Coverage Path Planning Algorithm Research Articles

Multi-AUV coverage path planning algorithm using side-scan sonar for maritime search

Coverage search is widely applied in the maritime military and civilian fields. To improve search efficiency and detection accuracy, we propose an effective and rapid multi-autonomous underwater vehicle (AUV) coverage path planning (ER-MCPP) algorithm using side-scan sonar (SSS). First, the given search region was optimized and modeled using grid decomposition. Next, a Voronoi diagram was used to divide and allocate the regions according to the initial position of the AUV. Subsequently, based on the idea of guidance, navigation, and control (GNC), a coverage search algorithm for a single AUV was proposed, including a path planning method combining improved lawn mower and outward-spiral methods, an integrated navigation method based on Kalman filtering, and a path tracking method considering ocean currents. In addition, we proposed a fault-tolerant control method. When an AUV fails unexpectedly, the other AUVs can accomplish a coverage search for the region through cooperation. Simulation experiments were performed using the MATLAB software. The results demonstrate that the proposed algorithm is valid. Compared with other methods, our algorithm has better robustness, finds targets more rapidly, and has a higher search efficiency. The practicality of the proposed algorithm is verified through field tests.

Optimization of coverage path planning algorithm for robots

Abstract. In the era of technology gradually into the home, sweeping robots have become a must for many families. However, due to the lack of algorithms in the robot itself, there are still many problems with sweeping robots. In order to save cost and improve efficiency, it is necessary to improve the algorithm of robot coverage path planning. In this paper, we study the robot to complete full-area coverage path planning using sensors in an unknown environment. In this paper, the wavefront algorithm is combined with the traditional zigzag full-area path planning algorithm. The authors find that the combined algorithm speeds up the sweeping and reduces the repetition rate. Combining several different algorithms together like this can have more different applications in real life.

Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.

Classical versus reinforcement learning algorithms for unmanned aerial vehicle network communication and coverage path planning: A systematic literature review

SummaryThe unmanned aerial vehicle network communication includes all points of interest during the coverage path planning. Coverage path planning in such networks is crucial for many applications, such as surveying, monitoring, and disaster management. Since the coverage path planning belongs to NP‐hard issues, researchers in this domain are constantly looking for optimal solutions for this task. The speed, direction, altitude, environmental variations, and obstacles make coverage path planning more difficult. Researchers have proposed numerous algorithms regarding coverage path planning. In this study, we examined and discussed existing state‐of‐the‐art coverage path planning algorithms. We divided the existing techniques into two core categories: Classical and reinforcement learning. The classical algorithms are further divided into subcategories due to the availability of considerable variations in this category. For each algorithm in both types, we examined the issues of mobility, altitude, and characteristics of known and unknown environments. We also discuss the optimality of different algorithms. At the end of each section, we discuss the existing research gaps and provide future insights to overcome those gaps.

Coverage Path Planning and Point-of-Interest Detection Using Autonomous Drone Swarms.

Unmanned Aerial Vehicles (UAVs) or drones presently are enhanced with miniature sensors that can provide information relative to their environment. As such, they can detect changes in temperature, orientation, altitude, geographical location, electromagnetic fluctuations, lighting conditions, and more. Combining this information properly can help produce advanced environmental awareness; thus, the drone can navigate its environment autonomously. Wireless communications can also aid in the creation of drone swarms that, combined with the proper algorithm, can be coordinated towards area coverage for various missions, such as search and rescue. Coverage Path Planning (CPP) is the field that studies how drones, independently or in swarms, can cover an area of interest efficiently. In the current work, a CPP algorithm is proposed for a swarm of drones to detect points of interest and collect information from them. The algorithm’s effectiveness is evaluated under simulation results. A set of characteristics is defined to describe the coverage radius of each drone, the speed of the swarm, and the coverage path followed by it. The results show that, for larger swarm sizes, the missions require less time while more points of interest can be detected within the area. Two coverage paths are examined here—parallel lines and spiral coverage. The results depict that the parallel lines coverage is more time-efficient since the spiral increases the required time by an average of 5% in all cases for the same number of detected points of interest.

Time-Efficient Coverage Path Planning for Energy-Constrained UAV

Unmanned aerial vehicles (UAVs) have the characteristics of high mobility and wide coverage, making them widely used in coverage, search, and other fields. In these applications, UAV often has limited energy. Therefore, planning a time-efficient coverage path for energy-constrained UAV to cover the area of interest is the core issue. The existing coverage path planning algorithms assume that the UAV moves at a constant speed, without taking into account the cost of turns (including deceleration, turning, and acceleration), which is unrealistic. To solve the above problem, we propose a time-efficient coverage path planning (TECPP) algorithm for the energy-constrained UAV. We build a novel gadget-based graph model, which formalizes the time and energy costs of the flight path including straight flights and making turns (deceleration, turning, and acceleration). Moreover, our graph model is suitable for irregular-shaped areas with multiple obstacles. Finally, we transform the above problem into a generalized traveling salesman problem (GTSP) and use the generalized large neighborhood search (GLNS) solver to solve it. The experimental results show that TECPP outperforms the existing coverage path planning algorithms, and TECPP saves at least 21.6% of time.

Complete coverage path planning algorithm based on energy compensation and obstacle vectorization

A complete coverage path planning (CCPP) algorithm based on energy compensation and obstacle vectorization (ECOV) is proposed. The algorithm can be used in demanding fields such as disinfection robots due to its advantages, such as a low path coverage repetition rate and high coverage rate. The algorithm builds an energy map, classifies various obstacles, and proposes separate special area definitions and corresponding energy reconstruction methods for various types of obstacles. Through the energy compensation of the path and the real-time and non-real-time energy reconstruction of special areas of various obstacles, the robot can adapt to more complicated map models and obtain improved results. The proposed algorithm has strong sensitivity to various complex obstacles. Furthermore, the concept of map parity is proposed. Experimental analysis showed that the algorithm is not sensitive to the map parity of the map model, and the parity will not have a large impact on the algorithm due to the minor changes in the actual environment.

Full coverage path planning algorithm for MRgFUS therapy.

BackgroundHigh‐quality methods for Magnetic Resonance guided Focussed Ultrasound (MRgFUS) therapy planning are needed for safe and efficient clinical practices. Herein, an algorithm for full coverage path planning based on preoperative MR images is presented.MethodsThe software functionalities of an MRgFUS robotic system were enhanced by implementing the developed algorithm. The algorithm's performance in accurate path planning following a Zig‐Zag pathway was assessed on MR images. The planned sonication paths were performed on acrylic films using the robotic system carrying a 2.75 MHz single element transducer.ResultsAblation patterns were successfully planned on MR images and produced on acrylic films by overlapping lesions with excellent match between the planned and experimental lesion shapes.ConclusionsThe advanced software was proven efficient in planning and executing full ablation of any segmented target. The reliability of the algorithm could be enhanced through the development of a fully automated segmentation procedure.

A UAV Coverage Path Planning Algorithm Based on Double Deep Q-Network

The UAV path planning method has practical value in the military field and automated production. Based on deep reinforcement learning theory and the characteristics of coverage path planning, this paper designs and implements a set of deep reinforcement learning frameworks suitable for UAV coverage path planning and trains it in the abstract environment model built. The simulation experiment results show that the designed UAV coverage path planning frameworks can consider obstacles, no-fly zones and length constraints, plan a reasonable path to complete the coverage task, and have a certain generalization ability.

An Improved A-Star Algorithm for Complete Coverage Path Planning of Unmanned Ships

Aiming at the low efficiency and high energy consumption of unmanned ships traversing the entire area, a complete coverage path planning algorithm based on the improved A-star algorithm is proposed. The positioning and vision systems of unmanned ships are used to digitize the actual water information, and the grid method is used to convert the information into an environmental map that can be planned. Compared to the trapezoidal partition of unity method and the short-side reciprocating traversal algorithm in the traversal process, experiments show that path planning is more efficient with the boustrophedon partition of unity method and the long-side reciprocating traversal algorithm. Aiming at the “dead zone”, an improved A-star algorithm is proposed on the basis of the traditional A-star algorithm, that it can shorten about 1/4 path using the proposed algorithm. Simulation shows that the improved A-star algorithm can shorten the traversal path to 40 steps but the traditional A-star algorithm needs 54 steps. Navigation test shows that the proposed algorithm can shorten the traversal path and improve traversal efficiency while ensuring the coverage of unmanned ships.

Coverage Path Planning Methods Focusing on Energy Efficient and Cooperative Strategies for Unmanned Aerial Vehicles.

The coverage path planning (CPP) algorithms aim to cover the total area of interest with minimum overlapping. The goal of the CPP algorithms is to minimize the total covering path and execution time. Significant research has been done in robotics, particularly for multi-unmanned unmanned aerial vehicles (UAVs) cooperation and energy efficiency in CPP problems. This paper presents a review of the early-stage CPP methods in the robotics field. Furthermore, we discuss multi-UAV CPP strategies and focus on energy-saving CPP algorithms. Likewise, we aim to present a comparison between energy efficient CPP algorithms and directions for future research.

Coverage path planning for multiple unmanned aerial vehicles in maritime search and rescue operations

The number of casualties in the maritime sector is increasing consistently. To reduce the scope of search area extensions in the event of a maritime accident, maritime authorities and operation centers are currently trying to develop a quick search for survivors at sea using unmanned aerial vehicles (UAVs) in maritime search and rescue (SAR) operations. Here, we propose a two-phase method for solving the coverage path planning (CPP) problem of multiple-UAV areas in maritime SAR. In phase 1, we propose a grid-based area decomposition method that minimizes the decomposed search area to transform the search area into a graph made up of vertices and edges. In phase 2, we formulate a mixed-integer linear programming (MILP) model to derive an optimal coverage path that minimizes the completion time. To solve the model for large-scale instances, a randomized search heuristic (RSH) algorithm is developed. We conducted extensive numerical experiments to validate the performance of the algorithm. Experimental results show that the RSH yields a better solution with an approximately 0.7% optimality gap within a much shorter computation time than that of a commercial solver. In addition, our grid-based CPP algorithm outperforms those used in previous research with respect to the solution quality. Furthermore, we showed the results of real flight experiments in the marine field using the proposed algorithm.

CPC Algorithm: Exact Area Coverage by a Mobile Robot Using Approximate Cellular Decomposition

SUMMARYA new coverage path planning (CPP) algorithm, namely cell permeability-based coverage (CPC) algorithm, is proposed in this paper. Unlike the most CPP algorithms using approximate cellular decomposition, the proposed algorithm achieves exact coverage with lower coverage overlap compared to that with the existing algorithms. Apart from a formal analysis of the algorithm, the performance of the proposed algorithm is compared with two representative approximate cellular decomposition-based coverage algorithms reported in the literature. Results of demonstrative experiments on a TurtleBot mobile robot within the robot operating system/Gazebo environment and on a Fire Bird V robot are also provided.

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

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.

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.

基于改进人工势场方法的无人机覆盖航路规划算法研究

基于改进人工势场方法的无人机覆盖航路规划算法研究

A new method using knowledge reasoning techniques for improving robot performance in coverage path planning

Robots are a rapidly evolving development field encompassing variable domains ranging from industrial robots to empathetic robots for human companions. Future robots will be highly dependent on the ability to understand, interpret, and generate a representation of the environment in which they are operating, ideally in both a human and machine-readable formalism. An important element in this process lies in Path Planning (PP) with obstacle avoidance in dynamic environments (including cleaning and monitoring in robotics) to identify optimal coverage paths. The study in this paper presents a new approach which combines knowledge reasoning techniques with Breadth First Search to find the optimal path for a cleaning robot in a dynamic environment. This approach is used to apply knowledge inference with conventional coverage PP algorithms to enable robot control and avoid obstacles with optimal coverage PP. The experimental results show that using the proposed approach a robot avoids fixed and mobile obstacles, optimal PP reducing both computational cost and time. When compared to other current approaches, the proposed approach with high-coverage rate and low-repetition rate in coverage performs better than the conventional robot algorithms.

A 3-D Volume Coverage Path Planning Algorithm With Application to Intracerebral Hemorrhage Evacuation

This letter presents a new heuristic 3-D volume coverage path planning (VCPP) algorithm for robotic intracerebral hemorrhage evacuation. In contrast to existing 3-D planning techniques, the proposed algorithm generates 3-D paths without first decomposing the volume into series of 2-D planning problems. It considers the morphology of the volume to be covered and minimizes the configuration or task space distance traveled. The algorithm merges elements from existing grid-based and wavefront approaches and accommodates kinematic and environmental constraints, as well as obstacle avoidance. We provide both simulation and experimental demonstrations of the algorithm in the context of intracerebral hemorrhage evacuation where a curved, needle-like robot must suction out blood from within the brain by covering the interior of a semicoagulated blood-filled volume with its tip. We perform a simulation study with 7 patient datasets and compare the distance traveled with our new algorithm using a conventional 2-D layer-by-layer planning approach. We also perform 3 in vitro evacuation experiments on phantoms made to match patient hemorrhage geometries. Our results illustrate that the VCPP algorithm economizes motion and is more efficient than a layer-by-layer CPP approach.

K-Means Partitioned Space Path Planning (KPSPP) for Autonomous Robotic Harvesting

A three-dimensional coverage path-planning algorithm is proposed for discrete harvesting machines. Although prior research has developed methods for coverage planning in continuous-crop fields, no such algorithm has been developed for discrete crops such as trees. The problem is formulated as a graph traversal problem and solved using graph techniques. Paths to facilitate autonomous operation are generated. A case study is formed around the novel tree-to-tree felling system developed by the University of Canterbury and Scion. This machine is being developed to manoeuvre through New Zealand's plantation forest to fell Pinus radiata trees on steep (≤ 45°) terrain. Algorithm performance is evaluated in 14 commercial plantation forests. Results indicate that a mean coverage of 84.43% was achieved.

Scan matching online cell decomposition for coverage path planning in an unknown environment

This paper presents a novel sensor-based online coverage path-planning algorithm that guarantees the complete coverage of an unknown rectilinear workspace for the task of a mobile robot. The proposed algorithm divides the workspace of the robot into cells at each scan sample. This division can be classified as an exact cell decomposition method, which incrementally constructs cell decomposition while the robot covers an unknown workspace. To guarantee complete coverage, a closed map representation based on a feature extraction that consists of a set of line segments called critical edges is proposed. In this algorithm, cell boundaries are formed by extended critical edges, which are the sensed partial contours of walls and objects in the workspace. The robot uses a laser scanner to sense the critical edges. Sensor measurement is sampled twice in each cell. Scan matching is performed to merge map information between the reference scan and the current scan. At each scan sample, a two-direction oriented rectilinear decomposition is achieved in the workspace and presented by a closed map representation. The construction order of the cells is very important in this incremental cell decomposition algorithm. To choose the next target cell from candidate cells, the robot checks for redundancy in the planned path and for possible positions of the ending points of the current cell. The key point of the algorithm is memorizing the covered space to define the next target cell from possible cells. The path generation within the defined cell is determined to minimize the number of turns, which is the main factor in saving time during the coverage. Therefore, the cell’s long boundary should be chosen as the main path of the robot. This algorithm is verified by an experiment under the LABVIEW environment.