A smooth path generation approach for sensor-based coverage path planning (original) (raw)
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On Complete Coverage Path Planning Algorithms for Non-holonomic Mobile Robots: Survey and Challenges
J. Inf. Sci. Eng., 2017
The problem of determining a collision free path within a region is an important area of research in robotics. One significant aspect of this problem is coverage path planning, which is a process to find a path that passes through each reachable position in the desired area. This task is fundamental to many robotic applications such as cleaning, painting, underwater operations, mine sweeping, lawn mowing, agriculture, monitoring, searching, and rescue operations. The total coverage time is significantly influenced by total number of turns, optimization of backtracking sequence, and smoothness in the complete coverage path. There is no comprehensive literature review on backtracking optimization and path smoothing techniques used in complete coverage path planning. Although the problem of coverage path planning has been addressed by many researchers. However, existing state of the art needs to be significantly improved, particularly in terms of accuracy, efficiency, robustness, and o...
IEEE Access, 2021
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. INDEX TERMS Coverage path planning, exploration, heuristic algorithm, deep reinforcement learning.
Pattern-Based Genetic Algorithm Approach to Coverage Path Planning for Mobile Robots
Lecture Notes in Computer Science, 2009
Sensor-based mobile robot coverage path planning (MRCPP) problem is a challenging problem in robotic management. We here develop a genetic algorithm (GA) for MRCPP problems. The area subject to coverage is modeled with disks representing the range of sensing devices. Then the problem is defined as finding a path which runs through the center of each disk at least once with minimal cost of full coverage. The proposed GA utilizes prioritized neighborhood-disk information to generate practical and high-quality paths for the mobile robot. Prioritized movement patterns are designed to generate efficient rectilinear coverage paths with no narrow-angle turn; they enable GA to find optimal or near-optimal solutions. The results of GA are compared with a well-known approach called backtracking spiral algorithm (BSA). Experiments are also carried out using P3-DX mobile robots in the laboratory environment.
Optimizing coverage performance of multiple random path-planning robots
Paladyn, 2012
This paper presents a new approach to the multi-agent coverage path-planning problem. An e cient 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 e cient 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 di cult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with e cient 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 e ectiveness and performance in such an application.
A Dynamic Path Planning Approach for Multirobot Sensor-Based Coverage Considering Energy Constraints
IEEE Transactions on Cybernetics, 2014
In this study, a novel dynamic path planning approach is proposed for multi-robot sensor-based coverage considering energy capacities of the mobile robots. The environment is assumed to be narrow and partially unknown. A Generalized Voronoi diagram-based network is used for the sensor-based coverage planning due to narrow nature of the environment. On the other hand, partially unknown nature is handled with proposed dynamic re-planning approach. Initially, the robots are assumed to be at the same depot with equal initial energy capacities. In this case, an initial complete coverage route is constructed considering robot energy capacities using classical capacitated arc routing problem (CARP) approach with some minor modifications related to coverage problem. But, due to partially unknown nature, the robots may face with blockage on routes, and a fast re-planning is required which considers remaining energy capacities and current positions of the robots. So, new plan is obtained by a modifying Ulusoy's algorithm that was developed for classical CARP. The developed algorithm is coded in C++ and implemented on P3-DX mobile robots in MobileSim simulation environment.
A Hybrid Algorithm for Coverage Path Planning with Imperfect Sensors
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013), 2013
We are interested in the coverage path planning problem with imperfect sensors, within the context of robotics for mine countermeasures. In the studied problem, an autonomous underwater vehicle (AUV) equipped with sonar surveys the bottom of the ocean searching for mines. We use a cellular decomposition to represent the ocean floor by a grid of uniform square cells. The robot scans a fixed number of cells sideways with a varying probability of detection as a function of distance and of seabed type. The goal is to plan a path that achieves the minimal required coverage in each cell while minimizing the total traveled distance and the total number of turns. We propose an off-line hybrid algorithm based on dynamic programming and on a traveling salesman problem reduction. We present experimental results and show that our algorithm’s performance is superior to published results in terms of path quality and computational time, which makes it possible to implement the algorithm in an AUV.
A computationally efficient complete area coverage algorithm for intelligent mobile robot navigation
2014 International Joint Conference on Neural Networks (IJCNN), 2014
Complete area coverage navigation (CAC) requires a special type of robot path planning, where the robots should visit every point of the state workspace. CAC is an essential issue for cleaning robots and many other robotic applications. Real-time complete area coverage path planning is desirable for efficient performance in many applications. In this paper, a novel vertical cell-decomposition (VCD) with convex hull (VCD-CH) approach is proposed for real-time CAC navigation of autonomous mobile robots. In this model, a vertical cell-decomposition (VCD) methodology and a spanningtree based approach with convex hull are effectively integrated to plan a complete area coverage motion for autonomous mobile robot navigation. The computational complexity of this method with minimum trajectory length planned by a cleaning robot in the complete area coverage navigation with rectangle obstacles in the Euclidean space is O(n log n). The performance analysis, computational validation and comparison studies demonstrate that the proposal model is computational efficient, complete and robust.
A solution to vicinity problem of obstacles in complete coverage path planning
Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), 2002
In real world applications there exist arbitrarily shaped obstacles in the workspace during complete coverage path planning of cleaning robots. A cleaning robot should be able to sweep in a variety of corners and in the vicinity of arbitrarily shaped obstacles in an indoor environment. Consequently, the robot is required not only to effectively avoid the obstacles, but also to delicately cover every area in the vicinity of obstacles. In this paper, a solution to vicinity problem of obstacles in complete coverage path planning is proposed using neural-neighborhood analysis. The path planner is a biologically inspired neural network. The proposed model is capable of planning a real-time path to reasonably cover every area in the vicinity of obstacles. The robot path is autonomously generated through the dynamic neural activity landscape of the neural network and the previous robot location. The effectiveness of the proposed approach is verified through computer simulations. 612 0-7803-7272-7/02/$17.00
AGENT-BASED ROUTE PLANNING FOR A MOBILE ROBOT
Constructing architecture and forming optimal paths for mobile robots are some of the heavily studied subjects in mobile robot applications. The aim of this paper is to find a sub-optimum path for a single mobile robot using agent- based client-server architecture in a known environment. The sub-optimum path is determined by a heuristic approach, A-Star algorithm. Client-server architecture is formed based on open agent architecture to control the communication between the robot (client) and the server. In the study robots communicate with the server to receive the planned route for the desired starting and the goal point. Simulations on MobileSim simulator program are conducted to show the effectiveness of the proposed architecture.
Online complete coverage path planning using two-way proximity search
Intelligent Service Robotics, 2017
This paper presents an efficient online approach for complete coverage path planning of mobile robots in an unknown workspace based on online boustrophedon motion and an optimized backtracking mechanism. The presented approach first performs a single continuous boustrophedon motion until a critical point is reached. In order to completely cover the environment, next starting point is decided by using the accumulated knowledge of the environment map. An efficient backtracking technique based on proposed Two-way Proximity Search algorithm is used to plan a path from the critical point to the new starting point. Simulation results show the efficiency of proposed backtracking approach with improved total coverage time, coverage path length and memory requirements.