The Energy Efficiency Multi-Robot System and Disinfection Service Robot Development in Large-Scale Complex Environment

In recent years, multi-robot control systems and service robots equipped with graphical computing have been introduced in various application scenarios. However, the long-term operation of VSLAM calculation leads to reduced energy efficiency of the robot, and accidental localization failure still persists in large-scale fields with dynamic crowds and obstacles. This study proposes an EnergyWise multi-robot system based on ROS that actively determines the activation of VSLAM using real-time fused localization poses by an innovative energy-saving selector algorithm. The service robot is equipped with multiple sensors and utilizes the novel 2-level EKF method and incorporates the UWB global localization mechanism to adapt to complex environments. During the COVID-19 pandemic, three disinfection service robots were deployed to disinfect a large, open, and complex experimental site for 10 days. The results demonstrated that the proposed EnergyWise multi-robot control system successfully achieved a 54% reduction in computing energy consumption during long-term operations while maintaining a localization accuracy of 3 cm.

A comprehensive review of the latest path planning developments for multi-robot formation systems

There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques.

REALMS: Resilient exploration and lunar mapping system

Space resource utilisation is opening a new space era. The scientific proof of the presence of water ice on the south pole of the Moon, the recent advances in oxygen extraction from lunar regolith, and its use as a material to build shelters are positioning the Moon, again, at the centre of important space programs. These worldwide programs, led by ARTEMIS, expect robotics to be the disrupting technology enabling humankind’s next giant leap. However, Moon robots require a high level of autonomy to perform lunar exploration tasks more efficiently without being constantly controlled from Earth. Furthermore, having more than one robotic system will increase the resilience and robustness of the global system, improving its success rate, as well as providing additional redundancy. This paper introduces the Resilient Exploration and Lunar Mapping System, developed with a scalable architecture for semi-autonomous lunar mapping. It leverages Visual Simultaneous Localization and Mapping techniques on multiple rovers to map large lunar environments. Several resilience mechanisms are implemented, such as two-agent redundancy, delay invariant communications, a multi-master architecture different control modes. This study presents the experimental results of REALMS with two robots and its potential to be scaled to a larger number of robots, increasing the map coverage and system redundancy. The system’s performance is verified and validated in a lunar analogue facility, and a larger lunar environment during the European Space Agency (ESA)-European Space Resources Innovation Centre Space Resources Challenge. The results of the different experiments show the efficiency of REALMS and the benefits of using semi-autonomous systems.

On the role and opportunities in teamwork design for advanced multi-robot search systems

Intelligent robotic systems are becoming ever more present in our lives across a multitude of domains such as industry, transportation, agriculture, security, healthcare and even education. Such systems enable humans to focus on the interesting and sophisticated tasks while robots accomplish tasks that are either too tedious, routine or potentially dangerous for humans to do. Recent advances in perception technologies and accompanying hardware, mainly attributed to rapid advancements in the deep-learning ecosystem, enable the deployment of robotic systems equipped with onboard sensors as well as the computational power to perform autonomous reasoning and decision making online. While there has been significant progress in expanding the capabilities of single and multi-robot systems during the last decades across a multitude of domains and applications, there are still many promising areas for research that can advance the state of cooperative searching systems that employ multiple robots. In this article, several prospective avenues of research in teamwork cooperation with considerable potential for advancement of multi-robot search systems will be visited and discussed. In previous works we have shown that multi-agent search tasks can greatly benefit from intelligent cooperation between team members and can achieve performance close to the theoretical optimum. The techniques applied can be used in a variety of domains including planning against adversarial opponents, control of forest fires and coordinating search-and-rescue missions. The state-of-the-art on methods of multi-robot search across several selected domains of application is explained, highlighting the pros and cons of each method, providing an up-to-date view on the current state of the domains and their future challenges.

Xiroi II, an Evolved ASV Platform for Marine Multirobot Operations

In this paper, we present the design, development and a practical use of an Autonomous Surface Vehicle (ASV) as a modular and flexible platform for a large variety of marine tasks including the coordination strategies with other marine robots. This work tackles the integration of an open-source Robot-Operating-System (ROS)-based control architecture that provides the ASV with a wide variety of navigation behaviors. These new ASV capabilities can be used to acquire useful data from the environment to survey, map, and characterize marine habitats. In addition, the ASV is used as a radio frequency relay point between an Autonomous Underwater Vehicle (AUV) and the ground station as well as to enhance the Acoustic Communication Link (ACL) with the AUV. In order to improve the quality of the ACL, a new Marine Multirobot System (MMRS) coordination strategy has been developed that aims to keep both vehicles close to each other. The entire system has been successfully designed, implemented, and tested in real marine environment robotic tasks. The experimental tests show satisfactory results both in ROS-based navigation architecture and the MMRS coordination strategy resulting in a significant improvement of the quality of the ACL.

Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation

The multipoint dynamic aggregation (MPDA) problem of the multirobot system is of great significance for its real-world applications such as bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this article, we consider the dynamic version of the problem, where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this article, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyperheuristic (GPHH) method to evolve reactive coordination strategies (RCSs), which can guide the robots to make decisions in real time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks and a cluster filter that assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.

Hybrid type multi-robot path planning of a serial manipulator and SwarmItFIX robots in sheet metal milling process

This work investigates on the coordinated locomotion between a ceiling-mounted serial manipulator and two SwarmItFIX robots. The former holds the machining tool as an end effector, and the other two robots act as swarm robotic fixtures in a sheet metal milling process. A novel offline coordination planner which follows the hierarchical based hybrid type decentralized planning strategy has been proposed. Motion of the serial manipulator and SwarmItFIX robots’ coordinated locomotion are divided into three sub-problems, viz, trajectory planning of serial manipulator, task planning of SwarmItFIX robots, and homogenous prioritized multi-robot path planning of SwarmItFIX robots. Mathematical formulation of all the three sub-problems is developed and presented in this paper. A hexagonal segment that fits inside the boundaries of the workspace is considered as the machining trajectory. The tool velocity is assumed to be constant as it improves the quality of machining. The results obtained from the proposed planner is found to be efficient as the task planning module has computed the precise support locations and support duration for the SwarmItFIX robots. The multi-robot path planning module of the planner computes the optimal collision-free paths of SwarmItFIX robots for all goal positions. Finally, trajectories of SwarmItFIX robots are found to be completely in-line with the trajectory of tool center point (TCP) of the serial manipulator.

IoT Security and Computation Management on a Multi-Robot System for Rescue Operations Based on a Cloud Framework

There is a growing body of literature that recognizes the importance of Multi-Robot coordination and Modular Robotics. This work evaluates the secure coordination of an Unmanned Aerial Vehicle (UAV) via a drone simulation in Unity and an Unmanned Ground Vehicle (UGV) as a rover. Each robot is equipped with sensors to gather information to send to a cloud server where all computations are performed. Each vehicle is registered by blockchain ledger-based network security. In addition to these, relevant information and alerts are displayed on a website for the users. The usage of UAV-UGV cooperation allows for autonomous surveillance due to the high vantage field of view. Furthermore, the usage of cloud computation lowers the cost of microcontrollers by reducing their complexity. Lastly, blockchain technology mitigates the security issues related to adversarial or malicious robotic nodes connecting to the cluster and not agreeing to privacy rules and norms.

Bio-Inspired Vision and Gesture-Based Robot-Robot Interaction for Human-Cooperative Package Delivery

This research presents a novel bio-inspired framework for two robots interacting together for a cooperative package delivery task with a human-in the-loop. It contributes to eliminating the need for network-based robot-robot interaction in constrained environments. An individual robot is instructed to move in specific shapes with a particular orientation at a certain speed for the other robot to infer using object detection (custom YOLOv4) and depth perception. The shape is identified by calculating the area occupied by the detected polygonal route. A metric for the area's extent is calculated and empirically used to assign regions for specific shapes and gives an overall accuracy of 93.3% in simulations and 90% in a physical setup. Additionally, gestures are analyzed for their accuracy of intended direction, distance, and the target coordinates in the map. The system gives an average positional RMSE of 0.349 in simulation and 0.461 in a physical experiment. A video demonstration of the problem statement along with the simulations and experiments for real world applications has been given here and in Supplementary Material.

The Use of Swarms of Unmanned Aerial Vehicles in Mitigating Area Coverage Challenges of Forest-Fire-Extinguishing Activities: A Systematic Literature Review

The use of Unmanned Aerial Vehicles (UAVs), colloquially known as drones, has grown rapidly over the past two decades and continues to expand at a rapid pace. This has resulted in the production of many research papers addressing the use of UAVs in a variety of applications, such as forest firefighting. The main purpose of this paper is to provide a comprehensive overview of UAV-based forest-fire-extinguishing activity (FFEA) operations. To achieve this goal, a systematic literature review was conducted to answer a specific set of questions, which were carefully formulated to address the results of research conducted between 2008 and 2021. This study aims to (i) expand our understanding of the development of UAVs and their current contributions to the FFEA; (ii) identify particularly novel or unique applications and characteristics of UAV-based fire-extinguishing systems; (iii) provide guidance for exploring and revising further ideas in this field by identifying under-researched topics and other areas in which more contributions are needed; and (iv) explore the feasibility of using UAV swarms to enable autonomous firefighting in the forest without human intervention. Of the 1353 articles systematically searched across five databases (Google Scholar, ACM Digital Library, Science Direct, Scopus, and IEEE Explore), 51 highly relevant articles were found to meet the inclusion criteria; therefore, they were analyzed and discussed. The results identified several gaps in this field of study among them the complexity of coordination in multi-robotic systems, the lack of evaluation and implementation of fire extinguishing systems, the inability of handling multiple spot fires, and poor management of time and resources. Finally, based on the conducted review, this paper provides significant research directions that require further investigations by researchers in this field including, the deployment of UAV-based Swarm Robotics, further study on the characteristics of the fire extinguishing systems; design more effective area coverage; and the propose of a self-firefighting model that enables individuals to decide on the course of events efficiently and locally for better utilization and management of time and resources.

The Vision of Self-Management in Cognitive Organic Power Distribution Systems

Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.

Multi-robot task allocation problem with multiple nonlinear criteria using branch and bound and genetic algorithms

The paper proposes the formulation of a single-task robot (ST), single-robot task (SR), time-extended assignment (TA), multi-robot task allocation (MRTA) problem with multiple, nonlinear criteria using discrete variables that drastically reduce the computation burden. Obtaining an allocation is addressed by a Branch and Bound (B&B) algorithm in low scale problems and by a genetic algorithm (GA) specifically developed for the proposed formulation in larger scale problems. The GA crossover and mutation strategies design ensure that the descendant allocations of each generation will maintain a certain level of feasibility, reducing greatly the range of possible descendants, and accelerating their convergence to a sub-optimal allocation. The proposed MRTA algorithms are simulated and analyzed in the context of a thermosolar power plant, for which the spatially distributed Direct Normal Irradiance (DNI) is estimated using a heterogeneous fleet composed of both aerial and ground unmanned vehicles. Three optimization criteria are simultaneously considered: distance traveled, time required to complete the task and energetic feasibility. Even though this paper uses a thermosolar power plant as a case study, the proposed algorithms can be applied to any MRTA problem that uses a multi-criteria and nonlinear cost function in an equivalent way. The performance and response of the proposed algorithms are compared for four different scenarios. The results show that the B&B algorithm can find the global optimal solution in a reasonable time for a case with four robots and six tasks. For larger problems, the genetic algorithm approaches the global optimal solution in much less computation time. Moreover, the trade-off between computation time and accuracy can be easily carried out by tuning the parameters of the genetic algorithm according to the available computational power.

Adaptive Segmentation of Streaming Sensor Data on Edge Devices

Sensor data streams often represent signals/trajectories which are twice differentiable (e.g., to give a continuous velocity and acceleration), and this property must be reflected in their segmentation. An adaptive streaming algorithm for this problem is presented. It is based on the greedy look-ahead strategy and is built on the concept of a cubic splinelet. A characteristic feature of the proposed algorithm is the real-time simultaneous segmentation, smoothing, and compression of data streams. The segmentation quality is measured in terms of the signal approximation accuracy and the corresponding compression ratio. The numerical results show the relatively high compression ratios (from 135 to 208, i.e., compressed stream sizes up to 208 times smaller) combined with the approximation errors comparable to those obtained from the state-of-the-art global reference algorithm. The proposed algorithm can be applied to various domains, including online compression and/or smoothing of data streams coming from sensors, real-time IoT analytics, and embedded time-series databases.

Design of a Digital 3D Model of Transport-Technological Environment of Open-Pit Mines Based on the Common Use of Telemetric and Geospatial Information

This article is devoted to the issues of processing and analysis of heterogeneous information related to the functioning of mining transport equipment, which becomes available for analysis within the framework of modern technological operations control systems in open-pit mines. These issues are very relevant to robotized technological operations. The paper gives a brief overview of the modern landscape of the autonomous haulage systems management problems, the features of the platform approach to solving the problem of managing unmanned transport and technological processes in open pits are considered. The concept of an agent-based approach to the modeling of an open-pit mining is described in detail on the basis of the interaction of three systems: technical, infrastructural–technological, and geostructural. Some features of the developed platform architecture integration of heterogeneous information are discussed. The principles of information integration are considered in detail when constructing a dynamic 3D model (digital twin) of infrastructure and technological system elements using large arrays of telemetric data. The results of building digital models of open-pit technological roads are presented. The resulting models are comparatively analyzed in the process of optimizing of the interaction of technical autonomous mobile agents and elements of technological infrastructure.

Intelligent Multi-Robot System for Collaborative Object Transportation Tasks in Rough Terrains

Human missions on other planets require constructing outposts and infrastructures, and one may need to consider relocating such large objects according to changes in mission spots. A multi-robot system would be a good option for such a transportation task because it can carry massive objects and provide better system reliability and redundancy when compared to a single robot system. This paper proposes an intelligent and decentralized approach for the multi-robot system using a genetic fuzzy system to perform an object transportation mission that not only minimizes the total travel distance of the multi-robot system but also guarantees the stability of the whole system in a rough terrain environment. The proposed fuzzy inference system determines the multi-robot system’s input for transporting an object to a target position and is tuned in the training process by a genetic algorithm with an artificially generated structured environment employing multiple scenarios. It validates the optimality of the proposed approach by comparing the training results with the results obtained by solving the formulated optimal control problem subject to path inequality constraints. It highlights the performance of the proposed approach by applying the trained fuzzy inference systems to operate the multi-robot system in unstructured environments.

Survey of Emotions in Human–Robot Interactions: Perspectives from Robotic Psychology on 20 Years of Research

Knowledge production within the interdisciplinary field of human–robot interaction (HRI) with social robots has accelerated, despite the continued fragmentation of the research domain. Together, these features make it hard to remain at the forefront of research or assess the collective evidence pertaining to specific areas, such as the role of emotions in HRI. This systematic review of state-of-the-art research into humans’ recognition and responses to artificial emotions of social robots during HRI encompasses the years 2000–2020. In accordance with a stimulus–organism–response framework, the review advances robotic psychology by revealing current knowledge about (1) the generation of artificial robotic emotions (stimulus), (2) human recognition of robotic artificial emotions (organism), and (3) human responses to robotic emotions (response), as well as (4) other contingencies that affect emotions as moderators.

Design, modeling, and constraint-compliant control of an autonomous morphing surface for omnidirectional object conveyance

In this paper, we conceptualize, analyze, and assemble a prototype adaptive surface system capable of morphing its geometric configuration using an array of linear actuators to impose omnidirectional movement of objects that lie on the surface. The principal focus and contribution of this paper is the derivation of feedback control protocols–for regulating the actuators’ length in order to accomplish the object conveyance task–that scale with the number of actuators and the nonlinear kinematic constraints of the morphing surface. Simulations and experimental results demonstrate the advantages of distributed manipulation over static-shaped feeders.

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

Broadcast Event-Triggered Control Scheme for Multi-Agent Rendezvous Problem in a Mixed Communication Environment

This paper addresses the communication issue encountered by a hybrid controller when finding consensus in terms of the rendezvous target point in a broadcast and communication environment. This issue may result in a high level of computation and the utilization of agent resources when a continuous communication is required by agents to meet convergence requirements. Thus, an event-triggered system was integrated into the design of a broadcast and distributed consensus linear controller using the simultaneous perturbation stochastic algorithm (SPSA). The agent’s movement towards the rendezvous point is based on the broadcast value, whereas the next agent’s state position depends on the distributed local controller output. The communication error obtained during communication between the agent and neighbors is only added to the gradient approximation error of the SPSA if the event-triggered function is violated. As a result, in our model, the number of channel utilizations was lower and the agents’ performances were preserved. The efficiencies and effectiveness of the proposed controller have been compared with the traditional sampling broadcast time-triggered (BTT) approach. The time and iterations required by the broadcast event-triggered (BET) system were less than 40.42% and 21% on average as compared to BTT. The trajectory was not the same—the BET showed scattered movements at the initial stage, whereas BTT showed a linear movement. In terms of the number of channels, 28.91% of channels were preserved during the few hundred iterations. Consequently, a variety of hybrid controllers with event-triggered mechanisms can be proposed for other multi-agent motion coordination tasks.

Dynamic Movement Primitives: Volumetric Obstacle Avoidance Using Dynamic Potential Functions

Obstacle avoidance for Dynamic Movement Primitives (DMPs) is still a challenging problem. In our previous work, we proposed a framework for obstacle avoidance based on superquadric potential functions to represent volumes. In this work, we extend our previous work to include the velocity of the system in the definition of the potential. Our formulations guarantee smoother behavior with respect to state-of-the-art point-like methods. Moreover, our new formulation allows obtaining a smoother behavior in proximity of the obstacle than when using a static (i.e. velocity independent) potential. We validate our framework for obstacle avoidance in a simulated multi-robot scenario and with different real robots: a pick-and-place task for an industrial manipulator and a surgical robot to show scalability; and navigation with a mobile robot in a dynamic environment.

Simulation and Analysis of Self-Replicating Robot Decision-Making Systems

Self-replicating robot systems (SRRSs) are a new prospective paradigm for robotic exploration. They can potentially facilitate lower mission costs and enhance mission capabilities by allowing some materials, which are needed for robotic system construction, to be collected in situ and used for robot fabrication. The use of a self-replicating robot system can potentially lower risk aversion, due to the ability to potentially replenish lost or damaged robots, and may increase the likelihood of mission success. This paper proposes and compares system configurations of an SRRS. A simulation system was designed and is used to model how an SRRS performs based on its system configuration, attributes, and operating environment. Experiments were conducted using this simulation and the results are presented.

Fish-Inspired Task Allocation Algorithm for Multiple Unmanned Aerial Vehicles in Search and Rescue Missions

The challenge concerning the optimal allocation of tasks across multiple unmanned aerial vehicles (multi-UAVs) has significantly spurred research interest due to its contribution to the success of various fleet missions. This challenge becomes more complex in time-constrained missions, particularly if they are conducted in hostile environments, such as search and rescue (SAR) missions. In this study, a novel fish-inspired algorithm for multi-UAV missions (FIAM) for task allocation is proposed, which was inspired by the adaptive schooling and foraging behaviors of fish. FIAM shows that UAVs in an SAR mission can be similarly programmed to aggregate in groups to swiftly survey disaster areas and rescue-discovered survivors. FIAM’s performance was compared with three long-standing multi-UAV task allocation (MUTA) paradigms, namely, opportunistic task allocation scheme (OTA), auction-based scheme, and ant-colony optimization (ACO). Furthermore, the proposed algorithm was also compared with the recently proposed locust-inspired algorithm for MUTA problem (LIAM). The experimental results demonstrated FIAM’s abilities to maintain a steady running time and a decreasing mean rescue time with a substantially increasing percentage of rescued survivors. For instance, FIAM successfully rescued 100% of the survivors with merely 16 UAVs, for scenarios of no more than eight survivors, whereas LIAM, Auction, ACO and OTA rescued a maximum of 75%, 50%, 35% and 35%, respectively, for the same scenarios. This superiority of FIAM performance was maintained under a different fleet size and number of survivors, demonstrating the approach’s flexibility and scalability.

A Framework for Human-Robot-Human Physical Interaction Based on N-Player Game Theory

In order to analyze the complex interactive behaviors between the robot and two humans, this paper presents an adaptive optimal control framework for human-robot-human physical interaction. N-player linear quadratic differential game theory is used to describe the system under study. N-player differential game theory can not be used directly in actual scenerie, since the robot cannot know humans’ control objectives in advance. In order to let the robot know humans’ control objectives, the paper presents an online estimation method to identify unknown humans’ control objectives based on the recursive least squares algorithm. The Nash equilibrium solution of human-robot-human interaction is obtained by solving the coupled Riccati equation. Adaptive optimal control can be achieved during the human-robot-human physical interaction. The effectiveness of the proposed method is demonstrated by rigorous theoretical analysis and simulations. The simulation results show that the proposed controller can achieve adaptive optimal control during the interaction between the robot and two humans. Compared with the LQR controller, the proposed controller has more superior performance.

Task Planning of Space-Robot Clusters Based on Modified Differential Evolution Algorithm

This study studies the problem of on-orbit maintenance task planning for space-robot clusters. Aiming at the problem of low maintenance efficiency of space-robot cluster task-planning, this study proposes a cluster-task-planning method based on energy and path optimization. First, by introducing the penalty-function method, the task planning problem of the space-robot cluster under limited energy is analyzed, and the optimal-path model for task planning with comprehensive optimization of revenue and energy consumption are constructed; then, the maintenance task points are clustered to reduce the scale of the problem, thus reducing the difficulty of solving the problem; finally, a modified differential evolution algorithm is proposed to solve the problem of space-robot cluster task-planning, improve the performance of space-robot cluster task-assignment and path planning. Simulation results show that the proposed optimal-path model of space-robot cluster and the modified differential evolution algorithm can effectively solve the task-planning problem of spatial robot clusters.

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

This paper studies the cooperation method of multi-cooperative Unmanned Surface Vehicles (USVs) for chemical pollution source monitoring in a dynamic water environment. Multiple USVs formed a mobile sensor network in a symmetrical or asymmetrical formation. Based on ‘Infotaxis’ algorithms for multi-USV, an improved shared probability is proposed for solving the problems of low success rate and low efficiency resulting from the cognitive differences of multi-USV in cooperative exploration. By introducing the confidence factor, the cognitive differences between USVs are coordinated. The success rate and the efficiency of exploration are improved. To further optimize the exploration strategy, the particle swarm optimization (PSO) algorithm is introduced into the ‘Infotaxis’ algorithm to plan the USVs’ exploration path. This method is called the ‘PSO-Infotaxis’ algorithm. The effectiveness of the proposed method is verified by simulation and laboratory experiments. A comparison of the test results shows that the ‘PSO-Infotaxis’ algorithm is superior with respect to exploring efficiency. It can reduce the uncertainty of the estimation for source location faster and has lower exploration time, which is most important for the exploration of a large range of water areas.