Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

Tan, Xiao; Han, Linhui; Gong, Hao; Wu, Qingwen

doi:10.3390/s23104647

Cited by 9 publications

(4 citation statements)

References 24 publications

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“…In order to mitigate these challenges, several papers have proposed modified QL techniques that are designed to reduce the computational burden by disregarding obstructed areas (Buono, Priandana, Wahjuni, et al, 2023). An alternative avenue involves the integration of neural networks, which leads to the attainment of comprehensive coverage path planning, as it optimizes the path planning strategy in the vicinity of obstacles (Tan, Han, Gong, & Wu, 2023). Moreover, in the realm of wheeled vehicles, such as Ackermann robots, can be combined with Bezier curves to generate smooth trajectories (Zhang, 2023).…”

Section: Background and Related Workmentioning

confidence: 99%

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Batista,

Carvalho,

Oliveira

et al. 2024

Preprint

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Robotics navigation holds significant importance in the transportation, manufacturing, and exploration sectors. The prioritization of path planning is essential for efficient navigation in environments where multiple robots coexist. In the realm of logistic libraries, characterized by repetitive tasks with a need to mitigate human errors, this study concentrates on achieving feasible and optimal path planning for multiple agents tasked with transporting books within a two-floor building. The proposed algorithm, based on Q-learning, incorporates transfer and curriculum learning, enabling cooperative and decentralized behavior among the agents. Numerical experiments conducted through a software-in-the-loop approach validate the effectiveness of the method. The results indicate a success rate of 94% with 9 agents, accompanied by a 73.36% reduction in task completion steps compared to a scenario with a single agent.This research serves to showcase the algorithm's capability in enhancing navigation and task efficiency in multi-agent logistics settings, particularly within smart library environments.

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Section: Background and Related Workmentioning

confidence: 99%

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Batista,

Carvalho,

Oliveira

et al. 2024

Preprint

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show abstract

“…Besides, they work with several dynamic search sub‐teams that collaborate to optimize the solution and obtain the next movement path of each robot. Somehow similar, in (Tan et al, 2023) The global environment information is introduced by Q‐learning reinforcement learning method in a complete coverage path planning biologically inspired neural network algorithm. Besides, the Q‐learning method is used for path planning at the positions where the accessible way points are changed, which optimizes the path planning strategy near these obstacles.…”

Section: Related Workmentioning

confidence: 99%

GBNN algorithm enhanced by movement planner for UV‐C disinfection

Rodrigo,

Sierra‐García,

Santos

2023

Expert Systems

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In order to maintain adequate levels of cleanliness and sanitation in public facilities, prevent the buildup of viruses and other harmful pathogens, and ensure health and safety, health and labor authorities have repeatedly warned of the need to adhere to proper disinfection protocols in the workplace. This is particularly important in public places where food is handled, where there are more vulnerable people, including hospitals and health care centers, or where there is a large concentration of people. One promising approach is the combination of ultraviolet‐C (UV‐C) light and mobile robots to automate disinfection processes. Being this technology effective for disinfection, an excessive dose of UV can damage the materials, limiting its applicability. Therefore, a major challenge for automatic disinfection is to find a route that covers the entire surface, ensures cleanliness, and provides the correct radiation dose while preventing environmental materials from being damaged. To achieve this, in this paper a novel intelligent control approach is proposed. A bio‐inspired Glasius neural network with a motion planner, an UV estimation module, a speed regulator, and pure pursuit controller are combined into one intelligent system. The motion planner proposes a sequence of movements to go through the space in the most efficient way possible, avoiding obstacles of the environment. The speed controller adjusts the dose of UV‐C radiation and the pure pursuit regulator ensures the following of the path. This approach has been tested in various simulation scenarios of increasing complexity and in four different areas of dosing requirements. In simulation, a 44% reduction of the maximum dose is achieved, 17% less distance travelled by the robot and, what is more important, 229% more locations with the appropriate dose.

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“…Li [26] proposed a heuristic approximate credit-based Dubins multi-robot coverage path planning (CDM) algorithm, which utilizes the credit model to balance tasks among robots and a tree partition strategy to reduce complexity. 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*.…”

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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Biologically Inspired Complete Coverage Path Planning Algorithm Based on Q-Learning

Cited by 9 publications

References 24 publications

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

Smart Library: A Multi-Agent Path Planning Logistics Operation using Reinforcement Learning

GBNN algorithm enhanced by movement planner for UV‐C disinfection

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

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