Multi-Robot Collaborative Hunting in Cluttered Environments With Obstacle-Avoiding Voronoi Cells

This work proposes an online collaborative hunting strategy for multi-robot systems based on obstacle-avoiding Voronoi cells in a complex dynamic environment. This involves firstly designing the construction method using a support vector machine(SVM) based on the definition of buffered Voronoi cells(BVCs). Based on the safe collision-free region of the robots, the boundary weights between the robots and the obstacles are dynamically updated such that the robots are tangent to the buffered Voronoi safety areas without intersecting with the obstacles. Then, the robots are controlled to move within their own buffered Voronoi safety area to achieve collision-avoidance with other robots and obstacles. The next step involves proposing a hunting method that optimizes collaboration between the pursuers and evaders. Some hunting points are generated and distributed evenly around a circle. Next, the pursuers are assigned to match the optimal points based on the Hungarian algorithm.Then, a hunting controller is designed to improve the containment capability and minimize containment time based on collision risk. Finally, simulation results have demonstrated that the proposed cooperative hunting method is more competitive in terms of time and travel distance.

Trajectory compensation for multi-robot coordinated lifting system considering elastic catenary of the rope

The multi-robot coordinated lifting system is an unconstrained system with a rigid and flexible coupling.The deformation of the flexible rope causes errors in the movement trajectory of the lifting system.Based on the kinematic and dynamic analysis of the lifting system,the elastic catenary mod-el considering the elasticity and mass of the flexible rope is established,and the effect of the deform-ation of the flexible rope on the position and posture of the suspended object is analyzed.According to the deformation of flexible rope,a real-time trajectory compensation method is proposed based on the compensation principle of position and posture.Under the lifting task of the low-speed move-ment,this is compared with that of the system which neglects the deformation of the flexible rope.The trajectoy of the lifting system considering the deformation of flexible rope.The results show that the mass and elasticity of the flexible rope can not be neglected.Meanwhile,the proposed trajectory compensation method can improve the movement accuracy of the lifting system,which verifies the ef-fectiveness of this compensation method.The research results provide the basis for trajectory plan-ning and coordinated control of the lifting system。

A Survey of Underwater Multi-Robot Systems

As a cross-cutting field between ocean development and multi-robot system(MRS),the underwater multi-robot system(UMRS)has gained increasing attention from researchers and engineers in recent decades.In this paper,we present a comprehensive survey of cooperation issues,one of the key components of UMRS,from the perspective of the emergence of new functions.More specifically,we categorize the cooperation in terms of task-space,motion-space,measurement-space,as well as their combination.Further,we analyze the architecture of UMRS from three aspects,i.e.,the performance of the individual underwater robot,the new functions of underwater robots,and the technical approaches of MRS.To conclude,we have discussed related promising directions for future research.This survey provides valuable insight into the reasonable utilization of UMRS to attain diverse underwater tasks in complex ocean application scenarios.

Gini Coefficient-based Task Allocation for Multi-robot Systems With Limited Energy Resources

Nowadays, robots generally have a variety of capabilities, which often form a coalition replacing human to work in dangerous environment, such as rescue, exploration, etc. In these operating conditions, the energy supply of robots usually cannot be guaranteed. If the energy resources of some robots are consumed too fast, the number of the future tasks of the coalition will be affected. This paper will develop a novel task allocation method based on Gini coefficient to make full use of limited energy resources of multi-robot system to maximize the number of tasks. At the same time, considering resources consumption,we incorporate the market-based allocation mechanism into our Gini coefficient-based method and propose a hybrid method,which can flexibly optimize the task completion number and the resource consumption according to the application contexts.Experiments show that the multi-robot system with limited energy resources can accomplish more tasks by the proposed Gini coefficient-based method, and the hybrid method can be dynamically adaptive to changes of the work environment and realize the dual optimization goals.

A Survey of Multi-robot Regular and Adversarial Patrolling

Multi-robot systems can be applied to patrol a concerned environment for security purposes.According to different goals,this work reviews the existing researches in a multi-robot patrolling field from the perspectives of regular and adversarial patrolling.Regular patrolling requires robots to visit important locations as frequently as possible and a series of deterministic strategies are proposed,while adversarial one focuses on unpredictable robots’moving patterns to maximize adversary detection probability.Under each category,a systematic survey is done including problem statements and modeling,patrolling objectives and evaluation criteria,and representative patrolling strategies and approaches.Existing problems and open questions are presented accordingly.

Multi-robot task allocation for exploration

The problem of allocating a number of exploration tasks to a team of mobile robots in dynamic environments was studied. The team mission is to visit several distributed targets. The path cost of target is proportional to the distance that a robot has to move to visit the target. The team objective is to minimize the average path cost of target over all targets. Finding an optimal allocation is strongly NP-hard. The proposed algorithm can produce a near-optimal solution to it. The allocation can be cast in terms of a multi-round single-item auction by which robots bid on targets. In each auction round, one target is assigned to a robot that produces the lowest path cost of the target. The allocated targets form a forest where each tree corresponds a robot’s exploring targets set. Each robot constructs an exploring path through depth-first search in its target tree. The time complexity of the proposed algorithm is polynomial. Simulation experiments show that the allocating method is valid.

Dynamic Frontier-Led Swarming:Multi-Robot Repeated Coverage in Dynamic Environments

A common assumption of coverage path planning research is a static environment.Such environments require only a single visit to each area to achieve coverage.However,some real-world environments are characterised by the presence of unexpected,dynamic obstacles.They require areas to be revisited periodically to maintain an accurate coverage map,as well as reactive obstacle avoidance.This paper proposes a novel swarmbased control algorithm for multi-robot exploration and repeated coverage in environments with unknown,dynamic obstacles.The algorithm combines two elements:frontier-led swarming for driving exploration by a group of robots,and pheromone-based stigmergy for controlling repeated coverage while avoiding obstacles.We tested the performance of our approach on heterogeneous and homogeneous groups of mobile robots in different environments.We measure both repeated coverage performance and obstacle avoidance ability.Through a series of comparison experiments,we demonstrate that our proposed strategy has superior performance to recently presented multi-robot repeated coverage methodologies.

Genetic Algorithm Based Combinatorial Auction Method for Multi-Robot Task Allocation

An improved genetic algorithm is proposed to solve the problem of bad real-time performance or inability to get a global optimal/better solution when applying single-item auction （SIA） method or combinatorial auction method to multi-robot task allocation. The genetic algorithm based combinatorial auction （GACA） method which combines the basic-genetic algorithm with a new concept of ringed chromosome is used to solve the winner determination problem （WDP） of combinatorial auction. The simulation experiments are conducted in OpenSim, a multi-robot simulator. The results show that GACA can get a satisfying solution in a reasonable shot time, and compared with SIA or parthenogenesis algorithm combinatorial auction （PGACA） method, it is the simplest and has higher search efficiency, also, GACA can get a global better/optimal solution and satisfy the high real-time requirement of multi-robot task allocation.

Multi-robot hunting strategy based on FIS and artificial immune algorithm

data is gathered to describe the relative location and relative motion state of the robots, which in turn forms the beginning stage of the fuzzy rule. The artificial immune algorithm optimizes and generates the rule data base and adaptive design considers factors in measuring the hunting efficiency. The optimized rules are applied to the hunting task and the results show that the algorithm can effectively actualize hunting of multiple mobile robots.

Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning:A Review

The robotics industry has seen rapid development in recent years due to the Corona Virus Disease 2019.With the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine learning.However,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data silos.The emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of robots.However,as scholars have studied more deeply,they found that federated learning has very limited privacy protection.Therefore,how to protect data privacy from infringement remains an important issue.In this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.

Graph rigidity and localization of multi-robot formations

This paper provides theoretical foundation for the problem of localization in multi-robot formations. Sufficient and necessary conditions for completely localizing a formation of mobile robots/vehicles in SE(2) based on distributed sensor networks and graph rigidity are proposed. A method for estimating the quality of localizations via a linearized weighted least-squares algorithm is presented, which considers incomplete and noisy sensory information. The approach in this paper had been implemented in a multi-robot system of five car-like robots equipped with omni-directional cameras and IEEE 802.11b wireless network.

New coordination scheme for multi-robot systems based on state space models

A new coordination scheme for multi-robot systems is proposed. A state space model of the multi- robot system is defined and constructed in which the system＇s initial and goal states are included along with the task definition and the system＇s internal and external constraints. Task accomplishment is considered a transition of the system state in its state space （SS） under the system＇s constraints. Therefore, if there exists a connectable path within reachable area of the SS from the initial state to the goal state, the task is realizable. The optimal strategy for the task realization under constraints is investigated and reached by searching for the optimal state transition trajectory of the robot system in the SS. Moreover, if there is no connectable path, which means the task cannot be performed Successfully, the task could be transformed to be realizable by making the initial state and the goal state connectable and finding a path connecting them in the system＇s SS. This might be done via adjusting the system＇s configuration and/or task constraints. Experiments of multi-robot formation control with obstacles in the environment are conducted and simulation results show the validity of the proposed method.

Applications and Challenges of Deep Reinforcement Learning in Multi-robot Path Planning

With the rapid advancement of deep reinforcement learning(DRL)in multi-agent systems,a variety of practical application challenges and solutions in the direction of multi-agent deep reinforcement learning(MADRL)are surfacing.Path planning in a collision-free environment is essential for many robots to do tasks quickly and efficiently,and path planning for multiple robots using deep reinforcement learning is a new research area in the field of robotics and artificial intelligence.In this paper,we sort out the training methods for multi-robot path planning,as well as summarize the practical applications in the field of DRL-based multi-robot path planning based on the methods;finally,we suggest possible research directions for researchers.

COLLISION-FREE OF MULTI-ROBOT SYSTEMS IN VIRTUAL ENVIRONMENT

This paper described a new method to plan out welding paths for multiple robots in virtual manufacturing environment. We first distribute welding tasks and priority for multi robots, and then apply corresponding behavior rules to help to plan out welding paths for robots collision free, which is a base fixed problem. Finally, we testify the algorithm to be practical in virtual environment, and output robot programs to direct production process. This new way will help us to find a new development method for multiple robots path planning.

Flocking of multi-robot systems with connectivity maintenance on directed graphs

Analysis and design techniques for cooperative flocking of nonholonomic multi-robot systems with connectivity maintenance on directed graphs are presented. First, a set of bounded and smoothly distributed control protocols are devised via carefully designing a class of bounded artificial potential fields （APF） which could guarantee the connectivity maintenance, col ision avoidance and distance stabilization simultaneously during the system evolution. The connectivity of the underlying network can be preserved, and the desired stable flocking behavior can be achieved provided that the initial communication topology is strongly connected rather than undirected or balanced, which relaxes the constraints for group topology and extends the previous work to more generalized directed graphs. Furthermore, the proposed control algorithm is extended to solve the flocking problem with a virtual leader. In this case, it is shown that al robots can asymptotically move with the desired velocity and orientation even if there is only one informed robot in the team. Finally, nontrivial simulations and experiments are conducted to verify the effectiveness of the proposed algorithm.

Collaborative task planning for an internet based multi-operator multi-robot system

In an Internet based multi-operator and multi-robot system (IMOMR), operators have to work collaboratively to overcome the constraints of space and time. Inherently, the activities among them can be defined as a computer-supported cooperative work (CSCW). As a practical application of CSCW, a collaborative task planning system (CTPS) for IMOMR is proposed in this paper on the basis of Petri nets. Its definition, components design, and concrete implementation are given in detail, respectively. As a result, a clear collaboration mechanism of multiple operators in an IMOMR is obtained to guarantee their task planning.

Discrete-event modeling for internet multi-robotics

Internet multi-robotics is a typical discrete-event system. In order to describe joint activities between multiple operators and multiple robots, a 4-level discrete-event model is proposed in this paper based on the controlled condition/event Petri nets (CCEP). On the first or mission level, the task splitting of the system is defined; on the second or multi-operator level, a precedence graph is introduced for every operator to plan his or her robotic actions; on the third or coordination level, the above precedence graphs are translated and integrated into the corresponding CCEPs in terms of specific rules; and on the last or multi-robot level, operators can select their control range by setting the corresponding control marks of the obtained CCEPs. As a consequence, a clear mechanism of operator-robot collaboration is obtained to conduct the development of the system.

Research on Multi-robot Coordination Searching Ignition Sources Approach

For the purpose of study on forecasting forest fire behavior,a probability approach was presented to search ignition sources by multi-robot coordination. Firstly,the environment map is built based on Bayes rules. Then,the probability searching strategy based on the environment map was designed. Every grid of the searching area was assigned searching expectation value, and robots selected the grid with the highest expectation value as its searching target. The simulation results show the search time reduces greatly,which proves the feasibility and validity of the given algorithm under unknown fire condition.

Internet based design of distributed virtual environment for multi-robot teleoperation

Studies the design of distributed virtual environments （DVEs） for tele-multi-robotics. The proposed design, incorporating two models （ distributlon-supported model and VE-supported model）, attempts to represent common functionality, communication issues, and requirements found in multi-operator DVEs. The distribution-supported model concentrates on the introduction of computer-supported collaborative work （CSCW） to realize the coordination of multi-operators, while the VE-supported model concentrates on the utilization of an object-oriented approach to strengthen the expandability and robustness of the system. Finally, the configuration anti running environments of the system are given.

A Control Strategy for Multi-Robot System Navigating in a Dynamic Environment

This paper introduces a new control strategy for heterogeneous multi-robots systems dedicated to industrial logistic setups. This control strategy is based on both distributed intelligence and machine learning and involves three parts: the rigid formation controller, the perception system and the path planner. Our controller is event-based and thus its control-coordination strategy can be self-adaptive and applied to real dynamic environment. During the navigating process, the multi-robots system derives the environment model, performs the path planning process that guaranties both the transportation constraints and the obstacle avoidance. For the validation, both simulation and real robot experiments are performed. The results show that the developed control strategy can be well used for realistic logistics applications.