A solution to vicinity problem of obstacles in complete coverage path planning (original) (raw)
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A Neural Network Approach to Complete Coverage Path Planning
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2004
Complete coverage path planning requires the robot path to cover every part of the workspace, which is an essential issue in cleaning robots and many other robotic applications such as vacuum robots, painter robots, land mine detectors, lawn mowers, automated harvesters, and window cleaners. In this paper, a novel neural network approach is proposed for complete coverage path planning with obstacle avoidance of cleaning robots in nonstationary environments. The dynamics of each neuron in the topologically organized neural network is characterized by a shunting equation derived from Hodgkin and Huxley's (1952) membrane equation. There are only local lateral connections among neurons. The robot path is autonomously generated from the dynamic activity landscape of the neural network and the previous robot location. The proposed model algorithm is computationally simple. Simulation results show that the proposed model is capable of planning collision-free complete coverage robot paths.
Many robot applications depend on solving the Complete Coverage Path Problem (CCPP). Specifically, robot vacuum cleaners have seen increased use in recent years, and some models offer room mapping capability using sensors such as LiDAR. With the addition of room mapping, applied robotic cleaning has begun to transition from random walk and heuristic path planning into an environment-aware approach. In this paper, a novel solution for pathfinding and navigation of indoor robot cleaners is proposed. The proposed solution plans a path from a priori cellular decomposition of the work environment. The plannedpath achieves complete coverage on the map and reduces duplicate coverage. The solution is implemented inside the ROS framework, and is validated with Gazebo simulation. Metrics to evaluate the performance of the proposed algorithm seek to evaluate the efficiency by speed, duplicate coverage and distance travelled.
BA*: an online complete coverage algorithm for cleaning robots
Applied Intelligence, 2013
This paper presents a novel approach to solve the online complete coverage task of autonomous cleaning robots in unknown workspaces based on the boustrophedon motions and the A* search algorithm (BA*). In this approach, the robot performs a single boustrophedon motion to cover an unvisited region until it reaches a critical point. To continue covering the next unvisited region, the robot wisely detects backtracking points based on its accumulated knowledge, determines the best backtracking point as the starting point of the next boustrophedon motion, and applies an intelligent backtracking mechanism based on the proposed A* search with smoothed path on tiling so as to reach the starting point with the shortest collision-free path. The robot achieves complete coverage when no backtracking point is detected. Computer simulations and experiments in real workspaces prove that our proposed BA* is efficient for the complete coverage task of cleaning robots.
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.
B-Theta*: an Efficient Online Coverage Algorithm for Autonomous Cleaning Robots
Journal of Intelligent & Robotic Systems, 2017
We propose a novel approach to deal with the online complete-coverage task of cleaning robots in unknown workspaces with arbitrarily-shaped obstacles. Our approach is based on the boustrophedon motions, the boundary-following motions, and the Theta* algorithm known as B-Theta*. Under control of B-Theta*, the robot performs a single boustrophedon motion to cover an unvisited region. While performing the boustrophedon motion, if the robot encounters an obstacle with a boundary that has not yet been covered, it switches to the boundary mode to cover portions along the obstacle boundary, and then continues the boustrophedon motion until it detects an ending point. To move to an unvisited region, the
Path planning and guidance techniques for an autonomous mobile cleaning robot
Robotics and Autonomous Systems, 1995
In the past mobile robot research was often focused to various kinds of point-to-point transportation tasks. Service tasks, such as floor cleaning, require specific approaches for path planning and vehicle guidance in real indoor environments. This article discusses automatic planning of a feasible cleaning path considering a 2D-map as well as kinematic and geometric robot models. Path construction makes use of two typical motion patterns. Each pattern is defined by a sequence of subgoals indicating robot position and orientation. Results of automatic path planning are illustrated by realistic examples of typical robots and cleaning environments. Vehicle guidance includes initialization of robot location, path execution, accurate path tracking and detection of unexpected environmental changes. Path tracking is achieved by subgoal modification during cleaning motion using data from the dead-reckoning and landmark localization systems. If obstacles permanently block the preplanned path, an automatic map update and path replanning is performed. Experimental results with the mobile robot MACROBE confirm the feasibility of the developed planning and guidance system.
Path Planning Algorithm Development for Autonomous Vacuum Cleaner Robots
A vacuum cleaner robot, generally called a robovac, is an autonomous robot that is controlled by intelligent program. Autonomous vacuum cleaning robot will perform task like sweeping and vacuuming in a single pass. The DVR-1 vacuum cleaning robot consists of two DC motor operated wheels that allow 360 degree rotation, a castor wheel, side spinning brushes, a front bumper and a miniature vacuum pump. Sensors in the bumper are used for generating binary information of obstacle detection then they are processed by some controlling algorithms. These algorithms are used for path planning and navigation. The robot's bumper prevents them from bumping into walls and furniture by reversing or changing path accordingly.
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...