Obstacle-avoidance path planning based on the improved artificial potential field for a 5 degrees of freedom bending robot

Jiang, Quan; Cai, Kaiyong; Xu, Fengyu

doi:10.5194/ms-14-87-2023

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Entering such areas not only jeopardizes the safety of UAV flights but also poses significant risks to urban structures. Classical methods employed in air channel planning under similar circumstances encompass the genetic algorithm [5], artificial potential field algorithm [6], A* algorithm [7], antcolony algorithm [8], and particle swarm algorithm [9]. In recent years, scholars have made noteworthy advancements in this field.…”

Section: Introductionmentioning

confidence: 99%

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

Li,

Shen,

et al. 2023

Aerospace

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With the rapid advancement of unmanned aerial vehicle (UAV) technology, the widespread utilization of UAVs poses significant challenges to urban low-altitude safety and airspace management. In the coming future, the quantity of drones is expected to experience a substantial surge. Effectively regulating the flight behavior of UAVs has become an urgent and imperative issue that needs to be addressed. Hence, this paper proposes a standardized approach to UAV flight through the design of an air channel network. The air channel network comprises numerous single air channels, and this study focuses on investigating the characteristics of a single air channel. To achieve optimal outcomes, the concept of the artificial potential field algorithm is integrated into the deep Q-learning algorithm during the establishment of a single air channel. By improving the action space and reward mechanism, the resulting single air channel enables efficient avoidance of various buildings and obstacles. Finally, the algorithm is assessed through comprehensive simulation experiments, demonstrating its effective fulfillment of the aforementioned requirements.

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Section: Introductionmentioning

confidence: 99%

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

Li,

Shen,

et al. 2023

Aerospace

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“…It adopts an adaptive step, generates a virtual new node strategy to explore the path, and removes redundant path nodes. Jiang et al [17] combine the RRT with the APF for path cutting and optimization. The planned path is short and smooth, and the success rate of planning is improved.…”

Section: Introductionmentioning

confidence: 99%

A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

Yu,

Wu,

et al. 2023

Machines

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In this study, a novel planning and tracking approach is proposed for a mobile robotic arm to grab objects in an obstacle environment. First, we developed an improved APF-RRT* algorithm for the motion planning of a mobile robotic arm. This algorithm optimizes the selection of random tree nodes and smoothing the path. The invalid branch and the planning time are decreased by the artificial potential field, which is determined by the specific characteristics of obstacles. Second, a Fuzzy-DDPG-PID controller is established for the mobile robotic arm to track the planned path. The parameters of the PID controller are set using the new DDPG algorithm, which integrated FNN. The response speed and control accuracy of the controller are enhanced. The error and time of tracking of the mobile robotic arm are decreased. The experiment results verify that the proposed approach has good planning and tracking results, high speed and accuracy, and strong robustness. To avoid the occasionality of the experiments and fully illustrate the effectiveness and generality of the proposed approach, the experiments are repeated multiple times. The experiment results demonstrate the effectiveness of the proposed approach. It outperforms existing planning and tracking approaches.

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Optimization of Artificial Potential Field Using Genetic Algorithm for Human-Aware Navigation of Autonomous Mobile Robots

Sampathkumar,

Chhabra,

Choi

et al. 2023

Lecture Notes in Networks and Systems

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Obstacle-avoidance path planning based on the improved artificial potential field for a 5 degrees of freedom bending robot

Cited by 8 publications

References 23 publications

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

Air Channel Planning Based on Improved Deep Q-Learning and Artificial Potential Fields

A Novel Planning and Tracking Approach for Mobile Robotic Arm in Obstacle Environment

Optimization of Artificial Potential Field Using Genetic Algorithm for Human-Aware Navigation of Autonomous Mobile Robots

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