Spanning Tree Coverage Research Articles

Multi-objective optimization approach for coverage path planning of mobile robot

AbstractCoverage path planning (CPP) is a subfield of path planning problems in which free areas of a given domain must be visited by a robot at least once while avoiding obstacles. In some situations, the path may be optimized for one or more criteria such as total distance traveled, number of turns, and total area covered by the robot. Accordingly, the CPP problem has been formulated as a multi-objective optimization (MOO) problem, which turns out to be a challenging discrete optimization problem, hence conventional MOO algorithms like Non-dominated Sorting Genetic Algorithm-2 (NSGA-II) do not work as it is. This study implements a modified NSGA-II to solve the MOO problem of CPP for a mobile robot. In this paper, the proposed method adopted two objective functions: (1) the total distance traveled by the robot and (2) the number of turns taken by the robot. The two objective functions are used to calculate energy consumption. The proposed method is compared to the hybrid genetic algorithm (HGA) and the traditional genetic algorithm (TGA) in a rectilinear environment containing obstacles of various complex shapes. In addition, the results of the proposed algorithm are compared to those generated by HGA, TGA, oriented rectilinear decomposition, and spatial cell diffusion and family of spanning tree coverage in existing research papers. The results of all comparisons indicate that the proposed algorithm outperformed the existing algorithms by reducing energy consumption by 5 to 60%. This paper provides the facility to operate the robot in different modes.

Large-Scale Multi-Robot Coverage Path Planning via Local Search

We study graph-based Multi-Robot Coverage Path Planning (MCPP) that aims to compute coverage paths for multiple robots to cover all vertices of a given 2D grid terrain graph G. Existing graph-based MCPP algorithms first compute a tree cover on G---a forest of multiple trees that cover all vertices---and then employ the Spanning Tree Coverage (STC) paradigm to generate coverage paths on the decomposed graph D of the terrain graph G by circumnavigating the edges of the computed trees, aiming to optimize the makespan (i.e., the maximum coverage path cost among all robots). In this paper, we take a different approach by exploring how to systematically search for good coverage paths directly on D. We introduce a new algorithmic framework, called LS-MCPP, which leverages a local search to operate directly on D. We propose a novel standalone paradigm, Extended-STC (ESTC), that extends STC to achieve complete coverage for MCPP on any decomposed graph, even those resulting from incomplete terrain graphs. Furthermore, we demonstrate how to integrate ESTC with three novel types of neighborhood operators into our framework to effectively guide its search process. Our extensive experiments demonstrate the effectiveness of LS-MCPP, consistently improving the initial solution returned by two state-of-the-art baseline algorithms that compute suboptimal tree covers on G, with a notable reduction in makespan by up to 35.7% and 30.3%, respectively. Moreover, LS-MCPP consistently matches or surpasses the results of optimal tree cover computation, achieving these outcomes with orders of magnitude faster runtime, thereby showcasing its significant benefits for large-scale real-world coverage tasks.

Efficient Path Planning Algorithm for Mobile Robots Performing Floor Cleaning Like Operations

In this paper, we introduce an efficient path planning algorithm designed for floor cleaning applications, utilizing the concept of Spanning Tree Coverage (STC). We operate under the assumption that the environment, i.e., the floor, is initially unknown to the robot, which also lacks knowledge regarding obstacle positions, except for the workspace boundaries. The robot executes alternating phases of exploration and coverage, leveraging the local map generated during exploration to construct a STC tree, which then guides the subsequent coverage (cleaning) phase. The extent of exploration is determined by the range of the robot's sensors. The path generation algorithms for cleaning fall within the broader category of coverage path planning (CPP) algorithms. A key advantage of this algorithm is that the robot returns to its initial position upon completing the operation, minimizing battery usage since sensors are only active during the exploration phase. We classify the proposed algorithm as an offline-online scheme. To validate the effectiveness and non-repetitive nature of the algorithm, we conducted simulations using VRep/MATLAB environments and implemented real-time experiments using Turtlebot in the ROS-Gazebo environment. The results substantiate the completeness of coverage and underscore the algorithm's significance in applications akin to floor cleaning.

An Improved Spanning Tree-Based Algorithm for Coverage of Large Areas Using Multi-UAV Systems

In this work, we propose an improved artificially weighted spanning tree coverage (IAWSTC) algorithm for distributed coverage path planning of multiple flying robots. The proposed approach is suitable for environment exploration in cluttered regions, where unexpected obstacles can appear. In addition, we present an online re-planner smoothing algorithm with unexpected detected obstacles. To validate our approach, we performed simulations and real robot experiments. The results showed that our proposed approach produces sub-regions with less redundancy than its previous version.

An Artificially Weighted Spanning Tree Coverage Algorithm for Decentralized Flying Robots

In this article, an artificially weighted spanning tree coverage (AWSTC) algorithm is proposed for the distributed path planning of multiple flying robots. To balance well the efficiency, redundancy, and robustness in the cooperative coverage problem, each robot simultaneously constructs its spanning tree, which grows toward the center of inertia of the uncovered area and keeps away from the trees of its partners. Based on this, each robot covers a concerned area with almost equal trajectory length and computational burden. To guarantee dynamical consistency, a trajectory-smoothing method is developed utilizing Bezier curve transition. As transition in the spanning tree path planning is always conducted around the corner with right angle, geometric and velocity profiles of this kind of transition can be preevaluated as a basic component and then connected to establish more complex real-time trajectories according to the constructed spanning trees. Numerical evaluations and real-time flight experiments are carried out at last. Results demonstrate that the proposed strategy can generate a smooth trajectory for an area coverage problem while ensuring efficiency and robustness. In particular, the efficiency of the proposed algorithm is quite close to the typical centralized spanning tree coverage (STC) algorithm, while ensuring the fulfillment of the coverage task regardless of any breakdown in the individual robot. Note to Practitioners —In applications such as infrastructure inspection and plant protection, the concerned area or terrain is required to be fully covered with the smallest possible time consumption. In view of these real-world requirements, multiple unmanned systems with proper path planning provide a promising solution that can significantly enhance the efficiency as well as the robustness, compared with a single-robot system. Especially, centralized STC algorithms, concerning the computation efficiency and trajectory redundancy of multiple unmanned systems, demonstrate satisfactory effectiveness, and thereby receive a lot of attention. Unfortunately, most of the STC algorithms are proposed in a centralized control framework. Therefore, it is worth to study its decentralized version that could exploit the advantages of the distributed multiple robots. With this consideration, a decentralized AWSTC algorithm is proposed in this article. This spanning tree-based strategy artificially assigns priority weight for each cell, with respect to each individual robot, in the concerned area. With specially designed weight assignment, each robot tends to construct a spanning tree with cells that are uncovered but keep away from the trees of its partners. Based on this, the task burden as well as computational cost of each robot are almost equal. To improve the real-time performance, a Bezier transition trajectory-generation method is also used. With proper parameter selection, robots can generate smooth trajectory with constant velocity in real-time missions. Numerical evaluations and experiments with real-time flight demonstrate the effectiveness of this methodology.

GM-VPC: An Algorithm for Multi-robot Coverage of Known Spaces Using Generalized Voronoi Partition

SUMMARYIn this paper we address the problem of coverage path planning (CPP) for multiple cooperating mobile robots. We use a ‘partition and cover’ approach using Voronoi partition to achieve natural passive cooperation between robots to avoid task duplicity. We combine two generalizations of Voronoi partition, namely geodesic-distance-based Voronoi partition and Manhattan-distance-based Voronoi partition, to address contiguity of partition in the presence of obstacles and to avoid partition-boundary-induced coverage gap. The region is divided into 2D×2Dgrids, whereDis the size of the robot footprint. Individual robots can use any of the single-robot CPP algorithms. We show that with the proposed Geodesic-Manhattan Voronoi-partition-based coverage (GM-VPC), a complete and non-overlapping coverage can be achieved at grid level provided that the underlying single-robot CPP algorithm has similar property.We demonstrated using two representative single-robot coverage strategies, namely Boustrophedon-decomposition-based coverage and Spanning Tree coverage, first based on so-called exact cellular decomposition and second based on approximate cellular decomposition, that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlaps. Simulation experiments using Matlab and V-rep robot simulator and experiments with Fire Bird V mobile robot are carried out to validate the proposed coverage strategy.

Hybrid Spiral STC-Hedge Algebras Model in Knowledge Reasonings for Robot Coverage Path Planning and Its Applications

Robotics is a highly developed field in industry, and there is a large research effort in terms of humanoid robotics, including the development of multi-functional empathetic robots as human companions. An important function of a robot is to find an optimal coverage path planning, with obstacle avoidance in dynamic environments for cleaning and monitoring robotics. This paper proposes a novel approach to enable robotic path planning. The proposed approach combines robot reasoning with knowledge reasoning techniques, hedge algebra, and the Spiral Spanning Tree Coverage (STC) algorithm, for a cleaning and monitoring robot with optimal decisions. This approach is used to apply knowledge inference and hedge algebra with the Spiral STC algorithm to enable autonomous robot control in the optimal coverage path planning, with minimum obstacle avoidance. The results of experiments show that the proposed approach in the optimal robot path planning avoids tangible and intangible obstacles for the monitoring and cleaning robot. Experimental results are compared with current methods under the same conditions. The proposed model using knowledge reasoning techniques in the optimal coverage path performs better than the conventional algorithms in terms of high robot coverage and low repetition rates. Experiments are done with real robots for cleaning in dynamic environments.

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

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.

Multi-robot exploration and terrain coverage in an unknown environment

Terrain exploration and coverage are required for a variety of applications such as mine clearing, intrusion detection and other humanitarian missions like search and rescue operations, for example, fire or blast in a building. During an emergency situation within a building it is crucial to explore the area as fast as possible in order to search and find the wounded people and other hazards. On account of the prevailing breakdown of communication in indoor environments in some situations, it is suggested that the robots can communicate indirectly with the use of markings in the environment. The Spanning Tree Coverage (STC) method, proposed for this problem, suffers in environments that have partially occupied cells and narrow door openings between rooms. In this paper, we consider an extension of the Simultaneous Multiple STC (S-MSTC) algorithm, which we proposed in our previous work on multiple autonomous robots used in exploration and coverage in an unknown terrain. The proposed extended S-MSTC (ES-MSTC) uses ant-type robots to cover the terrain leaving marks on the terrain, which can be sensed by the robots and allow them to cover the terrain, similar to the nature of ants. This algorithm can handle partially occupied cells and narrow door openings in the terrain and performs a complete coverage of the surface regardless of the shape of the environment by constructing multiple spanning trees simultaneously. We present a simulation study and compare the performance of the ES-MSTC algorithm with other existing algorithms.