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

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Phone Thiha Kyaw ◽  
Aung Paing ◽  
Theint Theint Thu ◽  
Rajesh Elara Mohan ◽  
Anh Vu Le ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Travelling Salesman Problem ◽  
Travelling Salesman ◽  
Coverage Path Planning

Coverage path planning for maritime search and rescue using reinforcement learning

2021 ◽  
Vol 241 ◽  
pp. 110098
Author(s):  
Bo Ai ◽  
Maoxin Jia ◽  
Hanwen Xu ◽  
Jiangling Xu ◽  
Zhen Wen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Search And Rescue ◽  
Coverage Path Planning ◽  
Maritime Search And Rescue

scholarly journals A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1994 ◽  
Author(s):  
Guibin Sun ◽  
Rui Zhou ◽  
Bin Di ◽  
Zhuoning Dong ◽  
Yingxun Wang
Keyword(s):  
Path Planning ◽  
Numerical Simulations ◽  
Obstacle Avoidance ◽  
Travelling Salesman Problem ◽  
Region Of Interest ◽  
Minimum Cost ◽  
Closed Path ◽  
Combinatorial Approach ◽  
Travelling Salesman ◽  
Multi Robot

In this paper, a multi-robot persistent coverage of the region of interest is considered, where persistent coverage and cooperative coverage are addressed simultaneously. Previous works have mainly concentrated on the paths that allow for repeated coverage, but ignored the coverage period requirements of each sub-region. In contrast, this paper presents a combinatorial approach for path planning, which aims to cover mission domains with different task periods while guaranteeing both obstacle avoidance and minimizing the number of robots used. The algorithm first deploys the sensors in the region to satisfy coverage requirements with minimum cost. Then it solves the travelling salesman problem to obtain the frame of the closed path. Finally, the approach partitions the closed path into the fewest segments under the coverage period constraints, and it generates the closed route for each robot on the basis of portioned segments of the closed path. Therefore, each robot can circumnavigate one closed route to cover the different task areas completely and persistently. The numerical simulations show that the proposed approach is feasible to implement the cooperative coverage in consideration of obstacles and coverage period constraints, and the number of robots used is also minimized.

Complete coverage path planning using reinforcement learning for Tetromino based cleaning and maintenance robot

2020 ◽  
Vol 112 ◽  
pp. 103078 ◽  
Author(s):  
Anirudh Krishna Lakshmanan ◽  
Rajesh Elara Mohan ◽  
Balakrishnan Ramalingam ◽  
Anh Vu Le ◽  
Prabahar Veerajagadeshwar ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Complete Coverage ◽  
Coverage Path Planning

scholarly journals Multiple Region Coverage Path Planning for Autonomous Underwater Vehicle.

2021 ◽  
Vol 71 (6) ◽  
pp. 784-790
Author(s):  
Shristi Deva Sinha
Keyword(s):  
Path Planning ◽  
Autonomous Underwater Vehicle ◽  
Specific Problem ◽  
Travelling Salesman Problem ◽  
Underwater Vehicle ◽  
Simulation Studies ◽  
Coverage Path Planning ◽  
Instance Optimality ◽  
Benchmark Instance ◽  
Planning Methodology

Coverage path planning methodology for an autonomous underwater vehicle to search multiple non-overlapping regions has been proposed in the paper. The proposed methodology is based on the genetic algorithm (GA). The GA used in the proposed methodology has been tuned for the specific problem, using design of experiment on an equivalent travelling salesman problem benchmark instance. Optimality of the generated paths was analysed through simulation studies. Results indicated that the proposed methodology generated shorter paths in comparison to conventional methods.

scholarly journals Coverage Path Planning Using Reinforcement Learning-Based TSP for hTetran—A Polyabolo-Inspired Self-Reconfigurable Tiling Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2577 ◽  
Author(s):  
Anh Vu Le ◽  
Prabakaran Veerajagadheswar ◽  
Phone Thiha Kyaw ◽  
Mohan Rajesh Elara ◽  
Nguyen Huu Khanh Nhan
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Coverage Problem ◽  
Pareto Optima ◽  
Entire Area ◽  
Coverage Path Planning ◽  
Different Shapes ◽  
Cleaning Robots ◽  
Policy Optimization ◽  
Least Energy

One of the critical challenges in deploying the cleaning robots is the completion of covering the entire area. Current tiling robots for area coverage have fixed forms and are limited to cleaning only certain areas. The reconfigurable system is the creative answer to such an optimal coverage problem. The tiling robot’s goal enables the complete coverage of the entire area by reconfiguring to different shapes according to the area’s needs. In the particular sequencing of navigation, it is essential to have a structure that allows the robot to extend the coverage range while saving energy usage during navigation. This implies that the robot is able to cover larger areas entirely with the least required actions. This paper presents a complete path planning (CPP) for hTetran, a polyabolo tiled robot, based on a TSP-based reinforcement learning optimization. This structure simultaneously produces robot shapes and sequential trajectories whilst maximizing the reward of the trained reinforcement learning (RL) model within the predefined polyabolo-based tileset. To this end, a reinforcement learning-based travel sales problem (TSP) with proximal policy optimization (PPO) algorithm was trained using the complementary learning computation of the TSP sequencing. The reconstructive results of the proposed RL-TSP-based CPP for hTetran were compared in terms of energy and time spent with the conventional tiled hypothetical models that incorporate TSP solved through an evolutionary based ant colony optimization (ACO) approach. The CPP demonstrates an ability to generate an ideal Pareto optima trajectory that enhances the robot’s navigation inside the real environment with the least energy and time spent in the company of conventional techniques.

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