Swarm robotics reviewed

Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination

Broadcast control (BC) is a bio-inspired coordination technique for a swarm of agents in which a single coordinator broadcasts an identical scalar signal to all performing agents without discrimination, and the agents make appropriate moves towards the agents' collective optimal state without communicating with one another. The BC technique aims to accomplish a globally assigned task for which BC utilizes a stochastic optimization algorithm to coordinate a group of agents. However, the challenge intensifies as the system becomes larger: it requires a larger number of agents, which protracts the converging time for a single coordinator-based BC model. This paper proposes a revamped version of BC model, which assimilates distributed multiple coordinators to control a larger multi-agent system efficiently in a pragmatic manner. Precisely, in this hierarchical BC scheme, the distributed multiple sub-coordinators broadcast the identical feedback signal to the agents, which they receive from the global coordinator to accomplish the coverage control task of the ordinary agents. The dual role of sub-coordinators is manipulated by introducing weighted averaging of the gradient estimation under the stochastic optimization mechanism. The potency of the proposed model is analyzed with numerical simulation for a coverage control task, and various performance aspects are compared with the typical BC schemes to demonstrate its practicability and performance improvement. Particularly, the proposed scheme shows the same convergence with about 30% less traveling costs, and the near convergence is reached by only about one-third of iteration steps compared to the typical BC.

Collective intelligence: A unifying concept for integrating biology across scales and substrates

A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

Effective UAV patrolling for swarm of intruders with heterogeneous behavior

The phenomenal growth in the utilization of commercial unmanned aerial vehicles (UAVs) or drones leads to an urgent need for new approaches to ensure safety in the sky. Effective aerial surveillance requires patrolling swarms to react according to the various behaviors demonstrated by intruding swarms, but existing approaches are not practical when dealing with a large number of drones. Specifically, predicting the behaviors or planned paths of the intruding swarms is highly challenging as intruders may perform evasive strategies to avoid detection. Therefore, this work utilizes heuristic search strategies and investigates how various intruder behaviors affect the search performance. To investigate the search performance, a swarm versus swarm simulator is developed. Using the simulator, first, a comparative study is performed to evaluate how intruders’ behaviors can affect the performance of the patrolling swarm. Subsequently, three approaches, including single-objective optimization, multi-objective optimization, and Lévy flight, are compared in terms of their detection performance in a bounded space. The results suggest that multi-objective optimization outperforms both single-objective optimization and Lévy flight-based approaches. Furthermore, our results show that intruders have a lower chance of being tracked when moving in a dense crowd, and this finding reaffirms the schooling behaviors of fish. In a specific simulation scenario, the total percentage of detection is above 90%. However, the detection percentage is highly related to other factors such as search space, number of patrolling UAVs, and the intruders’ behaviors.

On the Throughput of the Common Target Area for Robotic Swarm Strategies

A robotic swarm may encounter traffic congestion when many robots simultaneously attempt to reach the same area. This work proposes two measures for evaluating the access efficiency of a common target area as the number of robots in the swarm rises: the maximum target area throughput and its maximum asymptotic throughput. Both are always finite as the number of robots grows, in contrast to the arrival time at the target per number of robots that tends to infinity. Using them, one can analytically compare the effectiveness of different algorithms. In particular, three different theoretical strategies proposed and formally evaluated for reaching a circular target area: (i) forming parallel queues towards the target area, (ii) forming a hexagonal packing through a corridor going to the target, and (iii) making multiple curved trajectories towards the boundary of the target area. The maximum throughput and the maximum asymptotic throughput (or bounds for it) for these strategies are calculated, and these results are corroborated by simulations. The key contribution is not the proposal of new algorithms to alleviate congestion but a fundamental theoretical study of the congestion problem in swarm robotics when the target area is shared.

Magnetic Trails: A Novel Artificial Pheromone for Swarm Robotics in Outdoor Environments

Swarm robotics finds inspiration in nature to model behaviors, such as the use of pheromone principles. Pheromones provide an indirect and decentralized communication scheme that have shown positive experimental results. Real implementations of pheromones have suffered from slow sensors and have been limited to controlled environments. This paper presents a novel technology to implement real pheromones for swarm robotics in outdoor environments by using magnetized ferrofluids. A ferrofluid solution, with its deposition and magnetization system, is detailed. The proposed substance does not possess harmful materials for the environment and can be safely handled by humans. Validation demonstrates that the substance represents successfully pheromone characteristics of locality, diffusion and evaporation on several surfaces in outdoor conditions. Additionally, the experiments show an improvement over the chemical representation of pheromones by using magnetic substances and existing magnetometer sensor technologies, which provide better response rates and recovery periods than MOX chemical sensors. The present work represents a step toward swarm robotics experimentation in uncontrolled outdoor environments. In addition, the presented pheromone technology may be use by the broad area of swarm robotics for robot exploration and navigation.

Swarm Robotics: Simulators, Platforms and Applications Review

This paper presents an updated and broad review of swarm robotics research papers regarding software, hardware, simulators and applications. The evolution from its concept to its real-life implementation is presented. Swarm robotics analysis is focused on four aspects: conceptualization, simulators, real-life robotics for swarm use, and applications. For simulators and robots, a detailed comparison between existing resources is made. A summary of the most used swarm robotics applications and behaviors is included.

Multi-scale Chimerism: An experimental window on the algorithms of anatomical control

Despite the immense progress in genetics and cell biology, major knowledge gaps remain with respect to prediction and control of the global morphologies that will result from the cooperation of cells with known genomes. The understanding of cooperativity, competition, and synergy across diverse biological scales has been obscured by a focus on standard model systems that exhibit invariant species-specific anatomies. Morphogenesis of chimeric biological material is an especially instructive window on the control of biological growth and form because it emphasizes the need for prediction without reliance on familiar, standard outcomes. Here, we review an important and fascinating body of data from experiments utilizing DNA transfer, cell transplantation, organ grafting, and parabiosis. We suggest that these are all instances (at different levels of organization) of one general phenomenon: chimerism. Multi-scale chimeras are a powerful conceptual and experimental tool with which to probe the mapping between properties of components and large-scale anatomy: the laws of morphogenesis. The existing data and future advances in this field will impact not only the understanding of cooperation and the evolution of body forms, but also the design of strategies for system-level outcomes in regenerative medicine and swarm robotics.

From collections of independent, mindless robots to flexible, mobile, and directional superstructures

A swarm of simple active particles confined in a flexible scaffold is a promising system to make mobile and deformable superstructures. These soft structures can perform tasks that are difficult to carry out for monolithic robots because they can infiltrate narrow spaces, smaller than their size, and move around obstacles. To achieve such tasks, the origin of the forces the superstructures develop, how they can be guided, and the effects of external environment, especially geometry and the presence of obstacles, need to be understood. Here, we report measurements of the forces developed by such superstructures, enclosing a number of mindless active rod-like robots, as well as the forces exerted by these structures to achieve a simple function, crossing a constriction. We relate these forces to the self-organization of the individual entities. Furthermore, and based on a physical understanding of what controls the mobility of these superstructures and the role of geometry in such a process, we devise a simple strategy where the environment can be designed to bias the mobility of the superstructure, giving rise to directional motion. Simple tasks-such as pulling a load, moving through an obstacle course, or cleaning up an arena-are demonstrated. Rudimentary control of the superstructures using light is also proposed. The results are of relevance to the making of robust flexible superstructures with nontrivial space exploration properties out of a swarm of simpler and cheaper robots.

Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics

In recent years, collaborative robots have become one of the main drivers of Industry 4.0. Compared to industrial robots, automated guided vehicles (AGVs) are more productive, flexible, versatile, and safer. They are used in the smart factory to transport goods. Today, many producers and developers of industrial robots have entered the AGV sector. However, they face several challenges in designing AGV systems, such as the complexity and discontinuity of the design process, as well as the difficulty of defining a decentralized system decision. In this paper, we propose a new integrated design methodology based on swarm robotics to address the challenges of functional, physical, and software integration. This methodology includes two phases: a top-down phase from requirements specification to functional and structural modeling using the systems modeling language (SysML); with a bottom-up phase for model integration and implementation in the robot operating system (ROS). A case study of an automated guided vehicle (AGV) system was chosen to validate our design methodology and illustrate its contributions to the efficient design of AGVs. The novelty of this proposed methodology is the combination of SysML and ROS to address traceability management between the different design levels of AGV systems, in order to achieve functional, physical and software integration.

Swarm SLAM: Challenges and Perspectives

A robot swarm is a decentralized system characterized by locality of sensing and communication, self-organization, and redundancy. These characteristics allow robot swarms to achieve scalability, flexibility and fault tolerance, properties that are especially valuable in the context of simultaneous localization and mapping (SLAM), specifically in unknown environments that evolve over time. So far, research in SLAM has mainly focused on single- and centralized multi-robot systems-i.e., non-swarm systems. While these systems can produce accurate maps, they are typically not scalable, cannot easily adapt to unexpected changes in the environment, and are prone to failure in hostile environments. Swarm SLAM is a promising approach to SLAM as it could leverage the decentralized nature of a robot swarm and achieve scalable, flexible and fault-tolerant exploration and mapping. However, at the moment of writing, swarm SLAM is a rather novel idea and the field lacks definitions, frameworks, and results. In this work, we present the concept of swarm SLAM and its constraints, both from a technical and an economical point of view. In particular, we highlight the main challenges of swarm SLAM for gathering, sharing, and retrieving information. We also discuss the strengths and weaknesses of this approach against traditional multi-robot SLAM. We believe that swarm SLAM will be particularly useful to produce abstract maps such as topological or simple semantic maps and to operate under time or cost constraints.

Optocapillarity-driven assembly and reconfiguration of liquid crystal polymer actuators

Realizing programmable assembly and reconfiguration of small objects holds promise for technologically-significant applications in such fields as micromechanical systems, biomedical devices, and metamaterials. Although capillary forces have been successfully explored to assemble objects with specific shapes into ordered structures on the liquid surface, reconfiguring these assembled structures on demand remains a challenge. Here we report a strategy, bioinspired by Anurida maritima, to actively reconfigure assembled structures with well-defined selectivity, directionality, robustness, and restorability. This approach, taking advantage of optocapillarity induced by photodeformation of floating liquid crystal polymer actuators, not only achieves programmable and reconfigurable two-dimensional assembly, but also uniquely enables the formation of three-dimensional structures with tunable architectures and topologies across multiple fluid interfaces. This work demonstrates a versatile approach to tailor capillary interaction by optics, as well as a straightforward bottom-up fabrication platform for a wide range of applications.

A high-performance millirobot for swarm-behaviour studies: Swarm-topology estimation

In this article, we present a novel high-performance millirobot ( milli- robot- Toronto), designed to allow for the testing of complex swarm-behaviours, including human–swarm interaction. milli- robot- Toronto, built only with off-the-shelf components, has locomotion, processing and sensing capabilities that significantly improve upon existing designs, while maintaining one of the smallest footprints among current millirobots. As complementary software to this hardware development, herein, we also present a new global swarm-topology estimation algorithm. The method is novel in that it uniquely fuses incomplete location data collected by the individual robots in a distributed manner to optimally estimate the topology of the overall swarm using a centralized computer. It is a generalized technique usable by any swarm comprising robots capable of collecting location estimates of neighbouring robots. Numerous experiments, evaluating the performance of milli- robot- Toronto and the proposed optimal swarm-topology estimation algorithm, are also included.

Who Will Be the Members of Society 5.0? Towards an Anthropology of Technologically Posthumanized Future Societies

The Government of Japan’s “Society 5.0” initiative aims to create a cyber-physical society in which (among other things) citizens’ daily lives will be enhanced through increasingly close collaboration with artificially intelligent systems. However, an apparent paradox lies at the heart of efforts to create a more “human-centered” society in which human beings will live alongside a proliferating array of increasingly autonomous social robots and embodied AI. This study seeks to investigate the presumed human-centeredness of Society 5.0 by comparing its makeup with that of earlier societies. By distinguishing “technological” and “non-technological” processes of posthumanization and applying a phenomenological anthropological model, this study demonstrates: (1) how the diverse types of human and non-human members expected to participate in Society 5.0 differ qualitatively from one another; (2) how the dynamics that will shape the membership of Society 5.0 can be conceptualized; and (3) how the anticipated membership of Society 5.0 differs from that of Societies 1.0 through 4.0. This study describes six categories of prospective human and non-human members of Society 5.0 and shows that all six have analogues in earlier societies, which suggests that social scientific analysis of past societies may shed unexpected light on the nature of Society 5.0.

A Novel Framework for Multi-Agent Systems Using a Decentralized Strategy

This work addresses a new framework that proposes a decentralized strategy for collective and collaborative behaviours of multi-agent systems. This framework includes a new clustering behaviour that causes agents in the swarm to agree on attending a group and allocating a leader for each group, in a decentralized and local manner. The leader of each group employs a vision-based goal detection algorithm to find and acquire the goal in a cluttered environment. As soon as the leader starts moving, each member is enabled to move in the same direction by staying coordinated with the leader and maintaining the desired formation pattern. In addition, an exploration algorithm is designed and integrated into the framework so as to allow each group to be able to explore goals in a collaborative and efficient manner. A series of comprehensive experiments are conducted in order to verify the overall performance of the proposed framework.

A survey on evolutionary-aided design in robotics

The evolutionary-aided design process is a method to find solutions to design and optimisation problems. Evolutionary algorithms (EAs) are applied to search for optimal solutions from a solution space that evolves over several generations. EAs have found applications in many areas of robotics. This paper covers the efforts to determine body morphology of robots through evolution and body morphology with the controller of robots or similar creatures through co-evolution. The works are reviewed from the perspective of how different algorithms are applied and includes a brief explanation of how they are implemented.

Design and implementation of a millirobot for swarm studies –mROBerTO

The use of millirobots, particularly in swarm studies, would enable researchers to verify their proposed autonomous cooperative behavior algorithms under realistic conditions with a large number of agents. While multiple designs for such robots have been proposed, they, typically, require custom-made components, which make replication and manufacturing difficult, and, mostly, employ non-modular integral designs. Furthermore, these robots' proposed small sizes tend to limit sensory perception capabilities and operational time. Some have resolved few of the above issues through the use of extensions that, unfortunately, add to their size.

In contribution to the pertinent field, thus, a novel millirobot with an open-source design, addressing the above concerns, is presented in this paper. Our proposed millirobot has a modular design and uses easy to source, off-the-shelf components. Themilli-robot-Toronto (mROBerTO) also includes a variety of sensors and has a 16 × 16 mm2footprint.mROBerTO's wireless communication capabilities include ANT™, Bluetooth Smart, or both simultaneously. Data-processing is handled by an ARM processor with 256 KB of flash memory. Additionally, the sensing modules allow for extending or changing the robot's perception capabilities without adding to the robot's size. For example, the swarm-sensing module, designed to facilitate swarm studies, allows for measuring proximity and bearing to neighboring robots and performing local communications.

Extensive experiments, some of which are presented herein, have illustrated the capability ofmROBerTOunits for use in implementing a variety of commonly proposed swarm algorithms.

Grouping-based adaptive spatial formation of swarm robots in a dynamic environment

Spatial formations of swarm robots are increasingly applied in many domains in which the environments are dynamic and unpredictable. The autonomy of the individual robots and decentralization of the entire system increase the complexity of the response to environmental changes, which could prolong the formation convergence and significantly increase the communication cost. To address these issues, we propose an adaptive mechanism with three basic behaviours for each individual robot and design a grouping-based spatial formation algorithm for swarm robots to respond to changes and accomplish shape formation. Specifically, the robots are automatically partitioned into several groups based on their spatial neighbours. In this manner, the interactions and self-organization of robots are primarily performed at the intra-group rather than inter-group level, leading to decreased communication costs. Furthermore, this grouping mechanism naturally supports parallel formation and therefore improves the convergence speed. Our simulation and experimental results demonstrate that the proposed method significantly improves the convergence speed and decreases the communication cost, thus validating the effectiveness of the proposed adaptive mechanism.

A survey of formation control and motion planning of multiple unmanned vehicles

The increasing deployment of multiple unmanned vehicles systems has generated large research interest in recent decades. This paper therefore provides a detailed survey to review a range of techniques related to the operation of multi-vehicle systems in different environmental domains, including land based, aerospace and marine with the specific focuses placed on formation control and cooperative motion planning. Differing from other related papers, this paper pays a special attention to the collision avoidance problem and specifically discusses and reviews those methods that adopt flexible formation shape to achieve collision avoidance for multi-vehicle systems. In the conclusions, some open research areas with suggested technologies have been proposed to facilitate the future research development.

A review on automated sorting of source-separated municipal solid waste for recycling

A crucial prerequisite for recycling forming an integral part of municipal solid waste (MSW) management is sorting of useful materials from source-separated MSW. Researchers have been exploring automated sorting techniques to improve the overall efficiency of recycling process. This paper reviews recent advances in physical processes, sensors, and actuators used as well as control and autonomy related issues in the area of automated sorting and recycling of source-separated MSW. We believe that this paper will provide a comprehensive overview of the state of the art and will help future system designers in the area. In this paper, we also present research challenges in the field of automated waste sorting and recycling.