Coverage Path Planning Research Articles

Path Planning for Adaptive CSI Map Construction With A3C in Dynamic Environments

The fingerprint localization based Channel State Information (CSI) plays a vital role given the popularity of Location-Based Service. Since its easy implementation, low device cost and CSI provides fine-grained information which can achieve adequate accuracy. However, the main drawback is that the approach has to construct the fingerprint map manually during the off-line stage, which is tedious and time-consuming. In this paper, we propose a novel data collection strategy for path planning based on reinforcement learning, namely Asynchronous Advantage Actor-Critic (A3C). Given the limited exploration step length, it needs to maximize the informative CSI data for reducing manual cost. We collect a small amount of real data in advance to predict the rewards of all sampling points by multivariate Gaussian process and mutual information. Then the optimization problem is transformed to a sequential decision process, which can exploit the informative path by A3C. We complete the proposed algorithm in two real-world dynamic environments and extensive experiments verify its performance. Compared to coverage path planning, our system not only can achieve similar indoor localization accuracy, but also reduce the CSI collection task.

Design and Coverage Path Planning of a Disinfection Robot

Eliminating pathogen exposure is an important approach to control outbreaks of epidemics such as COVID-19 (coronavirus disease 2019). To deal with pathogenic environments, using disinfection robots is a practicable choice. This research formulates a 3D (three-dimensional) spatial disinfection strategy for a disinfection robot. First, a disinfection robot is designed with an extensible control framework for the integration of additional functions. The robot has eight degrees of freedom that can handle disinfection tasks in complex 3D environments where normal disinfection robots lack the capability to ensure complete disinfection. An ingenious clamping mechanism is designed to increase flexibility and adaptability. Secondly, a new coverage path planning algorithm targeted at the spraying area is used. This algorithm aims to achieve an optimal path via the rotating calipers algorithm after transformation between a 2D (two-dimensional) array and 3D space. Finally, the performance of the designed robot is tested through a series of simulations and experiments in various spaces that humans usually live in. The results demonstrate that the robot can effectively perform disinfection tasks both in computer simulation and in reality.

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.

A metaheuristic approach to optimal morphology in reconfigurable tiling robots

Reconfigurable robots are suitable for cleaning applications due to their high flexibility and ability to change shape according to environmental needs. However, continuous change in morphology is not an energy-efficient approach, with the limited battery capacity. This paper presents a metaheuristic-based framework to identify the optimal morphology of a reconfigurable robot, aiming to maximize the area coverage and minimize the energy consumption in the given map. The proposed approach exploits three different metaheuristic algorithms, namely, SMPSO, NSGA-II, and MACO, to generate the optimal morphology for every unique layout of a two-dimensional grid map by considering the path-length as the energy consumption. The novel feature of our approach is the implementation of the footprint-based Complete Coverage Path Planning (CCPP) adaptable for all possible configurations of reconfigurable robots. We demonstrate the proposed method in simulations and experiments using a Tetris-inspired robot with four blocks named Smorphi, which can reconfigure into an infinite number of configurations by varying its hinge angle. The optimum morphologies were identified for three settings, i.e., 2D indoor map with obstacles and free spaces. The optimum morphology is compared with the standard Tetris shapes in the simulation and the real-world experiment. The results show that the proposed framework efficiently produces non-dominated solutions for choosing the optimal energy-efficient morphologies.

Coverage path planning of unmanned surface vehicle based on improved biological inspired neural network

In order to meet the requirements of coverage path planning task for unmanned surface vehicle, a coverage apth planning algorithm of unmanned surface vehicle based on improved biological inspired neural network is proposed. The template model method and jump point search algorithm are introduced on the basis of biological inspired neural network to solve the problem that the original algorithm cannot completely cover and lock when adjacent to obstacles. To meet the task requirements and enrich the functionality of the algorithm, the island type obstacle template is introduced to make the algorithm give priority to island coverage detection. The problem of obstacle disappearance is solved by enhancing the ability of algorithm to cover specific area first. In the simulation, six marine maps with different complexity are established to verify the effectiveness of the path planning algorithm. Compared with the other coverage path planning algorithms, the simulation experiment proves that the proposed improved biological inspired neural network path planning algorithm improves the efficiency of coverage path planning, shortens the path length and reduces the path repetition rate on the premise of ensuring 100% coverage. Furthermore, the proposed improved biological inspired neural network algorithm achieves the shortest path planning time.

Fields2Cover: An Open-Source Coverage Path Planning Library for Unmanned Agricultural Vehicles

This paper describes Fields2Cover, a novel open source library for coverage path planning (CPP) for agricultural vehicles. While there are several CPP solutions nowadays, there have been limited efforts to unify them into an open source library and provide benchmarking tools to compare their performance. Fields2Cover provides a framework for planning coverage paths, developing novel techniques, and benchmarking state-of-the-art algorithms. The library features a modular and extensible architecture that supports various vehicles and can be used for a variety of applications, including farms. Its core modules are: a headland generator, a swath generator, a route planner and a path planner. An interface to the Robot Operating System (ROS) is also supplied as an add-on. In this paper, the functionalities of the library for planning a coverage path in agriculture are demonstrated using 8 state-of-the-art methods and 7 objective functions in simulation and field experiments.

An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage. Then, for the full-coverage path planning task, this paper proposes a preprocessing method for raster maps, which can effectively delete the blank areas that are impossible to cover in the raster map. In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used to train the complete coverage path planning strategy of the agent. The improvement of the selection of random actions during training is first proposed. Considering the task of complete coverage path planning, this paper replaces random actions with a set of actions toward the nearest uncovered grid. To solve the problem of the slow convergence speed of the deep reinforcement learning network in full-coverage path planning, this paper proposes an improved method of deep reinforcement learning, which superimposes the final output layer with a dangerous actions matrix to reduce the risk of selection of dangerous actions of USVs during the learning process. Finally, the designed method validates via simulation examples.

Path Planning Optimization With Multiple Pesticide and Power Loading Bases Using Several Unmanned Aerial Systems on Segmented Agricultural Fields

We propose a hybrid algorithm based on the nested genetic algorithm (GA) and integer particle swarm optimization (PSO) for multiple unmanned aerial systems (multi-UASs) plant protection operations in multiple segmented fields, with multibases loading pesticides and power. In the proposed algorithm, a preprocessing phase is adopted that includes segmented fields splitting and merging, to shrink the operation field number (i.e., multi-UASs total sorties) and ensure that each generated field area remains smaller than (but close to) the unmanned aerial system (UAS) maximum single-sortie operation area. An improved one-sortie coverage path generation model is established, avoiding power waste in the case when UAS needs to operate on more than one subfield in one sortie. The integer PSO is then used to optimize the initial assignment for UASs on multibases, with the purpose of reducing total operation time and nonoperation flight distance of overall coverage path planning. The proposed algorithm solves the problems of how to design pesticide spraying coverage path on segmented agricultural fields with several bases, and allocate to multiple plant protection UASs with constrained loading capacity. Computer simulations are provided to validate the effectiveness of our algorithms and the performance compared with other algorithms. The test results show that the total number of flight sorties using nested GA (53) is smaller than that using some representative metaheuristic algorithms (57), density-based spatial clustering of applications with noise (60), and traditional planning methods (83). Both nonoperation distance and total operation time obtained by nest GA and integer PSO are smaller than those using other planning methods. The appearance of multibases loading pesticide and power, would reduce the nonspraying flight energy and operation time wastage of UASs.

Exact and Heuristic Multi-Robot Dubins Coverage Path Planning for Known Environments.

Coverage path planning (CPP) of multiple Dubins robots has been extensively applied in aerial monitoring, marine exploration, and search and rescue. Existing multi-robot coverage path planning (MCPP) research use exact or heuristic algorithms to address coverage applications. However, several exact algorithms always provide precise area division rather than coverage paths, and heuristic methods face the challenge of balancing accuracy and complexity. This paper focuses on the Dubins MCPP problem of known environments. Firstly, we present an exact Dubins multi-robot coverage path planning (EDM) algorithm based on mixed linear integer programming (MILP). The EDM algorithm searches the entire solution space to obtain the shortest Dubins coverage path. Secondly, a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm is presented, which utilizes the credit model to balance tasks among robots and a tree partition strategy to reduce complexity. Comparison experiments with other exact and approximate algorithms demonstrate that EDM provides the least coverage time in small scenes, and CDM produces a shorter coverage time and less computation time in large scenes. Feasibility experiments demonstrate the applicability of EDM and CDM to a high-fidelity fixed-wing unmanned aerial vehicle (UAV) model.

Parameter value selection strategy for complete coverage path planning based on the Lü system to perform specific types of missions

Parameter value selection strategy for complete coverage path planning based on the Lü system to perform specific types of missions

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.

Distributed Cooperative Complete Coverage Path Planning in an Unknown Environment Based on a Heuristic Method

This paper proposes a distributed collaborative complete coverage path planning (CCPP) algorithm based on a heuristic method to solve a CCPP problem for multiple agents in an unknown environment. Based on the relationship between path length, energy consumption and number of turns, the algorithm instructs the agents to autonomously plan their respective paths in real time by giving the priority of directions. Simulation experiments show that the proposed CCPP algorithm can guarantee efficient collision-free complete coverage compared with related approaches.

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.

Coverage Path Planning Optimization of Heterogeneous UAVs Group for Precision Agriculture

Precision farming is one of the ways of transition to the intensive methods of agricultural production. The case of application of unmanned aerial vehicles (UAVs) for solving problems of agriculture and animal husbandry is among the actively studied issues. The UAV is capable of solving the tasks of monitoring, fertilizing, herbicides, etc. However, the effective use of UAV requires to solve the tasks of flight planning, taking into account the heterogeneity of the available attachments and the problem solved in the process of the overflight. This research investigates the problem of flight planning of a group of heterogeneous UAVs applied to solving the issues of coverage, which may arise both in the course of monitoring and in the process of the implementation of agrotechnical measures. The method of coverage path planning of heterogenic UAVs group based on a genetic algorithm is proposed; this method provides planning of flight by a group of UAVs using a moving ground platform on which UAVs are recharged and refueled (multi heterogenic UAVs coverage path planning with moving ground platform (mhCPPmp)). This method allows calculating a fly by to solve the task of covering fields of different shapes and permits selecting the optimal subset of UAVs from the available set of devices; it also provides a 10% reduction in the cost of a flyby compared to an algorithm that does not use heterogeneous UAVs or a moving platform.

Path Planning of Randomly Scattering Waypoints for Wafer Probing Based on Deep Attention Mechanism

Wafer probing is a critical process employed to measure the yield of wafer fabrication. The primary object of wafer probing is to find the defect grain on the wafer. After a full coverage check, there are always some suspected grains existing for further inspection. However, this second probing result could be affected by the shape of the probe card and the setting actions (path planning) of operators for grains randomly scattering on the wafer. Good grains can be damaged by reprobe actions, which decrease production performance and customer trust. In general, it also requires manpower to perform reprobing, which dramatically deteriorates the throughput of production. This article has studied this problem, and an adaptive coverage path planning (CPP) method for randomly scattering grains using an attention interface is proposed. The proposed randomly scattering waypoints method uses deep reinforcement learning (DRL) for automatic real-time path planning of the second detection. A soft attention interface accelerates the process with a less overlapped check. The experimental results demonstrate the efficiency of the proposed method in terms of less overlapping and steps, and this method learns a better CPP strategy for wafer probing than programmed paths and other RL-based methods.

Distributed collaborative complete coverage path planning based on hybrid strategy

Distributed collaborative complete coverage path planning based on hybrid strategy

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.

Path Planning of Autonomous 3-D Scanning and Reconstruction for Robotic Multi-Model Perception System

Applying a three-dimensional (3-D) reconstruction from mapping-oriented offline modeling to intelligent agent-oriented environment understanding and real-world environment construction oriented to agent autonomous behavior has important research and application value. Using a scanner to scan objects is a common way to obtain a 3-D model. However, the existing scanning methods rely heavily on manual work, fail to meet efficiency requirements, and are not sufficiently compatible with scanning objects of different sizes. In this article, we propose a creative visual coverage path planning approach for the robotic multi-model perception system (RMMP) in a 3-D environment under photogrammetric constraints. To realize the 3-D scanning of real scenes automatically, we designed a new robotic multi-model perception system. To reduce the influence of image distortion and resolution loss in 3-D reconstruction, we set scanner-to-scene projective geometric constraints. To optimize the scanning efficiency, we proposed a novel path planning method under photogrammetric and kinematics constraints. Under the RMMP system, a constraints-satisfied coverage path could be generated, and the 3-D reconstruction from the images collected along the way was carried out. In this way, the autonomous planning of the pose of the end scanner in scanning tasks was effectively solved. Experimental results show that the RMMP-based 3-D visual coverage method can improve the efficiency and quality in 3-D reconstruction.

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.

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.