Aerial robot coverage path planning approach with concave obstacles in precision agriculture

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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An Energy Efficient Coverage Path Planning Approach for Mobile Robots

Advances in Intelligent Systems and Computing - Intelligent Computing ◽

10.1007/978-3-030-01177-2_28 ◽

2018 ◽

pp. 387-397

Author(s):

Amna Khan ◽

Iram Noreen ◽

Zulfiqar Habib

Keyword(s):

Path Planning ◽

Mobile Robots ◽

Energy Efficient ◽

Coverage Path Planning ◽

Planning Approach

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Modified A-Star Algorithm for Efficient Coverage Path Planning in Tetris Inspired Self-Reconfigurable Robot with Integrated Laser Sensor

Sensors ◽

10.3390/s18082585 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2585 ◽

Cited By ~ 40

Author(s):

Anh Le ◽

Veerajagadheswar Prabakaran ◽

Vinu Sivanantham ◽

Rajesh Mohan

Keyword(s):

Path Planning ◽

Laser Sensor ◽

Coverage Area ◽

Operation System ◽

Coverage Path Planning ◽

Cleaning Robot ◽

Simulated Environment ◽

Planning Approach ◽

Reconfigurable Robot ◽

Set Up

Advancing an efficient coverage path planning in robots set up for application such as cleaning, painting and mining are becoming more crucial. Such drive in the coverage path planning field proposes numerous techniques over the past few decades. However, the proposed approaches were only applied and tested with a fixed morphological robot in which the coverage performance was significantly degraded in a complex environment. To this end, an A-star based zigzag global planner for a novel self-reconfigurable Tetris inspired cleaning robot (hTetro) presented in this paper. Unlike the traditional A-star algorithm, the presented approach can generate waypoints in order to cover the narrow spaces while assuming appropriate morphology of the hTtero robot with the objective of maximizing the coverage area. We validated the efficiency of the proposed planning approach in the Robot Operation System (ROS) Based simulated environment and tested with the hTetro robot in real-time under the controlled scenarios. Our experiments demonstrate the efficiency of the proposed coverage path planning approach resulting in superior area coverage performance in all considered experimental scenarios.

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