Team Of Robots Research Articles

Heterogeneous multi-agent task allocation based on graph neural network ant colony optimization algorithms

Heterogeneous multi-agent task allocation is a key optimization problem widely used in fields such as drone swarms and multi-robot coordination. This paper proposes a new paradigm that innovatively combines graph neural networks and ant colony optimization algorithms to solve the assignment problem of heterogeneous multi-agents. The paper introduces an innovative Graph-based Heterogeneous Neural Network Ant Colony Optimization (GHNN-ACO) algorithm for heterogeneous multi-agent scenarios. The multi-agent system is composed of unmanned aerial vehicles, unmanned ships, and unmanned vehicles that work together to effectively respond to emergencies. This method uses graph neural networks to learn the relationship between tasks and agents, forming a graph representation, which is then integrated into ant colony optimization algorithms to guide the search process of ants. Firstly, the algorithm in this paper constructs heterogeneous graph data containing different types of agents and their relationships and uses the algorithm to classify and predict linkages for agent nodes. Secondly, the GHNN-ACO algorithm performs effectively in heterogeneous multi-agent scenarios, providing an effective solution for node classification and link prediction tasks in intelligent agent systems. Thirdly, the algorithm achieves an accuracy rate of 95.31% in assigning multiple tasks to multiple agents. It holds potential application prospects in emergency response and provides a new idea for multi-agent system cooperation.

Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy

We present a coordinated autonomy pipeline for multi-sensor exploration of confined environments. We simultaneously address four broad challenges that are typically overlooked in prior work: (a) make effective use of both range and vision sensing modalities, (b) perform this exploration across a wide range of environments, (c) be resilient to adverse events, and (d) execute this onboard teams of physical robots. Our solution centers around a behavior tree architecture, which adaptively switches between various behaviors involving coordinated exploration and responding to adverse events. Our exploration strategy exploits the benefits of both visual and range sensors with a generalized frontier-based exploration algorithm and an OpenVDB-based map processing pipeline. Our local planner utilizes a dynamically feasible trajectory library and a GPU-based Euclidean distance transform map to allow fast and safe navigation through both tight doorways and expansive spaces. The autonomy pipeline is evaluated with an extensive set of field experiments, with teams of up to three robots that fly up to 3 m/s and distances exceeding 1 km in confined spaces. We provide a summary of various field experiments and detail resilient behaviors that arose: maneuvering narrow doorways, adapting to unexpected environment changes, and emergency landing. Experiments are also detailed from the DARPA Subterranean Challenge, where our proposed autonomy pipeline contributed to us winning the “Most Sectors Explored” award. We provide an extended discussion of lessons learned, release software as open source, and present a video that illustrates our extensive field trials.

The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal

Consider deploying a team of robots in order to visit sites in a risky environment (i.e., where a robot might be lost during a traversal), subject to team-based operational constraints such as limits on team composition, traffic throughputs, and launch constraints. We formalize this problem using a graph to represent the environment, enforcing probabilistic survival constraints for each robot, and using a matroid (which generalizes linear independence to sets) to capture the team-based operational constraints. The resulting “Matroid Team Surviving Orienteers” (MTSO) problem has broad applications for robotics such as informative path planning, resource delivery, and search and rescue. We demonstrate that the objective for the MTSO problem has submodular structure, which leads us to develop two polynomial time algorithms which are guaranteed to find a solution with value within a constant factor of the optimum. The second of our algorithms is an extension of the accelerated continuous greedy algorithm, and can be applied to much broader classes of constraints while maintaining bounds on suboptimality. In addition to in-depth analysis, we demonstrate the efficiency of our approaches by applying them to a scenario where a team of robots must gather information while avoiding dangers in the Coral Triangle and characterize scaling and parameter selection using a synthetic dataset.

Shared Task Representation for Human–Robot Collaborative Navigation: The Collaborative Search Case

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.

Assessing the impact of Byzantine attacks on coupled phase oscillators

For many coupled dynamical systems, the interaction is the outcome of the measurement that each unit has of the others as e.g. in modern inverter-based power grids, autonomous vehicular platoons or swarms of drones, or it is the result of physical flows. Synchronization among all the components of these systems is of primal importance to avoid failures. The overall operational state of these systems therefore crucially depends on the correct and reliable functioning of the individual elements as well as the information they transmit through the network. Here, we investigate the effect of Byzantine attacks where one unit does not behave as expected, but is controlled by an external attacker. For such attacks, we assess the impact on the global collective behavior of nonlinearly coupled phase oscillators. We relate the synchronization error induced by the input signal to the properties of the attacked node. This allows to anticipate the potential of an attacker and identify which network components to secure.

Research on Resource Allocation of Autonomous Swarm Robots Based on Game Theory

To address the issue of resource allocation optimization in autonomous swarm robots during emergency situations, this paper abstracts the problem as a two-stage extended game. In this game, participants are categorized as either resource-providing robots or resource-consuming robots. The strategies of the resource-providing robots involve resource production and pricing, whereas the strategies of the resource-consuming robots consist of determining the quantity to be purchased based on resource pricing. In the first stage of the game, the resource-providing robots use the Cournot game to determine the resource production according to market supply and demand conditions; in the second stage of the game, the resource-providing robots and the resource-consuming robots play the price game and establish the utility function of the swarm robots to seek the optimal pricing and the optimal purchasing strategy of the swarm robots. After the mathematical derivation, this paper demonstrates the existence of a single Nash equilibrium in the constructed game. Additionally, the inverse distributed iterative search algorithm solves the game’s optimal strategy. Finally, simulation verifies the game model’s validity. This study concludes that the designed game mechanism enables both sides to reach equilibrium and achieve optimal resource allocation.

An evolutionary robotics approach to a multi-legged robotic swarm in a rough terrain environment

An evolutionary robotics approach to a multi-legged robotic swarm in a rough terrain environment

A fast coordination approach for large-scale drone swarm

With the advances in artificial intelligence, robotics, and data fusion, large numbers of drones operating in a coordinated manner will become commonplace for a wide range of commercial and military uses. At present, the application methods of drone swarms are mainly divided into fully autonomous methods and controlled methods with human participation. Because of the limited level of artificial intelligence, controlled drone swarms will be the main way for the application of large-scale drone swarms for a long time. However, there is less research on achieving global coordination in a limited time for a controlled large-scale drone swarm. Therefore, a new large-scale drone swarm framework is proposed firstly in this paper, which achieves global coordination through local interaction and reduces the impact of limited channel resources. Secondly, this paper proposes a local interaction-based fast coordination method and introduces a prediction mechanism, to ensure that large-scale drone swarms can quickly achieve coordination even in the presence of node loss. Moreover, the numerical integration method is used to update the consensus state, so that the drones can increase the iteration period, reduce the number of packets, and further reduce the channel burden. Finally, considering that large-scale drones swarm are usually composed of drone swarms launched at different locations and times, a consensus algorithm considering the merging behavior of drone swarms is also proposed. The simulation results show that the large-scale drone swarm using the proposed architecture can achieve the leader–follower consensus in a very short time and even in a confrontational environment with poor communication conditions. Besides, after the merger of multiple drone swarms, the consensus problem can still be solved in very few iteration cycles.

Blockchain Technology for Secure Communication and Formation Control in Smart Drone Swarms

Today, intelligent drone technology is rapidly expanding, particularly in the defense industry. A swarm of drones can communicate, share data, and make the best decisions on their own. Drone swarms can swiftly and effectively carry out missions like surveillance, reconnaissance, and rescue operations, without exposing military troops to hostile conditions. However, there are still significant problems that need to be resolved. One of them is to protect communications on these systems from threat actors. In this paper, we use blockchain technology as a defense mechanism against such issues. Drones can communicate data safely, without the need for a centralized authority (ground station), when using a blockchain to facilitate communication between them in a leader–follower hierarchy structure. Solidity has been used to create a compact, lightweight, and effective smart contract that automates the process of choosing a position in a certain swarm formation structure. Additionally, a mechanism for electing a new leader is proposed. The effectiveness of the presented model is assessed through a simulation that makes use of a DApp we created and Gazebo software. The purpose of this work is to develop a reliable and secure UAV swarm communication system that will enable widespread global adoption by numerous sectors.

Lean back or lean in? Exploring social loafing in human–robot teams

Introduction: Thanks to technological advances, robots are now being used for a wide range of tasks in the workplace. They are often introduced as team partners to assist workers. This teaming is typically associated with positive effects on work performance and outcomes. However, little is known about whether typical performance-reducing effects that occur in human teams also occur in human–robot teams. For example, it is not clear whether social loafing, defined as reduced individual effort on a task performed in a team compared to a task performed alone, can also occur in human–robot teams.Methods: We investigated this question in an experimental study in which participants worked on an industrial defect inspection task that required them to search for manufacturing defects on circuit boards. One group of participants worked on the task alone, while the other group worked with a robot team partner, receiving boards that had already been inspected by the robot. The robot was quite reliable and marked defects on the boards before handing them over to the human. However, it missed 5 defects. The dependent behavioural measures of interest were effort, operationalised as inspection time and area inspected on the board, and defect detection performance. In addition, subjects rated their subjective effort, performance, and perceived responsibility for the task.Results: Participants in both groups inspected almost the entire board surface, took their time searching, and rated their subjective effort as high. However, participants working in a team with the robot found on average 3.3 defects. People working alone found significantly more defects on these 5 occasions–an average of 4.2.Discussion: This suggests that participants may have searched the boards less attentively when working with a robot team partner. The participants in our study seemed to have maintained the motor effort to search the boards, but it appears that the search was carried out with less mental effort and less attention to the information being sampled. Changes in mental effort are much harder to measure, but need to be minimised to ensure good performance.

Coordination of drones at scale: Decentralized energy-aware swarm intelligence for spatio-temporal sensing

Smart City applications, such as traffic monitoring and disaster response, often use swarms of intelligent and cooperative drones to efficiently collect sensor data over different areas of interest and time spans. However, when the required sensing becomes spatio-temporally large and varying, a collective arrangement of sensing tasks to a large number of battery-restricted and distributed drones is challenging. To address this problem, this paper introduces a scalable and energy-aware model for planning and coordination of spatio-temporal sensing. The coordination model is built upon a decentralized multi-agent collective learning algorithm (EPOS) to ensure scalability, resilience, and flexibility that existing approaches lack of. Experimental results illustrate the outstanding performance of the proposed method compared to state-of-the-art methods. Analytical results contribute a deeper understanding of how coordinated mobility of drones influences sensing performance. This novel coordination solution is applied to traffic monitoring using real-world data to demonstrate a 46.45% more accurate and 2.88% more efficient detection of vehicles as the number of drones become a scarce resource.

Drone routing problem with swarm synchronization

The advantages of rotary-wing drone (RWD) delivery modes have already been delineated. However, single-unit RWDs do not completely solve real problems in last-mile parcel deliveries because of limited payload capacity and flight endurance. Currently, drone swarm technology is being rapidly developed. An RWD swarm can deliver heavy or multiple packages to customers; therefore, the RWD swarm strategy can address the payload capacity limitation of RWDs. Herein, an RWD delivery mode that involves dynamic swarms of RWDs in addition to single-unit RWDs is explored. The “dynamic” characteristic permits the RWD members in swarms to vary by coupling/decoupling operations at nodes. From the routing plan perspective, using RWD swarms for last-mile parcel deliveries is challenging; accordingly, we introduce a swarm synchronization mode that involves interactions among RWD routes. We formally define the drone routing problem with swarm synchronization (DRP-SS) and develop a mixed-integer linear programming model, which considers the decision on RWD swarms and multi trips. An adaptive large neighborhood search heuristic with specific operators is proposed. In the computational experiments, both small- and large-scale instances are used to validate the effectiveness of the mathematical formulation and the heuristic. Several managerial insights are obtained regarding the influence of detours, the utilization of RWD swarms, and the benefits of multi trips. The DRP-SS model and solution method can be used to estimate the performance of the selection of RWD swarms in practical situations.

“Unfavorable” environmental effects can also be beneficial: a simulation analysis of centipede-like swarm robots based on implicit control for navigation and exploration in unknown environments

“Unfavorable” environmental effects can also be beneficial: a simulation analysis of centipede-like swarm robots based on implicit control for navigation and exploration in unknown environments

Robotic Swarms: Decentralized Control and Emergent Behaviour

This study explores the world of robotic swarms, looking at their distributed control systems and the formation of intelligent behaviours. The paper demonstrates how decentralized control allows robotic swarms to independently decide locally while encouraging behaviours like flocking, exploration, and task distribution. These behaviours reveal how swarm systems are flexible and scalable, particularly in dynamic environments. The effects are wide-ranging and include uses in agriculture, disaster readiness, surveillance, and environmental monitoring. Robotic swarms led by decentralized control have a lot of potential to improve automation and robustness in practical settings. The study does, however, highlight the need for continued research in algorithm design to strike a balance between individual autonomy and group collaboration. When using robotic swarms, security measures and ethical considerations are crucial factors. By providing options for additional investigation and experimentation with robotic swarms as a platform for teaching and developing the subject, this research makes a positive contribution to the robotics research. Overall, the results highlight the potential and difficulties of distributed control in robotic swarms, as well as its broad impact on a variety of fields of study.

Swarm robots using Lévy walk with concession in targets exploration

Swarm robots using Lévy walk with concession in targets exploration

Weighted formation control for a swarm of robots with coexisting cooperation and competition interactions

Weighted formation control for a swarm of robots with coexisting cooperation and competition interactions

Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution

Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution

Distributed decentralized receding horizon control for very large-scale networks with application to satellite mega-constellations

The implementation feasibility of control algorithms over very large-scale networks calls for hard constraints regarding communication, computational, and memory requirements. In this paper, the decentralized receding horizon control problem for very large-scale networks of dynamically decoupled systems with a common, possibly time-varying, control objective is addressed. Each system is assumed to be modeled by linear time-varying dynamics, which can be leveraged to approximate nonlinear systems about successive points of operation. A distributed and decentralized receding horizon control solution is put forward, which: (i) takes communication delays into account; (ii) allows local communication exclusively; and (iii) whose computational and memory requirements in each computational unit do not scale with the dimension of the network. The scalability of the proposed solution enables emerging very large-scale applications of swarm robotics and networked control. This approach is applied to the orbit control problem of low Earth orbit mega-constellations, featuring high-fidelity numerical simulations for the Starlink mega-constellation.

Perceptual discrimination in the face perception of robots is attenuated compared to humans

When interacting with groups of robots, we tend to perceive them as a homogenous group where all group members have similar capabilities. This overgeneralization of capabilities is potentially due to a lack of perceptual experience with robots or a lack of motivation to see them as individuals (i.e., individuation). This can undermine trust and performance in human–robot teams. One way to overcome this issue is by designing robots that can be individuated such that each team member can be provided tasks based on its actual skills. In two experiments, we examine if humans can effectively individuate robots: Experiment 1 (n = 225) investigates how individuation performance of robot stimuli compares to that of human stimuli that either belong to a social ingroup or outgroup. Experiment 2 (n = 177) examines to what extent robots’ physical human-likeness (high versus low) affects individuation performance. Results show that although humans are able to individuate robots, they seem to individuate them to a lesser extent than both ingroup and outgroup human stimuli (Experiment 1). Furthermore, robots that are physically more humanlike are initially individuated better compared to robots that are physically less humanlike; this effect, however, diminishes over the course of the experiment, suggesting that the individuation of robots can be learned quite quickly (Experiment 2). Whether differences in individuation performance with robot versus human stimuli is primarily due to a reduced perceptual experience with robot stimuli or due to motivational aspects (i.e., robots as potential social outgroup) should be examined in future studies.

Editor’s note - Special issue on Robot Swarms in the Real World: from Design to Deployment

Editor’s note - Special issue on Robot Swarms in the Real World: from Design to Deployment