Coverage Path Planning Problem Research Articles

Clustering-Based Multi-Region Coverage-Path Planning of Heterogeneous UAVs

Unmanned aerial vehicles (UAVs) multi-area coverage-path planning has a broad range of applications in agricultural mapping and military reconnaissance. Compared to homogeneous UAVs, heterogeneous UAVs have higher application value due to their superior flexibility and efficiency. Nevertheless, variations in performance parameters among heterogeneous UAVs can significantly amplify computational complexity, posing challenges to solving the multi-region coverage path-planning problem. Consequently, this study studies a clustering-based method to tackle the multi-region coverage path-planning problem of heterogeneous UAVs. First, the constraints necessary during the planning process are analyzed, and a planning formula based on an integer linear programming model is established. Subsequently, this problem is decomposed into regional allocation and visiting order optimization subproblems. This study proposes a novel clustering algorithm that utilizes centroid iteration and spatiotemporal similarity to allocate regions and adopts the nearest-to-end policy to optimize the visiting order. Additionally, a distance-based bilateral shortest-selection strategy is proposed to generate region-scanning trajectories, which serve as trajectory references for real flight. Simulation results in this study prove the effective performance of the proposed clustering algorithm and region-scanning strategy.

Improved BINN-Based Underwater Topography Scanning Coverage Path Planning for AUV in Internet of Underwater Things

Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with Autonomous Underwater Vehicle (AUV) has been becoming the chief methodology of knowing Seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full Coverage Path Planning (CPP) problem. In this paper, facing the complete coverage path planning problem of mobile AUV, a new strategy based on Bio-Inspired Neural Network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A* path planning based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved Bio-Inspired Neural Network (IBINN in short) based algorithm.

Joint Deployment and Coverage Path Planning for Capsule Airports with Multiple Drones

Due to the advantages of low cost and high flexibility, drones have been applied to urban surveillance, vegetation monitoring, and other fields with the need for coverage of regions. To expand UAVs’ coverage, we designed the Capsule Airport (CA) to recharge and restore drones and provide take-off and landing services. Meanwhile, the combination of drones’ coverage path planning (CPP) and the deployment of CAs is a crucial problem with few relevant studies. We propose a solution approach to the CPP problem based on selecting scanning patterns and trapezoidal decomposition. In addition, we construct a 0–1 integer programming model to minimize the cost of the distance between CAs and the scanning missions. Specifically, a solution approach based on greedy and clustering heuristics is designed to solve this problem. Furthermore, we then develop a local-search-based algorithm with the operators of CA location exchange and drone scanning mission exchange to further optimize the solution. Random instances at different sizes are used to validate the performance of proposed algorithms, through which the sensitivity analysis is conducted with some factors. Finally, a case study based on the Maolichong forest park in Changsha, China, is presented to illustrate the application of the proposed method.

Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking

Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment.

Fast and optimal branch-and-bound planner for the grid-based coverage path planning problem based on an admissible heuristic function.

This paper introduces an optimal algorithm for solving the discrete grid-based coverage path planning (CPP) problem. This problem consists in finding a path that covers a given region completely. First, we propose a CPP-solving baseline algorithm based on the iterative deepening depth-first search (ID-DFS) approach. Then, we introduce two branch-and-bound strategies (Loop detection and an Admissible heuristic function) to improve the results of our baseline algorithm. We evaluate the performance of our planner using six types of benchmark grids considered in this study: Coast-like, Random links, Random walk, Simple-shapes, Labyrinth and Wide-Labyrinth grids. We are first to consider these types of grids in the context of CPP. All of them find their practical applications in real-world CPP problems from a variety of fields. The obtained results suggest that the proposed branch-and-bound algorithm solves the problem optimally (i.e., the exact solution is found in each case) orders of magnitude faster than an exhaustive search CPP planner. To the best of our knowledge, no general CPP-solving exact algorithms, apart from an exhaustive search planner, have been proposed in the literature.

Energy-Efficient UAVs Coverage Path Planning Approach

Unmanned aerial vehicles (UAVs), commonly known as drones, have drawn significant consideration thanks to their agility, mobility, and flexibility features. They play a crucial role in modern reconnaissance, inspection, intelligence, and surveillance missions. Coverage path planning (CPP) which is one of the crucial aspects that determines an intelligent system’s quality seeks an optimal trajectory to fully cover the region of interest (ROI). However, the flight time of the UAV is limited due to a battery limitation and may not cover the whole region, especially in large region. Therefore, energy consumption is one of the most challenging issues that need to be optimized. In this paper, we propose an energy-efficient coverage path planning algorithm to solve the CPP problem. The objective is to generate a collision-free coverage path that minimizes the overall energy consumption and guarantees covering the whole region. To do so, the flight path is optimized and the number of turns is reduced to minimize the energy consumption. The proposed approach first decomposes the ROI into a set of cells depending on a UAV camera footprint. Then, the coverage path planning problem is formulated, where the exact solution is determined using the CPLEX solver. For small-scale problems, the CPLEX shows a better solution in a reasonable time. However, the CPLEX solver fails to generate the solution within a reasonable time for large-scale problems. Thus, to solve the model for large-scale problems, simulated annealing for CPP is developed. The results show that heuristic approaches yield a better solution for large-scale problems within a much shorter execution time than the CPLEX solver. Finally, we compare the simulated annealing against the greedy algorithm. The results show that simulated annealing outperforms the greedy algorithm in generating better solution quality.

A Multi-Robot Coverage Path Planning Method for Maritime Search and Rescue Using Multiple AUVs

In this study, we focus on the Multi-robot Coverage Path Planning (MCPP) problem for maritime Search And Rescue (SAR) missions using a multiple Autonomous Underwater Vehicle (AUV) system, with the ultimate purpose of efficiently and accurately discovering the target from sonar images taken by Side-Scan Sonar (SSS) mounted on the AUVs. Considering the specificities of real maritime SAR projects, we propose a novel MCPP method, in which the MCPP problem is transformed into two sub-problems: Area partitioning and single-AUV coverage path planning. The structure of the task area is first defined using Morse decomposition of the spike pattern. The area partitioning problem is then formulated as an AUV ordering problem, which is solved by developing a customized backtracking method to balance the workload and to avoid segmentation of the possible target area. As for the single-AUV coverage path planning problem, the SAR-A* method is adopted, which generates a path that preferentially visits the possible target areas and reduces the number of turns to guarantee the high quality of the resulting sonar images. Simulation results demonstrate that the proposed method can maintain the workload balance and significantly improve the efficiency and accuracy of discovering the target. Moreover, our experimental results indicate that the proposed method is practical and the mentioned specificities are useful for discovering targets.

Optimal Path Planning for Intelligent UAVs Using Graph Convolution Networks

Unmanned Aerial Vehicles (UAVs) are in use for surveillance services in the geographic areas, that are very hard and sometimes not reachable by humans. Nowadays, UAVs are being used as substitutions to manned operations in various applications. The intensive utilization of autonomous UAVs has given rise to many new challenges. One of the vital problems that arise while deploying UAVs in surveillance applications is the Coverage Path Planning(CPP) problem. Given a geographic area, the problem is to find an optimal path/tour for the UAV such that it covers the entire area of interest with minimal tour length. A graph can be constructed from the map of the area under surveillance, using computational geometric techniques. In this work, the Coverage Path Planning problem is posed as a Travelling Salesperson Problem(TSP) on these graphs. The graphs obtained are large in number of vertices and edges and the real-time applications require good computation speed. Hence a model is built using Graph Convolution Network (GCN). The model is effectively trained with different problem instances such as TSP20, TSP50, and TSP100. Results obtained from the Concorde Benchmark Dataset were used to analyze the optimality of the predicted tour length by the GCN. The model is also evaluated against the performance of evolutionary algorithms on several self-constructed graphs. Particle Swarm Optimization, Ant Colony Optimization, and Firefly Algorithm are used to find optimal tours and are compared with GCN. It is found that the proposed GCN framework outperforms these evolutionary algorithms in optimal tour length and also the computation time.

Near-Optimal Area-Coverage Path Planning of Energy-Constrained Aerial Robots With Application in Autonomous Environmental Monitoring

This article describes a Voronoi-based path generation (VPG) algorithm for an energy-constrained mobile robot, such as an unmanned aerial vehicle (UAV). The algorithm solves a variation of the coverage path-planning problem where complete coverage of an area is not possible due to path-length limits caused by energy constraints on the robot. The algorithm works by modeling the path as a connected network of mass-spring-damper systems. The approach further leverages the properties of Voronoi diagrams to generate a potential field to move path waypoints to near-optimal configurations while maintaining path-length constraints. Simulation and physical experiments on an aerial vehicle are described. Simulated runtimes show linear-time complexity with respect to the number of path waypoints. Tests in variously shaped areas demonstrate that the method can generate paths in both convex and nonconvex areas. Comparison tests with other path generation methods demonstrate that the VPG algorithm strikes a good balance between runtime and optimality, with significantly better runtime than direct optimization, lower cost coverage paths than a lawnmower-style coverage path, and moderately better performance in both metrics than the most conceptually similar method. Physical experiments demonstrate the applicability of the VPG method to a physical UAV, and comparisons between real-world results and simulations show that the costs of the generated paths are within a few percent of each other, implying that analysis performed in simulation will hold for real-world application, assuming that the robot is capable of closely following the path and a good energy model is available. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —For autonomous mobile-robotics-based applications where a robot equipped with a tool or sensor is required to survey an area for inspection, monitoring, cleaning, and so on, effectively covering the area is desirable. However, for energy-constrained systems such as aerial vehicles with limited flight time, complete coverage is not possible. Presented here is a new Voronoi-based path generation algorithm that takes energy constraints into account to generate waypoints for the robot to follow in a near-optimal configuration while maintaining path-length constraints. The approach is applied in simulation and experiments for an application in environmental monitoring using unmanned aerial vehicles.

Coverage Path Planning for Decomposition Reconfigurable Grid-Maps Using Deep Reinforcement Learning Based Travelling Salesman Problem

Optimizing the coverage path planning (CPP) in robotics has become essential to accomplish efficient coverage applications. This work presents a novel approach to solve the CPP problem in large complex environments based on the Travelling Salesman Problem (TSP) and Deep Reinforcement Learning (DRL) leveraging the grid-based maps. The proposed algorithm applies the cellular decomposition methods to decompose the environment and generate the coverage path by recursively solving each decomposed cell formulated as TSP. A solution to TSP is determined by training Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) layers using Reinforcement Learning (RL). We validated the proposed method by systematically benchmarked with other conventional methods in terms of path length, execution time, and overlapping rate under four different map layouts with various obstacle density. The results depict that the proposed method outperforms all considered parameters than the conventional schemes. Moreover, simulation experiments demonstrate that the proposed approach is scalable to the larger grid-maps and guarantees complete coverage with efficiently generated coverage paths.

Optimization Based Multi-View Coverage Path Planning for Autonomous Structure From Motion Recordings

The area of autonomous mapping and autonomous maintenance using unmanned robots has received much attention in recent years. However, usually only single-view coverage is ensured, which is not sufficient if a camera-based three-dimensional reconstruction is desired. If high precision is required for this reconstruction, computations are expensive and not real-time feasible, such that recording and reconstruction procedure must be executed consecutively. This calls for a reliable method for collecting a sufficient set of images during the recording procedure. In this letter, we present the first approach for multi-view coverage path planning, specifically targeted at an autonomous structure from motion data acquisition. For that purpose, we introduce an incremental, computational efficient update formula, that gives an upper bound on the expected reconstruction quality of observed points on the boundary of a structure. Using this quality estimate, we iteratively solve the coverage path-planning problem by greedily selecting a next-best-view as a solution to an optimal experimental design problem. Additional geometric and dynamic constraints ensure the admissibility of the solution. The correctness of our quality estimate and coverage path-planning approach are validated in the physics simulator Gazebo.

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.

Energy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation

This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.

An Integrated Traveling Salesman and Coverage Path Planning Problem for Unmanned Aircraft Systems

Unmanned aircraft systems (UASs) have gained great popularity in land monitoring, 3-D mapping, search and rescue, among others. Existing studies on UAS path planning in these missions consider only a single region to be examined. However, it is frequently encountered that multiple regions need to be considered while performing a real life mission. How to design the optimal path for the UAS to cover multiple regions is then critical. From a strategical point of view, such a problem can be considered as a variant of the traveling salesman problem (TSP) combined and enhanced with the coverage path planning (CPP) problem. Although TSP and CPP have been studied extensively, its combination, which here is given the name TSP-CPP, hasn’t received any attention. In this letter, a preliminary study on how to formulate and solve this problem is conducted. Two novel approaches including a grid-based approach and a dynamic programming based approach are introduced to find the (near) optimal solution. Both numerical analysis and simulation studies are conducted to prove and illustrate the optimality and efficiency of the proposed TSP-CPP approaches.

A survey on coverage path planning for robotics

Coverage Path Planning (CPP) is the task of determining a path that passes over all points of an area or volume of interest while avoiding obstacles. This task is integral to many robotic applications, such as vacuum cleaning robots, painter robots, autonomous underwater vehicles creating image mosaics, demining robots, lawn mowers, automated harvesters, window cleaners and inspection of complex structures, just to name a few. A considerable body of research has addressed the CPP problem. However, no updated surveys on CPP reflecting recent advances in the field have been presented in the past ten years. In this paper, we present a review of the most successful CPP methods, focusing on the achievements made in the past decade. Furthermore, we discuss reported field applications of the described CPP methods. This work aims to become a starting point for researchers who are initiating their endeavors in CPP. Likewise, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works.

Optimizing Coverage Performance of Multiple Random Path-planning Robots

Abstract This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

Coverage path planning for UAVs based on enhanced exact cellular decomposition method

In this paper, an enhanced exact cellular decomposition method to plan the coverage path of UAVs in a polygon area is proposed. To be more specific, the contributions of the paper are: firstly, the turning motion of UAVs is shown to be less efficient from the viewpoints of route length, duration and energy. Secondly, the problem of coverage Path Planning (CPP) in a convex polygon area is transformed to width calculation of the convex polygon, and a novel algorithm to calculate the widths of convex polygons with time complexity of O( n) is developed. The path of the least number of turns for an UAV based on the widths of convex polygons is devised. Thirdly, a convex decomposition algorithm for minimum width sum based on the greedy recursive method which revolves around decomposing the concave area into convex subregions is developed. It is proved that the algorithm is a polynomial time algorithm. To avoid unnecessary back and forth motion, some entirely adjacent subregions are combined. Finally, comparing different weights of two joint-points, a subregion connection algorithm based on minimum traversal of weighted undirected graph is proposed to connect the coverage paths of the subregions. Simulation results show that the proposed method is feasible and effective.