TMSTC*: A Path Planning Algorithm for Minimizing Turns in Multi-Robot Coverage

Lu, Junjie; Zeng, Bi; Tang, Jingtao; Lam, Tin Lun; Wen, Junbin

doi:10.1109/lra.2023.3293319

Cited by 13 publications

(2 citation statements)

References 22 publications

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“…The second approach is the GBNN algorithm [19], which is not explicitly tailored for 3D terrains but an innovative bioinspired method dealing with complex 2D maps. In contrast, STC is a widely used and sophisticated method [16] that has received much research attention [32,33]. GBNN and STC are both grid-based methods, where GBNN uses a grid map with the same cell edge length as the path shift distance d pl and STC requires a cell edge length as 2d pl .…”

Section: Comparative Studymentioning

confidence: 99%

Terrain-Shape-Adaptive Coverage Path Planning With Traversability Analysis

Qiu,

Zhou,

Hui

et al. 2024

J Intell Robot Syst

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Coverage path planning (CPP) is in great demand with applications in agriculture, mining, manufacturing, etc. Most research in this area focused on 2D CPP problems solving the coverage problem with irregular 2D maps. Comparatively, CPP on uneven terrains is not fully solved. When there are many slopy areas in the working field, it is necessary to adjust the path shape and make it adapt to the 3D terrain surface to save energy consumption. This article proposes a terrain-shape-adaptive CPP method with three significant features. First, the paths grow by themselves according to the local terrain surface shapes. Second, the growth rule utilizes the 3D terrain traversability analysis, which makes them automatically avoid entering hazardous zones. Third, the irregularly distributed paths are connected under an optimal sequence with an improved genetic algorithm. As a result, the method can provide an autonomously growing terrain-adaptive coverage path with high energy efficiency and coverage rate compared to previous research works. It is demonstrated on various maps and is proven to be robust to terrain conditions.

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Section: Comparative Studymentioning

confidence: 99%

Terrain-Shape-Adaptive Coverage Path Planning With Traversability Analysis

Qiu,

Zhou,

Hui

et al. 2024

J Intell Robot Syst

View full text Add to dashboard Cite

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“…Tan [27] proposed a complete coverage path planning algorithm based on Q-learning to solve the problems of local optimal path and high path coverage ratio in the complete coverage path planning of the traditional biologically inspired neural network algorithm. Lu [28] proposed Turn-minimizing Multirobot Spanning Tree Coverage Star (TMSTC*), an improved multirobot coverage path planning (mCPP) algorithm based on MSTC*. Ai [29] planned a search path that would be the least time-consuming and prioritized coverage of high-probability areas based on reinforcement learning, considering complete coverage of maritime SAR areas and avoiding maritime obstacles.…”

Section: Introductionmentioning

confidence: 99%

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Xing,

Wang,

Liu

2024

JMSE

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The path planning strategy of deep-sea mining vehicles is an important factor affecting the efficiency of deep-sea mining missions. However, the current traditional path planning algorithms suffer from hose entanglement problems and small coverage in the path planning of mining vehicle cluster. To improve the security and coverage of deep-sea mining systems, this paper proposes a cluster-coverage path planning strategy based on a traditional algorithm and Deep Q Network (DQN). First, we designed a deep-sea mining environment modeling and map decomposition method. Subsequently, the path planning strategy design is based on traditional algorithms and DQN. Considering the actual needs of deep-sea mining missions, the mining vehicle cluster path planning algorithm is optimized in several aspects, such as loss function, neural network structure, sample selection mechanism, constraints, and reward function. Finally, we conducted simulation experiments and analysis of the algorithm on the simulation platform. The experimental results show that the deep-sea mining cluster path planning strategy proposed in this paper performs better in terms of security, coverage, and coverage rate.

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Multiple unmanned ship coverage and exploration in complex sea areas

Chen,

2024

Auton. Intell. Syst.

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This study addresses the complexities of maritime area information collection, particularly in challenging sea environments, by introducing a multi-agent control model for regional information gathering. Focusing on three key areas—regional coverage, collaborative exploration, and agent obstacle avoidance—we aim to establish a multi-unmanned ship coverage detection system. For regional coverage, a multi-objective optimization model considering effective area coverage and time efficiency is proposed, utilizing a heuristic simulated annealing algorithm for optimal allocation and path planning, achieving a 99.67% effective coverage rate in simulations. Collaborative exploration is tackled through a comprehensive optimization model, solved using an improved greedy strategy, resulting in a 100% static target detection and correct detection index. Agent obstacle avoidance is enhanced by a collision avoidance model and a distributed underlying collision avoidance algorithm, ensuring autonomous obstacle avoidance without communication or scheduling. Simulations confirm zero collaborative failures. This research offers practical solutions for multi-agent exploration and coverage in unknown sea areas, balancing workload and time efficiency while considering ship dynamics constraints.

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TMSTC*: A Path Planning Algorithm for Minimizing Turns in Multi-Robot Coverage

Cited by 13 publications

References 22 publications

Terrain-Shape-Adaptive Coverage Path Planning With Traversability Analysis

Terrain-Shape-Adaptive Coverage Path Planning With Traversability Analysis

The Wide-Area Coverage Path Planning Strategy for Deep-Sea Mining Vehicle Cluster Based on Deep Reinforcement Learning

Multiple unmanned ship coverage and exploration in complex sea areas

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