou's Nansha District dataset successfully reconstructed 24 BIM wall objects and 12 pipelines within the utility tunnel, verifying the efficiency of the method.

Autonomous Robots for Services-State of the Art, Challenges, and Research Areas

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.

Design and Analysis of a Wheel−Leg Hybrid Robot with Passive Transformable Wheels

This paper proposes a novel wheel−leg hybrid robot that can be applied on both flat and rugged terrains, it utilizes two passive transformable symmetrical wheels that combine the stability of the circular wheel and the obstacle climbing ability of the legged wheel. To minimize the number of actuators, the transformation process of the wheel is designed to be triggered passively when in contact with the obstacles. A new triggering mechanism is employed to eliminate the adverse effect of the robot’s weight on the transformation torque. The parameters of the wheel are optimized to maximize the climbing ability in low-friction conditions. The robot’s body length and angular velocity are also tuned based on the dynamic model during the obstacle climbing process. The simulation experiment results show that the robot can switch modes stably on terrain with a friction coefficient as low as 0.2, and can climb over an obstacle 3.9 times as tall as its wheel radius.

Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments

Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.

The Cloud-to-Edge-to-IoT Continuum as an Enabler for Search and Rescue Operations

When a natural or human disaster occurs, time is critical and often of vital importance. Data from the incident area containing the information to guide search and rescue (SAR) operations and improve intervention effectiveness should be collected as quickly as possible and with the highest accuracy possible. Nowadays, rescuers are assisted by different robots able to fly, climb or crawl, and with different sensors and wireless communication means. However, the heterogeneity of devices and data together with the strong low-delay requirements cause these technologies not yet to be used at their highest potential. Cloud and Edge technologies have shown the capability to offer support to the Internet of Things (IoT), complementing it with additional resources and functionalities. Nonetheless, building a continuum from the IoT to the edge and to the cloud is still an open challenge. SAR operations would benefit strongly from such a continuum. Distributed applications and advanced resource orchestration solutions over the continuum in combination with proper software stacks reaching out to the edge of the network may enhance the response time and effective intervention for SAR operation. The challenges for SAR operations, the technologies, and solutions for the cloud-to-edge-to-IoT continuum will be discussed in this paper.

Optimal path planning using a continuous anisotropic model for navigation on irregular terrains

Mobile robots usually need to minimize energy when they are traversing uneven terrains. To reach a location of interest, one strategy consists of making the robot follow the path that demands the least possible amount of energy. Yet, its calculation is not trivial with irregular surfaces. Gravity makes the energy consumption of a robot change according to its heading. Such a variation is subject to the terramechanic characteristics of the surface. This paper introduces a cost function that addresses this variation when traversing slopes. This function presents direction-dependency (anisotropic) and returns the cost for all directions (continuous).. Moreover, it is compatible with the Ordered Upwind Method, which allows finding globally optimal paths in a deterministic way. Besides, the segments of these paths are not restricted to the shape of a grid. Finally, this paper also introduces the description and discussion of a simulation experiment. It served to analyse what kinds of terrain motivate the use of anisotropy. The Ordered Upwind Method was executed on a virtual crater with different terrain parameter configurations, using both isotropic (direction-non-dependent) and anisotropic cost functions. The results evince how in certain situations the use of an anisotropic cost function instead of an isotropic one produces a path that reduces the accumulated cost by up to 20%.

Miniature Amphibious Robot Actuated by Rigid-Flexible Hybrid Vibration Modules

Amphibious robots can undertake various tasks in terrestrial and aquatic environments for their superior environmental compatibility. However, the existing amphibious robots usually utilize multi-locomotion systems with transmission mechanisms, leading to complex and bulky structures. Here, a miniature amphibious robot based on vibration-driven locomotion mechanism is developed. The robot has two unique rigid-flexible hybrid modules (RFH-modules), in which a soft foot and a flexible fin are arranged on a rigid leg to conduct vibrations from an eccentric motor to the environment. Then, it can run on ground with the soft foot adopting the friction locomotion mechanism and swim on water with the flexible fin utilizing the vibration-induced flow mechanism. The robot is untethered with a compact size of 75 × 95 × 21 mm³ and a small weight of 35 g owing to no transmission mechanism or joints. It realizes the maximum speed of 815 mm s^-1 on ground and 171 mm s^-1 on water. The robot, actuated by the RFH-modules based on vibration-driven locomotion mechanism, exhibits the merits of miniature structure and fast movements, indicating its great potential for applications in narrow amphibious environments.

Let's Work Together: A Meta-Analysis on Robot Design Features That Enable Successful Human-Robot Interaction at Work

OBJECTIVE

This meta-analysis reviews robot design features of interface, controller, and appearance and statistically summarizes their effect on successful human-robot interaction (HRI) at work (that is, task performance, cooperation, satisfaction, acceptance, trust, mental workload, and situation awareness).

BACKGROUND

Robots are becoming an integral part of many workplaces. As interactions with employees increase, ensuring success becomes ever more vital. Even though many studies investigated robot design features, an overview on general and specific effects is missing.

METHOD

Systematic selection of literature and structured coding led to 81 included experimental studies containing 380 effect sizes. Mean effects were calculated using a three-level meta-analysis to handle dependencies of multiple effect sizes in one study.

RESULTS

Sufficient feedback through the interface, clear visibility of affordances, and adaptability and autonomy of the controller significantly affect successful HRI, whereas appearance does not. The features of the interface and controller affect performance and satisfaction but do not affect situation awareness and trust. Specific effects of adaptability on cooperation and acceptance, as well as autonomy on mental workload, could be shown.

CONCLUSION

Robot design at work needs to cover multiple features of interface and controller to achieve successful HRI that covers not only performance and satisfaction, but also cooperation, acceptance, and mental workload. More empirical research is needed to investigate mediating mechanisms and underrepresented design features' effects.

APPLICATION

Robot designers should carefully choose design features to balance specific effects and implementation costs with regard to tasks, work design aims, and employee needs in the specific work context.

Rescue Robots for the Urban Earthquake Environment

Robotics have important applications in the field of disaster medical rescue. The deployment of urban rescue robots at the earthquake site can help shorten response time, improve rescue efficiency and keep rescue personnel away from danger. This discussion introduces the performance of some robots in actual rescue scenarios, focuses on the current research status of robots that can provide medical assistance, and analyzes the merits and shortcomings of each system. Based on existing studies, the limitations and development directions of urban rescue robots are also discussed.

Wilderness Search for Lost Persons Using a Multimodal Aerial-Terrestrial Robot Team

Mobile robots that are capable of multiple modes of locomotion may have tangible advantages over unimodal robots in unstructured and non-homogeneous environments due to their ability to better adapt to local conditions. This paper specifically considers the use of a team of multimodal robots capable of switching between aerial and terrestrial modes of locomotion for wilderness search and rescue (WiSAR) scenarios. It presents a novel search planning method that coordinates the members of the robotic team to maximize the probability of locating a mobile target in the wilderness, potentially, last seen on an a priori known trail. It is assumed that the search area expands over time and, thus, an exhaustive search is not feasible. Earlier research on search planning methods for heterogeneous though unimodal search teams have exploited synergies between robots with different locomotive abilities through coordination and/or cooperation. Work on multimodal robots, on the other hand, has primarily focused on their mechanical design and low-level control. In contrast, our recent work, presented herein, has two major components: (i) target-motion prediction in the presence of a priori known trails in the wilderness, and (ii) probability-guided multimodal robot search-trajectory generation. For the former sub-problem, the novelty of our work lies in the formulation and use of 3D probability curves to capture target distributions under the influence of a priori known walking/hiking trails. For the latter, the novelty lies in the use of a tree structure to represent the decisions involved in multimodal probability-curve-guided search planning, which enables trajectory generation and mode selection to be optimized simultaneously, for example, via a Monte Carlo tree search technique. Extensive simulations, some of which are included herein, have shown that multimodal robotic search teams, coordinated via the trajectory planning method proposed in this paper, clearly outperform their unimodal counterparts in terms of search success rates.

Generation of Dynamically Feasible Window Traversing Quadrotor Trajectories Using Logistic Curve

This work considers dynamically feasible point-to-point trajectory generation problem for a quadrotor flying through a constrained planner region referred as window (narrow gap). A four parameter logistic (4PL) curve is investigated as a prospective candidate and closed-form conditions are derived on the 4PL design parameters to satisfy the window traversability and vehicle dynamic feasibility constraints. A hierarchical approach first computes a dynamically feasible design parameter set for decoupled trajectory components and then obtains a solution set satisfying 3-D axis-coupled window traversability conditions. Numerical examples with a comparative study are presented to validate the analytical findings that highlight the quick computation of the dynamically feasible window traversing trajectories in complex window scenarios.

Still Not Solved: A Call for Renewed Focus on User-Centered Teleoperation Interfaces

Teleoperation is one of the oldest applications of human-robot interaction, yet decades later, robots are still difficult to control in a variety of situations, especially when used by non-expert robot operators. That difficulty has relegated teleoperation to mostly expert-level use cases, though everyday jobs and lives could benefit from teleoperated robots by enabling people to get tasks done remotely. Research has made great progress by improving the capabilities of robots, and exploring a variety of interfaces to improve operator performance, but many non-expert applications of teleoperation are limited by the operator’s ability to understand and control the robot effectively. We discuss the state of the art of user-centered research for teleoperation interfaces along with challenges teleoperation researchers face and discuss how an increased focus on human-centered teleoperation research can help push teleoperation into more everyday situations.

BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching

Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to support the robot's mass during stance and the rapid cycling of the leg's state between stance and swing phases. Here, we demonstrate an avian-inspired robot leg design, BirdBot, that challenges the reliance on rapid feedback control for joint coordination and replaces active control with intrinsic, mechanical coupling, reminiscent of a self-engaging and disengaging clutch. A spring tendon network rapidly switches the leg's slack segments into a loadable state at touchdown, distributes load among joints, enables rapid disengagement at toe-off through elastically stored energy, and coordinates swing leg flexion. A bistable joint mediates the spring tendon network's disengagement at the end of stance, powered by stance phase leg angle progression. We show reduced knee-flexing torque to a 10th of what is required for a nonclutching, parallel-elastic leg design with the same kinematics, whereas spring-based compliance extends the leg in stance phase. These mechanisms enable bipedal locomotion with four robot actuators under feedforward control, with high energy efficiency. The robot offers a physical model demonstration of an avian-inspired, multiarticular elastic coupling mechanism that can achieve self-stable, robust, and economic legged locomotion with simple control and no sensory feedback. The proposed design is scalable, allowing the design of large legged robots. BirdBot demonstrates a mechanism for self-engaging and disengaging parallel elastic legs that are contact-triggered by the foot's own lever-arm action.

Negotiating Uneven Terrain by a Simple Teleoperated Tracked Vehicle with Internally Movable Center of Gravity

We propose a mechanical design for a simple teleoperated unmanned ground vehicle (UGV) to negotiate uneven terrain. UGVs are typically classified into legged, legged-wheeled, wheeled, and tanked forms. Legged vehicles can significantly shift their center of gravity (COG) by positioning their multi-articulated legs at appropriate trajectories, stepping over a high obstacle. To realize a COG movable mechanism with a small number of joints, a number of UGVs have been developed that can shift their COG by moving a mass at a high position above the body. However, these tend to pose a risk of overturning, and the mass must be moved quite far to climb a high step. To address these issues, we design a novel COG shift mechanism, in which the COG can be shifted forward and backward inside the body by moving most of its internal devices. Since this movable mass includes DC motors for driving both tracks, we can extend the range of the COG movement. We demonstrate that a conventional tracked vehicle prototype can traverse a step and a gap between two steps, as well as climb stairs and a steep slope, with a human operating the vehicle movement and the movable mass position.

A bio-inspired robotic climbing robot to understand kinematic and morphological determinants for an optimal climbing gait

Robotic systems for complex tasks, such as search and rescue or exploration, are limited for wheeled designs, thus the study of legged locomotion for robotic applications has become increasingly important. To successfully navigate in regions with rough terrain, a robot must not only be able to negotiate obstacles, but also climb steep inclines. Following the principles of biomimetics, we developed a modular bio-inspired climbing robot, named X4, which mimics the lizard's bauplan including an actuated spine, shoulders, and feet which interlock with the surface via claws. We included the ability to modify gait and hardware parameters and simultaneously collect data with the robot's sensors on climbed distance, slip occurrence and efficiency. We first explored the speed-stability trade-off and its interaction with limb swing phase dynamics, finding a sigmoidal pattern of limb movement resulted in the greatest distance travelled. By modifying foot orientation, we found two optima for both speed and stability, suggesting multiple stable configurations. We varied spine and limb range of motion, again showing two possible optimum configurations, and finally varied the centre of pro- and retraction on climbing performance, showing an advantage for protracted limbs during the stride. We then stacked optimal regions of performance and show that combining optimal dynamic patterns with either foot angles or ROM configurations have the greatest performance, but further optima stacking resulted in a decrease in performance, suggesting complex interactions between kinematic parameters. The search of optimal parameter configurations might not only be beneficial to improve robotic in-field operations but may also further the study of the locomotive evolution of climbing of animals, like lizards or insects.

Service Robots: Trends and Technology

The 2021 sales volume in the market of service robots is attractive. Expert reports from the International Federation of Robotics confirm 27 billion USD in total market share. Moreover, the number of new startups with the denomination of service robots nowadays constitutes 29% of the total amount of robotic companies recorded in the United States. Those data, among other similar figures, remark the need for formal development in the service robots area, including knowledge transfer and literature reviews. Furthermore, the COVID-19 spread accelerated business units and some research groups to invest time and effort into the field of service robotics. Therefore, this research work intends to contribute to the formalization of service robots as an area of robotics, presenting a systematic review of scientific literature. First, a definition of service robots according to fundamental ontology is provided, followed by a detailed review covering technological applications; state-of-the-art, commercial technology; and application cases indexed on the consulted databases.

Autonomous Thermal Vision Robotic System for Victims Recognition in Search and Rescue Missions

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper’s main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot’s automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method’s efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.

Remote Modular Electronics for Wireless Magnetic Devices

Small-scale wireless magnetic robots and devices offer an effective solution to operations in hard-to-reach and high-risk enclosed places, such as inside the human body, nuclear plants, and vehicle infrastructure. In order to obtain functionalities beyond the capability of magnetic forces and torques exerted on magnetic materials used in these robotic devices, electronics need to be also integrated into them. However, their capabilities and power sources are still very limited compared to their larger-scale counterparts due to their much smaller sizes. Here, groups of milli/centimeter-scale wireless magnetic modules are shown to enable on-site electronic circuit construction and operation of highly demanding wireless electrical devices with no batteries, that is, with wireless power. Moreover, the mobility of the modular components brings remote modification and reconfiguration capabilities. When these small-scale robotic modules are remotely assembled into specific geometries, they can achieve, if not impossible, challenging electrical tasks for individual modules. Using such a method, several wireless and battery-free robotic devices are demonstrated using milli/centimeter-scale robotic modules, such as a wireless circuit to power light-emitting diodes with lower external fields, a device to actuate relatively high force-output shape memory alloy actuators, and a wireless force sensor, all of which can be modified on-site.

2D-Material-integrated hydrogels as multifunctional protective skins for soft robots

Soft robots provide compliant object-machine interactions, but they exhibit insufficient material stability, which restricts them from working in harsh environments. Herein, we developed a class of soft robotic skins based on two-dimensional materials (2DMs) and gelatin hydrogels, featuring skin-like multifunctionality (stretchability, thermoregulation, threat protection, and strain sensing). The 2DM-integrated hydrogel (2DM/H) skins enabled soft robots to execute designated missions in the presence of high levels of heat and various environmental threats while maintaining mild machine temperatures. Via adopting different 2DMs (graphene oxide (GO), montmorillonite (MMT), and titanium carbide (MXene)), the 2DM/H-protected robots were able to perform soft grasping in organic liquids (GO/H) and open fire (MMT/H), and in the presence of electromagnetic radiation and biocontamination (MXene/H). Through blending MXene nanosheets into gelatin, the MXene-blended hydrogel (M-H) skin became strain sensitive, and a GO/M-H gripper exhibited the high-level integration of skin-mimicking capabilities. Finally, we incorporated 2DM/H skins onto an origami-inspired walker robot and a soft batoid-like robot to execute vision-guided searching in fire and underwater locomotion/navigation in chemical spills.

ResQbot 2.0: An Improved Design of a Mobile Rescue Robot with an Inflatable Neck Securing Device for Safe Casualty Extraction

Despite the fact that a large number of research studies have been conducted in the field of search and rescue robotics, significantly little attention has been given to the development of rescue robots capable of performing physical rescue interventions, including loading and transporting victims to a safe zone—i.e., casualty extraction tasks. The aim of this study is to develop a mobile rescue robot that could assist first responders when saving casualties from a dangerous area by performing a casualty extraction procedure whilst ensuring that no additional injury is caused by the operation and no additional lives are put at risk. In this paper, we present a novel design of ResQbot 2.0—a mobile rescue robot designed for performing the casualty extraction task. This robot is a stretcher-type casualty extraction robot, which is a significantly improved version of the initial proof-of-concept prototype, ResQbot (retrospectively referred to as ResQbot 1.0), that has been developed in our previous work. The proposed designs and development of the mechanical system of ResQbot 2.0, as well as the method for safely loading a full-body casualty onto the robot’s ‘stretcher bed’, are described in detail based on the conducted literature review, evaluation of our previous work, and feedback provided by medical professionals. We perform simulation experiments in the Gazebo physics engine simulator to verify the proposed design and the casualty extraction procedure. The simulation results demonstrate the capability of ResQbot 2.0 to carry out safe casualty extractions successfully.

The UMA-SAR Dataset: Multimodal data collection from a ground vehicle during outdoor disaster response training exercises

This article presents a collection of multimodal raw data captured from a manned all-terrain vehicle in the course of two realistic outdoor search and rescue (SAR) exercises for actual emergency responders conducted in Málaga (Spain) in 2018 and 2019: the UMA-SAR dataset. The sensor suite, applicable to unmanned ground vehicles (UGVs), consisted of overlapping visible light (RGB) and thermal infrared (TIR) forward-looking monocular cameras, a Velodyne HDL-32 three-dimensional (3D) lidar, as well as an inertial measurement unit (IMU) and two global positioning system (GPS) receivers as ground truth. Our mission was to collect a wide range of data from the SAR domain, including persons, vehicles, debris, and SAR activity on unstructured terrain. In particular, four data sequences were collected following closed-loop routes during the exercises, with a total path length of 5.2 km and a total time of 77 min. In addition, we provide three more sequences of the empty site for comparison purposes (an extra 4.9 km and 46 min). Furthermore, the data is offered both in human-readable format and as rosbag files, and two specific software tools are provided for extracting and adapting this dataset to the users’ preference. The review of previously published disaster robotics repositories indicates that this dataset can contribute to fill a gap regarding visual and thermal datasets and can serve as a research tool for cross-cutting areas such as multispectral image fusion, machine learning for scene understanding, person and object detection, and localization and mapping in unstructured environments. The full dataset is publicly available at: www.uma.es/robotics-and-mechatronics/sar-datasets .

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

In the last decade, the development of more compact and lightweight radiation detection systems led to their application in handheld and small unmanned systems, particularly air-based platforms. Examples of improvements are: the use of silicon photomultiplier-based scintillators, new scintillating crystals, compact dual-mode detectors (gamma/neutron), data fusion, mobile sensor networks, cooperative detection and search. Gamma cameras and dual-particle cameras are increasingly being used for source location. This study reviews and discusses the research advancements in the field of gamma-ray and neutron measurements using mobile radiation detection systems since the Fukushima nuclear accident. Four scenarios are considered: radiological and nuclear accidents and emergencies; illicit traffic of special nuclear materials and radioactive materials; nuclear, accelerator, targets, and irradiation facilities; and naturally occurring radioactive materials monitoring-related activities. The work presented in this paper aims to: compile and review information on the radiation detection systems, contextual sensors and platforms used for each scenario; assess their advantages and limitations, looking prospectively to new research and challenges in the field; and support the decision making of national radioprotection agencies and response teams in respect to adequate detection system for each scenario. For that, an extensive literature review was conducted.

A hybrid tactile sensor-based obstacle overcoming method for hexapod walking robots

Walking robots are considered as a promising solution for locomotion across irregular or rough terrain. While wheeled or tracked robots require flat surface like roads or driveways, walking robots can adapt to almost any terrain type. However, overcoming diverse terrain obstacles still remains a challenging task even for multi-legged robots with a high number of degrees of freedom. Here, we present a novel method for obstacle overcoming for walking robots based on the use of tactile sensors and generative recurrent neural network for positional error prediction. By using tactile sensors positioned on the front side of the legs, we demonstrate that a robot is able to successfully overcome obstacles close to robots height in the terrains of different complexity. The proposed method can be used by any type of a legged machine and can be considered as a step toward more advanced walking robot locomotion in unstructured terrain and uncertain environment.

Engineering Human–Machine Teams for Trusted Collaboration

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

Research on the Trajectory Planning of Demolition Robot Attachment Changing

The process of changing the attachment of a demolition robot is a complex operation and requires a high docking accuracy, so it is hard for operators to control this process remotely through the camera’s perspective. To solve this problem, this paper studies trajectory planning for changing a demolition robot attachment. This paper establishes a link parameter model of the demolition robot; the position and attitude of the attachment are obtained through a camera, the optimal docking point is calculated to minimize the distance error during angle alignment for attachment change, the inverse kinemics of the demolition robot are solved, the trajectory planning algorithm and visualization program are programmed, and then the trajectory planning for the demolition robot attachment changing method is proposed. The results of calculations and experiments show that the method in this paper can meet the accuracy, efficiency, and safety requirements of demolition robot attachment changing, and it has promising application prospects in the decommissioning and dismantling of nuclear facilities and other radioactive environments.

3D Registration and Integrated Segmentation Framework for Heterogeneous Unmanned Robotic Systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.