Coverage path planning for maritime search and rescue using reinforcement learning

In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

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

IEEE Access ◽

10.1109/access.2020.3045027 ◽

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

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

Sensors ◽

10.3390/s21082577 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2577 ◽

Cited By ~ 1

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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From Coverage Path Planning to parking lot exploration

2020 23rd International Symposium on Measurement and Control in Robotics (ISMCR) ◽

10.1109/ismcr51255.2020.9263739 ◽

2020 ◽

Author(s):

Anna Barbara Adam ◽

Laszlo Kocsany ◽

Emese Gincsaine Szadeczky-Kardoss

Keyword(s):

Path Planning ◽

Coverage Path Planning ◽

Parking Lot

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R-DFS: A Coverage Path Planning Approach Based on Region Optimal Decomposition

Remote Sensing ◽

10.3390/rs13081525 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1525

Author(s):

Gang Tang ◽

Congqiang Tang ◽

Hao Zhou ◽

Christophe Claramunt ◽

Shaoyang Men

Keyword(s):

Path Planning ◽

Google Earth ◽

Simulation Experiments ◽

Minimum Width ◽

Coverage Path Planning ◽

Optimal Paths ◽

Planning Approach ◽

Polygonal Area ◽

Aerial Vehicle ◽

Working Distance

Most Coverage Path Planning (CPP) strategies based on the minimum width of a concave polygonal area are very likely to generate non-optimal paths with many turns. This paper introduces a CPP method based on a Region Optimal Decomposition (ROD) that overcomes this limitation when applied to the path planning of an Unmanned Aerial Vehicle (UAV) in a port environment. The principle of the approach is to first apply a ROD to a Google Earth image of a port and combining the resulting sub-regions by an improved Depth-First-Search (DFS) algorithm. Finally, a genetic algorithm determines the traversal order of all sub-regions. The simulation experiments show that the combination of ROD and improved DFS algorithm can reduce the number of turns by 4.34%, increase the coverage rate by more than 10%, and shorten the non-working distance by about 29.91%. Overall, the whole approach provides a sound solution for the CPP and operations of UAVs in port environments.

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UAV Aided Search and Rescue Operation Using Reinforcement Learning

2020 SoutheastCon ◽

10.1109/southeastcon44009.2020.9368285 ◽

2020 ◽

Cited By ~ 1

Author(s):

Shriyanti Kulkarni ◽

Vedashree Chaphekar ◽

Md Moin Uddin Chowdhury ◽

Fatih Erden ◽

Ismail Guvenc

Keyword(s):

Reinforcement Learning ◽

Search And Rescue ◽

Rescue Operation

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