The small battery capacities of the mobile robot and the un-optimized planning efficiency of the industrial robot bottlenecked the time efficiency and productivity rate of coverage tasks in terms of speed and accuracy, putting a great constraint on the usability of the robot applications in various planning strategies in specific environmental conditions. Thus, it became highly desirable to address the optimization problems related to exploration and coverage path planning (CPP). In general, the goal of the CPP is to find an optimal coverage path with generates a collision-free trajectory by reducing the travel time, processing speed, cost energy, and the number of turns along the path length, as well as low overlapped rate, which reflect the robustness of CPP. This paper reviews the principle of CPP and discusses the development trend, including design variations and the characteristic of optimization algorithms, such as classical, heuristic, and most recent deep learning methods. Then, we compare the advantages and disadvantages of the existing CPP-based modeling in the area and target coverage. Finally, we conclude numerous open research problems of the CPP and make suggestions for future research directions to gain insights.

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A Comprehensive Review of Coverage Path Planning in Robotics Using Classical and Heuristic Algorithms

Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles.

https://www.mdpi.com/1424-8220/18/2/417/pdf

A Decentralized Framework for Multi-Agent Robotic Systems

The Cooperative Multi-Area Coverage (CMAC) refers to a class of complex tasks in which multiple robots are required to jointly cover multiple areas by performing specific operations while moving across them. Practical operations may be garbage clearance, demining, area scanning for information acquisition, and so on. This paper proposes a distributed motion planning method for multiple robots to cooperatively accomplish CMAC tasks. Firstly, a general multi-robot task model recently proposed, named multi-point dynamic aggregation (MPDA), is applied to formulate the multi-robot motion planning problem in CMAC. Then, a rule-based heuristic for the distributed motion planning of each single robot is set forth. Further, coordination mechanisms are proposed to coordinate multiple robots from the perspective of both task allocation and motion planning. Simulations validate the effectiveness of the proposed distributed motion planning method.

Distributed multi-robot motion planning for cooperative multi-area coverage

Many scholars have proposed different single-robot coverage path planning (SCPP) and multi-robot coverage path planning (MCPP) algorithms to solve the coverage path planning (CPP) problem of robots in specific areas. However, in outdoor environments, especially in emergency search and rescue tasks, complex geographic environments reduce the task execution efficiency of robots. Existing CPP algorithms have hardly considered environmental complexity. This article proposed an MCPP algorithm considering the complex land cover types in outdoor environments to solve the related problems. The algorithm first describes the visual fields of the robots in different land cover types by constructing a hierarchical quadtree and builds the adjacent topological relations among the cells in the same and different layers in the hierarchical quadtree by defining shared neighbor direction based on Binary System. The algorithm then performs an approximately balanced task assignment to the robots considering the moving speeds in different land cover types using the azimuth trend method we proposed to ensure the convergence of the task assignment process. Finally, the algorithm improves Spanning Tree Covering (STC) algorithm to complete the CPP in the area where each robot belongs. This study used a classification image of the real outdoor environment to the verification of the algorithm. Results show that the coverage paths planned by the algorithm are reasonable and efficient and its performance has obvious advantages compare with the current mainstream MCPP algorithm.

/pdf/a-multi-robot-coverage-path-planning-algorithm-for-the-paud12e4vw.pdf

A Multi-Robot Coverage Path Planning Algorithm for the Environment With Multiple Land Cover Types

In large scale coverage operations, such as marine exploration or aerial monitoring, single robot approaches are not ideal, as they may take too long to cover a large area. In such scenarios, multi-robot approaches are preferable. Furthermore, several real world vehicles are non-holonomic, but can be modeled using Dubins vehicle kinematics. This paper focuses on environmental monitoring of aquatic environments using Autonomous Surface Vehicles (ASVs). In particular, we propose a novel approach for solving the problem of complete coverage of a known environment by a multi-robot team consisting of Dubins vehicles. It is worth noting that both multi-robot coverage and Dubins vehicle coverage are NP-complete problems. As such, we present two heuristics methods based on a variant of the traveling salesman problem -- k-TSP -- formulation and clustering algorithms that efficiently solve the problem. The proposed methods are tested both in simulations to assess their scalability and with a team of ASVs operating on a lake to ensure their applicability in real world.

Multi-robot Dubins Coverage with Autonomous Surface Vehicles

The multi-robot coverage motion planning (MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the “auction-based spanning tree coverage (A-STC)” algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes’ relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

A-STC: auction-based spanning tree coverage algorithm formotion planning of cooperative robots

A kind of typical task model, namely Multi-Point Dynamic Aggregation (MPDA), is proposed in this paper for systematically studying the cooperation and coordination methodologies of multiple robots in complex tasks. MPDA refers to that multiple robots which are located in different places aggregate to their task points to complete their tasks cooperatively, and the tasks are completed when the state of each task point reaches to a value of hope. In the MPDA task, a distributed motion planning is proposed in this paper for the motion planning of multiple robots. Simulation results show that the algorithm can effectively solve the motion planning problem of multiple robots in the MPDA task. It is also demonstrated that all robots can effectively avoid collisions with obstacles in the environment and other robots, and cooperatively complete the MPDA task efficiently.

Coordinated motion planning of multiple robots in multi-point dynamic aggregation task

Multipoint dynamic aggregation is a meaningful optimization problem due to its important real-world applications, such as post-disaster relief, medical resource scheduling, and bushfire elimination. The problem aims to design the optimal plan for a set of robots to execute geographically distributed tasks. Unlike the majority of scheduling and routing problems, the tasks in this problem can be executed by multiple robots collaboratively. Meanwhile, the demand of each task changes over time at an incremental rate and is affected by the abilities of the robots executing it. This poses extra challenges to the problem, as it has to consider complex coupled relationships among robots and tasks. To effectively solve the problem, this article develops a new metaheuristic algorithm, called adaptive coordination ant colony optimization (ACO). We develop a novel coordinated solution construction process using multiple ants and pheromone matrices (each robot/ant forages a path according to its own pheromone matrix) to effectively handle the collaborations between robots. We also propose adaptive heuristic information based on domain knowledge to promote efficiency, a pheromone-based repair mechanism to tackle the tight constraints of the problem, and an elaborate local search to enhance the exploitation ability of the algorithm. The experimental results show that the proposed adaptive coordination ACO significantly outperforms the state-of-the-art methods in terms of both effectiveness and efficiency.

Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation.

Multi-Point Dynamic Aggregation (MPDA) is a novel task model for describing the process of multiple robots performing time-variant tasks. In the MPDA problem, several task points are located in different places and their states change over time. Multiple robots aggregate to these task points and execute the tasks cooperatively to make the states of all the task points change to zero. The task planning of MPDA is a typical NP-hard combinatorial optimization problem. Estimation of Distribution Algorithms (EDA) are evolutionary techniques based on probabilistic models. In this paper, a permutation-based EDA is proposed to solve the task planning problems in MPDA. The algorithm uses K-means clustering to update its probabilistic model which follows the multi-modal Gaussian distribution. Experimental results show that the proposed algorithm outperforms other compared methods in solving the task planning problems of MPDA.

Guanqiang Gao

Papers

Distributed multi-robot motion planning for cooperative multi-area coverage

A-STC: auction-based spanning tree coverage algorithm formotion planning of cooperative robots

Coordinated motion planning of multiple robots in multi-point dynamic aggregation task

Adaptive Coordination Ant Colony Optimization for Multipoint Dynamic Aggregation.

An Estimation of Distribution Algorithm for Multi-robot Multi-point Dynamic Aggregation Problem