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We present novel techniques for simultaneous task allocation and planning in multi-robot systems operating under uncertainty. By performing task allocation and planning simultaneously, allocations are informed by individual robot behaviour, creating more efficient team behaviour. We go beyond existing work by planning for task reallocation across the team given a model of partial task satisfaction under potential robot failures and uncertain action outcomes. We model the problem using Markov decision processes, with tasks encoded in co-safe linear temporal logic, and optimise for the expected number of tasks completed by the team. To avoid the inherent complexity of joint models, we propose an alternative model that simultaneously considers task allocation and planning, but in a sequential fashion. We then build a joint policy from the sequential policy obtained from our model, thus allowing for concurrent policy execution. Furthermore, to enable adaptation in the case of robot failures, we consider replanning from failure states and propose an approach to preemptively replan in an anytime fashion, replanning for more probable failure states first. Our method also allows us to quantify the performance of the team by providing an analysis of properties, such as the expected number of completed tasks under concurrent policy execution. We implement and extensively evaluate our approach on a range of scenarios. We compare its performance to a state-of-the-art baseline in decoupled task allocation and planning: sequential single-item auctions. Our approach outperforms the baseline in terms of computation time and the number of times replanning is required on robot failure. [ABSTRACT FROM AUTHOR]

AbstractThis article presents the explanation of higher than expected increase of stiffness of coupled robots in some geometric configurations against its expected simple addition. This phenomenon was named the vault principle for robots. In order to achieve that and to explore fully the consequences it was necessary to develop a new method for robot stiffness analysis. Single serial robot is compared with two serial robots mutually connected. Connected (coupled) robots are analyzed in five various topological configurations. The end effector stiffness field is evaluated at particular points of intersections of workspaces of five topological configurations in order to preserve the objectivity of the comparison. The new method for robot stiffness analysis is based on the evaluation of minimum/maximum values of principal axes of stiffness distribution determined by the singular value decomposition. The manimum/maximum values across the workspace are processed by average/median and used for computing homogeneity factor. It is a new integral criterion for robot topology configuration judgment. The direct implications of these findings are relevant to high-precision milling applications in cooperative robotic setups. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach for planning for teams of heterogeneous robots with different capabilities and the transportation of resources. We use Capability Temporal Logic (CaTL), a formal language that helps express tasks involving robots with multiple capabilities with spatial, temporal, and logical constraints. We extend CaTL to also capture resource constraints, where resources can be divisible and indivisible, for instance, sand and bricks, respectively. Robots transport resources using various storage types, such as uniform (shared storage among resources) and compartmental (individual storage per resource). Robots' resource transportation capacity is defined based on resource type and robot class. Robot and resource dynamics and the CaTL mission are jointly encoded in a Mixed Integer Linear Programming (MILP), which maximizes disjoint robot and resource robustness while minimizing spurious movement of both. We propose a multi-robustness approach for Multi-Class Signal Temporal Logic (mcSTL), allowing for generalized quantitative semantics across multiple predicate classes. Thus, we compute availability robustness scores for robots and resources separately. Finally, we conduct multiple experiments demonstrating functionality and time performance by varying resources and storage types. [ABSTRACT FROM AUTHOR]

Multi-robot SLAM (simultaneous localization and mapping) is crucial for the implementation of robots in practical scenarios. Bandwidth constraints significantly influence multi-robot SLAM systems, prompting a reliance on lightweight feature descriptors for robot cooperation in positioning tasks. Real-time map sharing among robots is also frequently ignored in such systems. Consequently, such algorithms are not feasible for autonomous multi-robot navigation tasks in the real world. Furthermore, the computation cost of the global optimization of multi-robot SLAM increases significantly in large-scale scenes. In this study, we introduce a novel distributed multi-robot SLAM framework incorporating sliding window-based optimization to mitigate computation loads and manage inter-robot loop closure constraints. In particular, we transmit a 2.5D grid map of the keyframe-based submap between robots to promote map consistency among robots and maintain bandwidth efficiency in data exchange. The proposed algorithm was evaluated in extensive experimental environments, and the results validate its effectiveness and superiority over other mainstream methods. [ABSTRACT FROM AUTHOR]

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of decentralized, collaborative state estimation in robotic teams. In particular, this paper considers problems where individual robots estimate similar physical quantities, such as each other's position relative to themselves. The use of pseudomeasurements is introduced as a means of modeling such relationships between robots' state estimates and is shown to be a tractable way to approach the decentralized state estimation problem. Moreover, this formulation easily leads to a general-purpose observability test that simultaneously accounts for measurements that robots collect from their own sensors, as well as the communication structure within the team. Finally, input preintegration is proposed as a communication-efficient way of sharing odometry information between robots, and the entire theory is appropriate for both vector-space and Lie-group state definitions. To overcome the need for communicating preintegrated covariance information, a deep autoencoder is proposed that reconstructs the covariance information from the inputs, hence further reducing the communication requirements. The proposed framework is evaluated on three different simulated problems, and one experiment involving three quadcopters. [ABSTRACT FROM AUTHOR]

The current trajectory planning methods for multi‐robot systems face challenges due to high computational burden and inadequate adaptability in complex constrained environments, obstructing efficiency improvements in production and logistics. This article presents an innovative solution by integrating model predictive contouring control (MPCC) and long short‐term memory (LSTM) networks for real‐time trajectory planning of multiple mobile robots. Based on the datasets generated by MPCC, a customized LSTM network is constructed to learn the collaborative planning behavior from these datasets offline, subsequently producing smooth and efficient trajectories online with a low computational burden. Moreover, a hybrid control scheme, incorporating a lidar‐based safety evaluator, avoids unexpected collision risks by switching to MPCC when necessary, ensuring the overall safety and reliability of the multi‐robot system. The proposed hybrid LSTM method is implemented and tested in the robot operating system (ROS) within diverse constrained scenarios. Experimental results demonstrate that the hybrid LSTM method achieves ≈6% enhancements in trajectory productivity and a reduced computational burden of roughly 75% compared to MPCC, thereby providing a promising solution for local multi‐robot trajectory planning in logistics transportation tasks. [ABSTRACT FROM AUTHOR]

Copyright of Modares Mechanical Engineering is the property of Tarbiat Modares University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Communication is crucial for human-robot collaborative tasks. In this context, legibility studies movement as the means of implicit communication between robotic systems and a human observer. This concept has been explored mostly for manipulators and humanoid robots. In contrast, little information is available in the literature about legibility of multi-robot systems or swarms, where simplicity and non-anthropomorphism of robots, along with the complexity of their interactions and aggregated behavior impose different challenges that are not encountered in single-robot scenarios. This article investigates legibility of multi-robot systems. Hence, we extend the definition of legibility, incorporating information about high-level goals in terms of the coordination objective of the group of robots, to previous results that focused solely on the legibility of spatial goals. A set of standard multi-robot algorithms corresponding to different coordination objectives are implemented and their legibility is evaluated in a user study, where participants observe the behavior of the multi-robot system in a virtual reality setup and are asked to identify the system's spatial goal and coordination objective. The results of the study confirmed that coordination objectives are discernible by the users, hence multi-robot systems can be controlled to be legible, in terms of spatial goal and coordination objective. [ABSTRACT FROM AUTHOR]

In recent decades, multi-robot region coverage has played an important role in the fields of environmental sensing, target searching, etc., and it has received widespread attention worldwide. Due to the effectiveness in segmenting nearest regions, Voronoi diagrams have been extensively used in recent years for multi-robot region coverage. This paper presents a survey of recent research works on region coverage methods within the framework of the Voronoi diagram, to offer a perspective for researchers in the multi-robot cooperation domain. First, some basic knowledge of the Voronoi diagram is introduced. Then, the region coverage issue under the Voronoi diagram is categorized into sensor coverage and task execution coverage problems, respectively, considering the sensor range parameter. Furthermore, a detailed analysis of the application of Voronoi diagrams to the aforementioned two problems is provided. Finally, some conclusions and potential further research perspectives in this field are given. [ABSTRACT FROM AUTHOR]

In this paper, we propose a distributed controller for holonomic and nonholonomic multi-robot systems to patrol multiple targets. Distributed control is a control method that uses only information observed by the robot itself and gathered from its surrounding without relying on centralized control. In order for the robots to accomplish their patrols, we propose an objective function that changes switching weights depending on the time when the targets are not observed. As a result, each robot determines which targets to observe based on the time weights and the information from surrounding robots to achieve efficient patrols. We design a distributed controller based on the gradient-flow method for both holonomic and nonholonomic systems. Finally, simulation and experimental results show that the proposed controller is useful for the robots to realize patrolling. [ABSTRACT FROM AUTHOR]

Multi-Robot Task Allocation (MRTA) is a complex problem domain with the majority of problem representations categorized as NP-hard. Existing solution approaches handling dynamic MRTA scenarios do not consider the problem structure changes as a possible system dynamic. RoSTAM (Robust and Self-adaptive Task Allocation for Multi-robot teams) presents a novel approach to handle a variety of MRTA problem representations without any alterations to the task allocation framework. RoSTAM's capabilities against a range of MRTA problem distributions have already been established. This paper further validates RoSTAM's performance against the more conventional dynamics, such as robot failure and new task arrival, while performing allocations against two of the most frequently faced problem representations. The framework's performance is evaluated against a state-of-the-art online auction scheme. The results validate RoSTAM's capability to allocate tasks across a range of dynamics efficiently. [ABSTRACT FROM AUTHOR]

Over decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots' positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper's purpose is to overcome these challenges using a novel force-based approach taking into account the robots' actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object's shape. The controller's functionality and performance as well as the scheme's versatility are demonstrated by several hardware experiments. [ABSTRACT FROM AUTHOR]

Software development for robotics applications is still a major challenge that becomes even more complex when considering multi-robot systems (MRSs). Such distributed software has to perform multiple cooperating tasks in a well-coordinated manner to avoid unsatisfactory emerging behavior. This paper provides an approach for programming MRSs at a high abstraction level using the programming language X-Klaim. The computation and communication model of X-Klaim, based on multiple distributed tuple spaces, permits coordinating with the same abstractions and mechanisms both intra- and inter-robot interactions of an MRS. This allows developers to focus on MRS behavior, achieving readable, reusable, and maintainable code. The proposed approach can be used in practice by integrating X-Klaim and the popular robotics framework ROS. We demonstrate the feasibility and effectiveness of our approach by (i) showing how it scales when implementing two warehouse scenarios allowing us to reuse most of the code when passing from the simpler to the more enriched scenario and (ii) presenting the results of a few experiments showing that our code introduces a slightly greater but acceptable latency and consumes less memory than the traditional ROS implementation based on Python code. [ABSTRACT FROM AUTHOR]

This work addresses a time‐sensitive coverage control problem for multiple non‐holonomic mobile robots. The coverage objective is evaluated by the total arrivial time of robots reaching their dominant clients with the predefined constant but heterogeneous velocities. An extremum‐seeking control framework is proposed, including a numerical optimizer that iteratively produces the optimal waypoints of robots minimizing the total arriving time and a state regulator that drives the robot's positions to the optimal locations under both constraints non‐holonomic kinematic model and the constant‐velocity. An improved K‐means algorithm is first designed to produce the waypoint sequence. A theoretical analysis of the convergence of coverage objectives is provided. Then a fixed‐time state regulator for the robot's angular velocity is derived from regulating robot positions to the desired waypoints within each time step that varies with the heterogeneous traversing velocity and the distance between waypoints. Finally, the results are validated by a virtual robotic simulation platform and the experiment on real robots, showing that the proposed methods can efficiently deploy multiple heterogeneous robots for time‐sensitive services to clients with a discrete distribution. The simulation and experimental code are open‐sourced at https://gitee.com/ahu‐icip/simulation.git. [ABSTRACT FROM AUTHOR]

In multi-robot simultaneous localization and mapping (SLAM) systems, the system must create a consistent global map with multiple local maps and loop closures between robot poses. However, false-positive loop closures caused by perceptual aliasing can severely distort the global map, especially in GNSS-denied areas, where a good prior of relative poses between robots is unavailable. In addition, the performance of the consistency metric in existing map fusion methods relies on accurate odometry from each robot. However, in practice, cumulative noise is inevitably present in robot trajectories, which leads to poor map fusion with existing methods. Thus, in this paper, we propose a robust consistency-based inter-robot and intra-robot loop closure selection algorithm for map fusion. We consider both pairwise-loop consistency and loop-odometry consistency to improve robustness against false-positive loop closures and accumulative noise in the odometry. Specifically, we select a reliable inter-robot loop closure measurement with a consistency-based strategy to provide an initial prior of relative pose between two robot trajectories and update the pose variables of the robot trajectories. The loop closure selection problem is formulated as a maximum edge weight clique problem in graph theory. A performance evaluation of the proposed method was conducted on the ManhattanOlson3500, modified CSAIL and Bicocca datasets, and the experimental results demonstrate that the proposed method outperforms the pairwise consistency measurement set maximization method (PCM) under severe accumulative noise and can be integrated with M-estimation methods. [ABSTRACT FROM AUTHOR]

A primary challenge to form a geometric pattern for a fleet of randomly distributed agents is the determination of their locations on the boundary of a formation. The challenge is more acute if the agents need to be uniformly distributed on the formation. This study addresses this challenge for polygon formations via a two-phase procedure. In the first phase, the agents form an enclosing circle to the location of the formation. This prevents the agents from location-conflicts on the polygon and hence each agent can uniquely ascertain its projection point on the formation. In the second phase, the agents establish their projected points and move toward their locations on the polygon, while mitigating collisions. The circle formation is also used as a regrouping feature before the agents reconfigure themselves into a different polygon formation. The formation control laws developed are verified through simulation for circle formation, convex polygons, and some categories of concave polygons. The control laws include the cases for rotation, translation (relocation), and scaling of polygons as well. [ABSTRACT FROM AUTHOR]

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving the unknown area exploration problem with energy constraints using metaheuristics in both single- and multi-robot scenarios. This framework allowed us to benchmark the performances of different metaheuristics for the robotics exploration problem. We conducted several experiments to validate this framework and used it to compare the effectiveness of xBOA with well-known metaheuristics used in the literature through 5 evaluation criteria. Although BOA and xBOA are not optimal in all these criteria, we found that BOA can be a good alternative to many metaheuristics in terms of the exploration time, while xBOA is more robust to local optima; has better fitness convergence; and achieves better exploration rates than the original BOA and its other variants. [ABSTRACT FROM AUTHOR]

This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: (i) consolidation of multiple types of data into one cohesive model, (ii) fast determination of optimal sensing locations for mobile robots, and (iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank. [ABSTRACT FROM AUTHOR]

Collision avoidance is one of the most important topics in the robotics field. In this problem, the goal is to move the robots from initial locations to target locations such that they follow the shortest non-colliding paths in the shortest time and with the least amount of energy. Robot navigation among pedestrians is an example application of this problem which is the focus of this paper. This paper presents a distributed and real-time algorithm for solving collision avoidance problems in dense and complex 2D and 3D environments. This algorithm uses angular calculations to select the optimal direction for the movement of each robot and it has been shown that these separate calculations lead to a form of cooperative behavior among agents. We evaluated the proposed approach on various simulation and experimental scenarios and compared the results with ORCA one of the most important algorithms in this field. The results show that the proposed approach is at least 25% faster than ORCA while is also more reliable. The proposed method is shown to enable fully autonomous navigation of a swarm of Crazyflies. [ABSTRACT FROM AUTHOR]

Most mission planning algorithms solve multi-robot-multi-mission problems based on mixed integer linear programming. In these algorithms, the rewards (or costs) of missions for each robot are calculated according to the purpose of the user. Then, the (robot-mission) pair that has maximum rewards (or minimum costs) is found in the rewards (or costs) table and the mission is allocated to the robot. However, it is hard to design the reward for minimizing total mission completion time because not only a robot, but also the whole robots' mission plans must be considered to achieve the purpose. In this paper, we propose centralized mission planning for multi-robot-multi-mission problems, minimizing total mission completion time. First, mission planning for single-robot-multi-mission problems is proposed because it is easy to solve. Then, this method is applied for multi-robot-multi-mission problems, adding a mission-plan-adjustment step. To show the excellent performance of the suggested algorithm in diverse situations, we demonstrate simulations for 3 representative cases: a simple case, which is composed of 3 robots and 8 missions, a medium case, which is composed of 4 robots and 30 missions, and a huge case, which is composed of 6 robots and 50 missions. The total mission completion time of the proposed algorithm for each case is lower than the results of the existing algorithm. [ABSTRACT FROM AUTHOR]

In industrial transportation applications, multi-robot systems (MRS) are assigned to perform transportation tasks until their batteries are depleted, requiring them to move to battery charging stations. This temporary unavailability of the robots during charging decreases system productivity that measures the number of transportation tasks accomplished with the available robot energy. As a result, maximizing task completion becomes crucial, especially with prioritized tasks and increased workload. This can be achieved through an energy management strategy. In this context, the Lexicographic Optimization-based Multi-Robot Task Allocation (LO-MRTA) approach is proposed to maximize the reward in terms of system productivity with a limited energy consumption. The existing multi-robot energy management approaches consider the energy management during the tasks execution and neglect workload scaling issue with increasing tasks, whereas the LO-MRTA approach accounts for the energy consumption in the large-scale tasks allocation process. The main idea of the proposed approach is to consider the global state of the robots in the MRS system before carrying out the tasks execution, enhancing the task allocation decisions in terms of the energy management. The evaluation scenarios show the promising performance of the LO-MRTA approach in comparison with three existing baselines in terms of the system productivity, overall energy consumption and workload scaling effects. • Lexicographic optimization is used for task allocation in multi-robot systems. • The approach considers the global state of the system for better energy management. • It outperforms three baselines in productivity, energy efficiency, and workload handling. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

The exploration of unknown environments is an essential application of multi-robot systems, particularly in critical missions, such as hazard detection and search and rescue. These missions share the need to reach full coverage of the explorable space in the shortest time possible. To minimize the completion time, robots in the fleet must be able to reliably exchange information about the environment with one another. One of the main methods to expand coverage is by placing relays. Existing relay-placement algorithms tend to either require prior knowledge of the environment, or they rely on maintaining specific distances between the relays and the rest of the robots. These approaches lack flexibility and adaptability to the environment. This paper introduces the "Connectivity-Aware Relay Algorithm" (CARA), a dynamic context-aware relay-placement algorithm that does not require any prior knowledge of the environment. We compare CARA against a state-of-the-art distance-based relay-placement algorithm. Our results demonstrate that CARA outperformed the state-of-the-art algorithm in terms of the time to completion by a factor of 10 as it placed, on average, half the number of relays. [ABSTRACT FROM AUTHOR]

Multiagent systems (MASs) are distributed systems with two or more intelligent agents. Formation control is a significant control technique of MASs. To date, formation control on MASs is widely used in various fields, such as robots, spacecrafts, satellites, and unmanned aerial/surface/underwater vehicles. However, there is a relatively small body of literature that is concerned with security problems of formation control on MASs in past years. Our research represents the first step toward developing security attacks of formation control on MASs. Our study aims to investigate potential security problems of formation control on a multirobot system for the first time. We propose two kinds of control-level attacks and each kind of attack includes several specific attack forms. Then, we discuss specific features of formation control on a classical multirobot system and utilize theoretical analyses to illustrate how cyberattacks can influence the physical movements of robots. The experimental results of the proposed attacks show that attacks can easily interrupt formation movements of a multirobot system and several carefully designed attacks even can cause irreversible loss. [ABSTRACT FROM AUTHOR]

This paper reports some results of network connectivity control experiments using a multi-robot system which we developed. Although a lot of connectivity control algorithms for a multi-robot network are proposed, almost all of them are verified only on computer simulations or using experimental robots with centralized sensors and controllers. To execute experimental verifications of connectivity control algorithms on a distributed robotic system, we developed an experimental multi-robot system. Hardware installed on the robot and information flow from sensors to actuators are detailed. Some results of measurement experiments are shown to estimate accuracy to detect a neighbor position. Then, results of connectivity control experiments using the developed multi-robot system are discussed. [ABSTRACT FROM AUTHOR]

Collaboration localization and mapping in GPS-denied environments is a challenging problem, due to the computing power and communication bandwidth constraints of onboard equipment. In this article, a voxel-based localization and mapping system is developed, which achieves real-time trajectory estimation for the multirobot system utilizing only onboard sensors, computing power, and communication module. The key idea of the voxel-based matching approach is to formulate the registration problem as a maximum a posteriori (MAP) problem and to simplify it to a nonlinear least-squares problem using Gauss approximation and principal component analysis (PCA). The voxel-based matching method significantly improves the computational efficiency and reduces the communication bandwidth of the localization and mapping system. The effectiveness of the proposed method is demonstrated through experiments in real-world environments for various scenarios. [ABSTRACT FROM AUTHOR]

This article addresses a new cooperative localization problem for a team of mobile robots subject to limited communication resources. First, we develop a decentralized event-triggered cooperative localization (DECL) algorithm for multirobot system such that each robot localizes itself with minimum communication exchange between robots. Then, using an event-triggered mechanism we propose an optimization framework to achieve a balance between estimation performance and communication rate. Simulation results show the main benefits of the event-triggered mechanism. Also, experimental results using four e-puck2 mobile robots demonstrate the effectiveness of the proposed method. Note to Practitioners—Multiple mobile robots are able to implement certain tasks that are beyond the capabilities of individual robots. In multirobot system, the accurate localization of each robot in the team is essential for a successful operation. Existing cooperative localization approaches neglect some realistic limitations of mobile robots, such as battery capacity and communication bandwidth. Especially, this issue is important when the number of sensors, actuators, and robots in the team increases. This article was motivated by these realistic limitations of mobile robots and it suggests a new approach for cooperative localization based on event-triggered mechanism. Motived by the aforementioned discussion, our objective is to design and implement the event-triggered cooperative localization for a group of e-puck2 robots. Our theoretical analysis and experimental results show that we achieve a tradeoff between localization accuracy and communication resources. [ABSTRACT FROM AUTHOR]

This paper investigates a distributed competition behavior in multi-robot coordination under variable communication topology and switching one. In terms of multi-robot competition-based coordination, a winner-take-all (WTA) strategy is leveraged to address this issue with inevitable environmental barriers incorporated. Moreover, an innovative control theory stimulated gradient neural network (CTSGNN) algorithm is proposed to realize the WTA with prominent robustness and convergence over the traditional ones. Besides, to adapt to diversified local communication modes among multi-robot systems, fast variable and low switching topologies are constructed to establish two dynamic consensus estimators, accompanied by the proposed distributed control schemes. Traditional algorithms are introduced and served as a contrast. Afterward, the global convergence of the proposed algorithm in dealing with multi-robot competitive coordination, the universality of the application scenario, as well as the weaknesses of traditional methods are substantiated theoretically. The effectiveness and superiority of the proposed CTSGNN algorithm and the resultant distributed control schemes by integrating consensus estimators are further sustained via simulations. Note to Practitioners—The motivation of this paper is the coordination operation of multi-robot systems, but it is also applicable to other fields adopting multi-agent systems. Most of the existing researches on multi-robot coordination only exploit their collaborative behavior, which usually leads to the system redundancy and overflow of control costs. To this end, an innovative control algorithm is proposed for this competitive coordination and remains to be perfect in terms of stability and accuracy. Then, this paper establishes new distributed control schemes for multi-robot systems to compete for optimal dynamic task allocation. In this sense, under the premise of ensuring a successful task execution, only a few individuals with strong abilities and advantages are assigned. It is promising to maximize resource utilization, increase efficiency, and be extended to multi-objective scenarios. Note that the design of this scheme takes into account the environmental constraints and physical constraints of the robot itself. Theoretical analysis and preliminary simulation experiments prove the high efficiency of the control scheme. In ongoing research, the competitive coordination tasks of the mobile robot systems and communication delays or fault in control scheme design are explored to expand the operation scope and system extensibility. [ABSTRACT FROM AUTHOR]

This paper presents a distributed scalable multi-robot planning algorithm for informed sampling of quasistatic spatials fields. We address the problem of efficient data collection using multiple autonomous vehicles and consider the effects of communication between multiple robots, acting independently, on the overall sampling performance of the team. We focus on the distributed sampling problem where the robots operate independent of their teammates, but have the ability to communicate their current state to other neighbors within a fixed communication range. Our proposed approach is scalable and adaptive to various environmental scenarios, changing robot team configurations, and runs in real-time, which are important features for many real-world applications. We compare the performance of our proposed algorithm to baseline strategies through simulated experiments that utilize models derived from both synthetic and field deployment data. The results show that our sampling algorithm is efficient even when robots in the team are operating with a limited communication range, thus demonstrating the scalability of our method in sampling large-scale environments. [ABSTRACT FROM AUTHOR]

This article reports on a multirobot system that collaboratively obtains above-water, surface, and below-water information on a floating target. This capability allows a ship to autonomously survey and obtain situational awareness on a floating unresponsive target from a safe stand-off before inspecting it more closely or navigating around it. The target could be another ship, structure, or a navigational obstruction like an iceberg. The proposed solution is a collaborative system with an unmanned aerial vehicle (UAV), an unmanned underwater vehicle (UUV), and an unmanned surface vehicle (USV). The UAV captures visual imagery to create a 3-D model of the target’s above-water geometry using photogrammetry. The UUV surveys the target’s submerged hull with integrated imaging and profiling bathymetric sonars. The USV hosts an intelligent mission-planning node which manages the robotic collaboration in a centralized architecture by autonomously planning and distributing the missions for the UUV and UAV. The intelligent node also adaptively plans the USV’s trajectory to support the other autonomous assets, specifically reducing and bounding the UUV’s state-estimate error through collaborative localization. The resulting above- and below-water sensor data is fused at the waterplane, using a sliding correlation algorithm, to yield a 3-D representation of the floating unresponsive target. The contributions from this article include the cross-domain robotic collaboration and autonomous mission-planning toward acquiring and fusing data from heterogeneous robots. The autonomous mission-planning and data-merging algorithms are presented. The setup and results from simulations and in-water testing with an UUV, USV, and UAV are described. [ABSTRACT FROM AUTHOR]

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In this paper, we develop the analytical framework for a novel Wireless signal-based Sensing capability for Robotics (WSR) by leveraging a robots' mobility in 3D space. It allows robots to primarily measure relative direction, or Angle-of-Arrival (AOA), to other robots, while operating in non-line-of-sight unmapped environments and without requiring external infrastructure. We do so by capturing all of the paths that a wireless signal traverses as it travels from a transmitting to a receiving robot in the team, which we term as an AOA profile. The key intuition behind our approach is to enable a robot to emulate antenna arrays as it moves freely in 2D and 3D space. The small differences in the phase of the wireless signals are thus processed with knowledge of robots' local displacement to obtain the profile, via a method akin to Synthetic Aperture Radar (SAR). The main contribution of this work is the development of (i) a framework to accommodate arbitrary 2D and 3D motion, as well as continuous mobility of both signal transmitting and receiving robots, while computing AOA profiles between them and (ii) a Cramer–Rao Bound analysis, based on antenna array theory, that provides a lower bound on the variance in AOA estimation as a function of the geometry of robot motion. This is a critical distinction with previous work on SAR-based methods that restrict robot mobility to prescribed motion patterns, do not generalize to the full 3D space, and require transmitting robots to be stationary during data acquisition periods. We show that allowing robots to use their full mobility in 3D space while performing SAR results in more accurate AOA profiles and thus better AOA estimation. We formally characterize this observation as the informativeness of the robots' motion, a computable quantity for which we derive a closed form. All analytical developments are substantiated by extensive simulation and hardware experiments on air/ground robot platforms using 5 GHz WiFi. Our experimental results bolster our analytical findings, demonstrating that 3D motion provides enhanced and consistent accuracy, with a total AOA error of less than 10◦ for 95% of trials. We also analytically characterize the impact of displacement estimation errors on the measured AOA and validate this theory empirically using robot displacements obtained using an off-the-shelf Intel Tracking Camera T265. Finally, we demonstrate the performance of our system on a multi-robot task where a heterogeneous air/ground pair of robots continuously measure AOA profiles over a WiFi link to achieve dynamic rendezvous in an unmapped, 300 m2 environment with occlusions. [ABSTRACT FROM AUTHOR]

For safe and efficient navigation of heterogeneous multiple mobile robots (HMRs), it is essential to incorporate dynamics (mass and inertia) in motion control algorithms. Many methods rely only on kinematics or point-mass models, resulting in conservative results or occasionally failure. This is especially true for robots with different masses. In this article, we develop a novel navigation methodology for a distributed scheme by incorporating the robots’ dynamics through calculating the time to collision (TTC) and designing a new controller accordingly that avoids collisions. We first propose a new predictive collision term by TTC that will be used to quantify imminent collisions among HMRs. Subsequently, using this term, we develop a novel nonlinear controller that explicitly incorporates TTC in the design and guarantees collision-free motion. Simulations and experiments were performed to demonstrate the effectiveness of the developed methods. We first compared the results of our proposed approach with controllers that only consider the robots’ kinematics. It was shown that the proposed control strategy (a TTC-based controller) proves to be less conservative when determining safe motions. Specifically, for environments with limited space, it was demonstrated that using robots’ kinematics may result in a collision, while our strategy results in safe motion. We also performed experiments that proved collision-free navigation of HMRs with this approach. The outcomes of this work provide more reliable motion control for HMRs, especially when the robots’ masses or inertias are significantly different, for example, warehouses. The developments in this work are also applicable to vehicles and can therefore be beneficial in automated collision avoidance in autonomous driving and intelligent transportation. [ABSTRACT FROM AUTHOR]

This article describes a novel approach to achieve motion coordination in a multirobot system based on the concept of deformation. Our main novel contribution is to link these two elements (namely, coordination and deformation). In particular, the core idea of our approach is that the robots’ motions minimize a global measure of the deformation of their positions relative to a prescribed shape. Based on this idea, we propose a linear shape controller that also incorporates a term modeling an affine deformation. We show that the affine term is particularly useful when the deformation to be controlled is large. We also propose controls for the other variables (centroid, rotation, and size) that define the geometric configuration of the team. Importantly, these additional controls are completely decoupled from the shape control. The overall approach is simple and robust, and it creates closely coordinated robot motions. Being based on deformation, it is useful in several scenarios involving manipulation tasks, e.g., handling of a highly deformable object, control of an object’s shape, or regulation of the shape formed by the fingertips of a robotic hand. We present simulation and experimental results to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Multi-robot formations have numerous applications, such as cooperative object transportation in intelligent warehouses. In this context, robots are tasked with delivering objects in formation while avoiding intra- and inter-formation collisions. This necessitates the development of solutions for multi-robot task allocation, formation generation, rigid formation route planning, and formation coordination. In this paper, we present a cooperative formation object transportation system for heterogeneous multi-robot systems which captures robot dynamics and avoids inter-formation collisions. Accounting for heterogeneous formations expands the applicability of the proposed robotic transport system. For formation generation, we propose an approach based on conflict-based search, which integrates high-level path planning with low-level trajectory optimisation. For heterogeneous formation planning, we present a two-stage iterative trajectory optimisation framework which adheres to the kinematic constraints of our heterogeneous multi-robot system while retaining formation rigidity. For multi-formation coordination, we use a loosely-coupled algorithm which can guarantee collision-free and deadlock-free formation navigation under minimal assumptions. We demonstrate the efficacy of our approach in simulation. • Multi-robot formations have numerous applications, such as cooperative object transportation in intelligent warehouses. • This paper presents a cooperative formation object transportation system for heterogeneous multi-robot systems. • Accounting for heterogeneous formations expands the applicability of the proposed robotic transport system. [ABSTRACT FROM AUTHOR]

In the real world, multi-robot systems need to deal with on-the-fly (runtime) arrivals of new sets of tasks. This entails repeated adjustments of their current task allocations to include the newer ones while also ensuring that the overall performance does not degrade. This paper proposes a decentralized and distributed dynamic task allocation algorithm to handle this issue in a multi-robot scenario. The proposed work provides a conflict-free allocation of a set of tasks constituting a job to robots and minimizes the total execution time. These jobs can comprise multiple independent and/or dependent tasks or a combination thereof, which are injected on-the-fly into a network of robots. The dependent tasks of a job are related by precedence constraints that specify the ordering or dependencies between pairs of tasks. The work also describes a decentralized adaptive energy threshold mechanism for determining whether or not a robot needs to visit a battery stockpile after the execution of a task. Conflicting task selections among the robots in this decentralized set-up are resolved using mobile agents during runtime. Apart from allocating tasks to the robots, these mobile agents exploit the benefits of centralized and decentralized systems and provide an advantage over auction-based task allocation algorithms. The proposed algorithm takes into consideration the energy requirements, both during the task allocation process and actual execution. The proposed algorithm also caters to strategies to deal with delays caused by obstacles and congestion during the actual execution of the tasks. Experiments conducted using Webots , an open-source robot simulator, and Tartarus , a multi-agent platform, authenticate the efficacy of the proposed algorithm compared to other prominent task allocation algorithms in terms of minimization of average waiting time , total task allocation time , total job allocation time , and total execution time of an experiment. • Decentralized, distributed, and dynamic precedence-constrained MRTA algorithm • Mobile agents are used to resolve task conflicts and to allocate tasks among robots. • An adaptive energy threshold for recharging the robots is used. • Energy consumption is considered both during task allocation and actual execution. • Strategies to overcome delays due to obstacles during actual execution are proposed. [ABSTRACT FROM AUTHOR]

Multi-robot collaboration has become a needed component in unknown environment exploration due to its ability to accomplish various challenging situations. Potential-field-based methods are widely used for autonomous exploration because of their high efficiency and low travel cost. However, exploration speed and collaboration ability are still challenging topics. Therefore, we propose a D istributed M ulti-Robot P otential- F ield-Based Exploration (DMPF-Explore). In particular, we first present a D istributed S ubmap-Based M ulti-Robot C ollaborative Mapping Method (DSMC-Map), which can efficiently estimate the robot trajectories and construct the global map by merging the local maps from each robot. Second, we introduce a Potential-Field-Based Exploration Strategy Augmented with M odified W ave- F ront Distance and C olored N oises (MWF-CN), in which the accessible frontier neighborhood is extended, and the colored noise provokes the enhancement of exploration performance. The proposed exploration method is deployed for simulation and real-world scenarios. The results show that our approach outperforms the existing ones regarding exploration speed and collaboration ability. • Our exploration method, DMPF-Explore, comprises the DSMC-Map and the MWF-CN. • The DSMC-Map provides reliable and consistent maps of unknown environments. • The MWF-CN boosts frontier detection and robots' adaptability. • By experiments, robots with DMPF-Explore explore more efficiently than the baselines. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm that provides operational strategies for multiple heterogeneous mobile robot systems utilized in many real-world applications, such as deliveries, surveillance, search and rescue, monitoring, and transportation. Specifically, the authors focus on developing an algorithm that solves a min–max multiple depot heterogeneous asymmetric traveling salesperson problem (MDHATSP). The algorithm is designed based on a primal–dual technique to operate given multiple heterogeneous robots located at distinctive depots by finding a tour for each robot such that all the given targets are visited by at least one robot while minimizing the last task completion time. Building on existing work, the newly developed algorithm can solve more generalized problems, including asymmetric cost problems with a min–max objective. Though producing optimal solutions requires high computational loads, the authors aim to find reasonable sub-optimal solutions within a short computation time. The algorithm was repeatedly tested in a simulation with varying problem sizes to verify its effectiveness. The computational results show that the algorithm can produce reliable solutions to apply in real-time operations within a reasonable time. [ABSTRACT FROM AUTHOR]

In this article, we address a multi-robot planning problem in environments with partially unknown semantics. The environment is assumed to have a known geometric structure (e.g., walls) and to be occupied by static labeled landmarks with uncertain positions and classes. This modeling approach gives rise to an uncertain semantic map generated by semantic simultaneous localization and mapping algorithms. Our goal is to design control policies for robots equipped with noisy perception systems so that they can accomplish collaborative tasks captured by global temporal logic specifications. To specify missions that account for environmental and perceptual uncertainty, we employ a fragment of linear temporal logic (LTL), called co-safe LTL, defined over perception-based atomic predicates modeling probabilistic satisfaction requirements. The perception-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that are updated online to adapt to a continuously learned semantic map. We provide extensive experiments to demonstrate the efficiency of the proposed planning architecture. [ABSTRACT FROM AUTHOR]

In this article, we address the problem of coordinating the motion of a team of robots with limited field of view (FOV), which induces asymmetry in their interactions. In this context, we first propose a general coordinated motion framework for multirobot systems with triangular FOV capable of guaranteeing stability under asymmetric (directed) interactions. In deriving this framework, we illustrate that asymmetry in multirobot interactions can lead to degenerate configurations for which a fully decentralized controller may be insufficient to achieve coordination. Thus, we introduce a switching control mechanism that achieves adaptive decentralization, enabling collaborative behaviors that seek support of a centralized planner for situations that are inherently unstable (degenerate). To demonstrate the generality of our framework we provide a case study involving varying team objectives, such as topology control, that the robots can achieve with limited FOV, while remaining stable. Experimental and numerical validations based on the previously discussed case study are provided to corroborate the theoretical findings [ABSTRACT FROM AUTHOR]

Multi-robot formation control has been intensively studied in recent years. In practical applications, the multi-robot system’s ability to independently change the formation to avoid collision among the robots or with obstacles is critical. In this study, a multi-robot adaptive formation control framework based on deep reinforcement learning is proposed. The framework consists of two layers, namely the execution layer and the decision-making layer. The execution layer enables the robot to approach its target position and avoid collision with other robots and obstacles through a deep network trained by a reinforcement learning method. The decision-making layer organizes all robots into a formation through a new leader–follower configuration and provides target positions to the leader and followers. The leader’s target position is kept unchanged, while the follower’s target position is changed according to the situation it encounters. In addition, to operate more effectively in environments with different levels of complexity, a hybrid switching control strategy is proposed. The simulation results demonstrate that our proposed formation control framework enables the robots to adjust formation independently to pass through obstacle areas and can be generalized to different scenarios with unknown obstacles and varying number of robots. [ABSTRACT FROM AUTHOR]

Swarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models. Note to Practitioners—This research developed a swarm foraging algorithm that takes into consideration the vision and communication sensing noise levels faced by robots in real world applications. The algorithm, known as RepAtt, was developed with the aim of emphasizing algorithmic simplicity and limiting the hardware requirements for the robots in the swarm. In this paper, we have focused on the problem of deploying swarm robots to forage litter in an environment such as a park. The communication model of the robots was based on the physics of sound, while their vision system was modelled using experiments with deep neural networks based object detectors. The results show that the RepAtt algorithm is robust to different distributions of targets (or litter) in the search space, exhibits good swarm efficiency with changes in swarm population and is robust to noise in its communication and vision systems. Apart from the RepAtt algorithm, other contributions made by this research include modelling of robot vision system to aid extensive study of the impact of communication and vision noise on swarm coordination. This will be relevant for extensive testing and validation before deployment to swarm robots hardware. The sound communication used in this research limits the kinds of environment the robots can be deployed in. Echoes within an enclosed environment and bandwidth limitation for communication frequency and public disturbance due to sound emitted by the robots can all contribute to this limitation. Thus, this research can be improved by investing in the development of a communication technology with similar physics. Other areas of improvement include adopting better obstacle avoidance algorithms and implementing suitable manipulators for handling litter objects. The algorithm can be extended to make it applicable for solving other problems such as search and rescue operations where foraging targets could be disaster survivors; demining and hazardous waste cleanup, where targets are the mines or waste material; and planetary exploration, where targets could be interesting features of the planets are the targets searched for by the robots. [ABSTRACT FROM AUTHOR]

In this overview paper, we present the work of the Goal-Oriented Long-Lived Systems Lab on multi-robot systems. We address multi-robot systems from a decision-making under uncertainty perspective, proposing approaches that explicitly reason about the inherent uncertainty of action execution, and how such stochasticity affects multi-robot coordination. To develop effective decision-making approaches, we take a special focus on (i) temporal uncertainty, in particular of action execution; (ii) the ability to provide rich guarantees of performance, both at a local (robot) level and at a global (team) level; and (iii) scaling up to systems with real-world impact. We summarise several pieces of work and highlight how they address the challenges above, and also hint at future research directions. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel framework to address the formation control problem for a class of multirobot systems with two types of constraints, namely the performance constraints and the feasibility constraints. For the performance constraints, we consider the constraint requirements on the distance tracking errors between the real and the desired trajectories for each robot, so that to ensure precise tracking of the robot without deviating too much from its desired trajectory, as well as the constraints on the interrobot distance, so that to ensure the safe operation of the team. For the feasibility constraints, we consider the constraints on the heading angle, so that the controllers designed in the brief are feasible. Universal barrier functions are adopted in the controller design and analysis, which is a generic framework that can address systems with different types of constraints in a unified controller architecture. Through rigorous analysis, exponential convergence rate can be guaranteed on the distance tracking errors, while all constraints are satisfied during the operation. A simulation example and an experiment using three AmigoBot mobile robots further demonstrate the efficacy of the proposed control framework. [ABSTRACT FROM AUTHOR]

Reconfigurable modular robots, which can actively assemble and disassemble on command, offer the possibility of mesoscale (milliscale and microscale) manufacturing with robustness and controllability. In this study, we present a design of a scalable modular subunit with embedded permanent magnets in a 3-D printed cubic body. The subunit can be wirelessly controlled by an external uniform magnetic field. We also present controlled assembly–disassembly techniques for these subunits. Our modular robotic platform is highly reconfigurable and can create programmable, predetermined patterns based on open-loop control. The 2-D motion planner computes all reachable polyomino shapes from an arbitrary initial configuration and provides the shortest movement sequences to form each shape. Experimental results match computational modeling, demonstrating robust and reproducible behavior of the modular robotic platform that is promising for mesoscale manufacturing applications. Two cube sizes were tested: 10-mm edge lengths and 2.8-mm edge lengths. [ABSTRACT FROM AUTHOR]

In a multi-robot system, several robots coordinate to perform a joint task such as border patrol, etc. There are many different methods which can be employed to model a multi-robot system. Some of these techniques are based on the discrete events systems theory. In this work, we have proposed, a novel deliberative/reactive hybrid architecture based on the supervisory control theory (SCT). We have incorporated safety requirements as well as heuristics’ performance requirements into our system. We have validated our proposed architecture via several computer simulations. Simulation results confirm the effectiveness of the proposed architecture. [ABSTRACT FROM AUTHOR]

Summary: To improve time efficiency for structured environment exploration of multi‐robot systems, a novel coordinated strategy based on predicate logic reasoning is proposed in this paper. The strategy assigns the room with maximal utility to the robot with the optimal exploring property, and realizes cooperative exploration by considering the room capacity. A set of predicate logic reasoning formulas is established to implement the coordination. The coordination takes into account robot performance and environment property and is conducted in a distributed manner. In addition, to save computing and storage resources, a triple set named as task information was proposed instead of traditional unfinished job list for environment information sharing among robots. The results are validated by computer simulations for two simulated office like environments. [ABSTRACT FROM AUTHOR]