Local Path Planning of Mobile Robot Based on Artificial Potential Field

Wang, Di; Li, Caihong; Guo, Na; Song, Yong; Gao, Tengteng; Liu, Guoming

doi:10.23919/ccc50068.2020.9189250

Cited by 16 publications

(14 citation statements)

References 4 publications

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“…In order to solve the problems of timing conflict and pulse overlap, VFFA is introduced. The basic idea of VFFA [40][41][42][43][44][45][46][47] is to treat a false target set in space as a node or particle that can also be regarded as a virtual point charge in space. The repulsive forces from other virtual charges will act among nodes.…”

Section: Principles Of the Analysis Of Vffamentioning

confidence: 99%

Design of detection‐jamming shared waveform based on virtual force field algorithm

Xiong

Liu

Chen

2022

Engineering Reports

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Due to the technical barriers between radars and jammers and the poor performance of the traditional detection‐jamming shared signal in integrated radar‐electronic warfare systems, a new detection‐jamming shared signal waveform based on the virtual force field algorithm (VFFA) is proposed in this article. First, a multiobjective and multidimensional characteristic parameter optimization model, based on a virtual force field, is established, and then the design principle of the shared signal is presented in detail. The simulation results show that the detection‐jamming shared signal based on the VFFA presents the deceptive jamming of multiple false targets in a noncollaborative target's radar screen. Further, there is better detection performance with the advantages of multiple pulse repetition frequency (PRF) and pulse accumulation numbers, which are highly sensitive to the multijagged PRF signals emitted by the noncollaborative target. According to the VFFA described in this article, the optimum detection‐jamming shared signal waveform can be output in real time for specific free space targets, to improve the efficiency of integrated radar and electronic warfare systems.

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Section: Principles Of the Analysis Of Vffamentioning

confidence: 99%

Design of detection‐jamming shared waveform based on virtual force field algorithm

Xiong

Liu

Chen

2022

Engineering Reports

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“…Local motion planning belongs to dynamic planning and focuses on considering the local environment information of the robot, which enables the robot to have good obstacle avoidance ability [38] . Robots need to be robust enough to environmental errors and noise, and they can provide realtime feedback and correction for planning results through efficient information processing capability [39] .The algorithms used in local motion planning mainly include artificial potential field method [40] , simulated annealing method [41] , fuzzy logic method [42] , neural network method(NN) [43] , dynamic window Approach(DWA) [44] , etc.…”

Section: Figure1 Schematic Diagram Of Mobile Robot Motion Planningmentioning

confidence: 99%

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

Sun

Zhang

et al. 2021

IEEE Access

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Mobile robots contributed significantly to the intelligent development of human society, and the motion-planning policy is critical for mobile robots. This paper reviews the methods based on motionplanning policy, especially the ones involving Deep Reinforcement Learning (DRL) in the unstructured environment. The conventional methods of DRL are categorized to value-based, policy-based and actorcritic-based algorithms, and the corresponding theories and applications are surveyed. Furthermore, the recently-emerged methods of DRL are also surveyed, especially the ones involving the imitation learning, meta-learning and multi-robot systems. According to the surveys, the potential research directions of motion-planning algorithms serving for mobile robots are enlightened.

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“…But in the face of semiclosed obstacles, the robot cannot escape the local oscillation. Di et al (2020) and Lee et al (2017) set a virtual target point to make the robot escape from the local oscillation point by setting a virtual target point to provide gravity when the robot falls into local oscillation. Rostami et al (2019) added adjustment factors to the traditional APF function to bypass obstacles to overcome local minima and target unreachable problems.…”

Section: Introductionmentioning

confidence: 99%

An obstacle avoidance strategy for complex obstacles based on artificial potential field method

Zhang

Song

et al. 2023

Journal of Field Robotics

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When there are obstacles around the target point, the mobile robot cannot reach the target using the traditional artificial potential field (APF). Besides, the traditional APF is prone to local oscillation in complex terrain such as three‐point collinear or semiclosed obstacles. Aiming at solving the defects of traditional APF, a novel improved APF algorithm named back virtual obstacle setting strategy‐APF has been proposed in this paper. There are two main advantages of the proposed method. First, by redefining the gravitational function as a logarithmic function, the proposed method can make the mobile robot reach the target point when there are obstacles around the target. Second, the proposed method can avoid falling into local oscillation for both three‐point collinear and semiclosed obstacles. Compare with APF and other improved APF, the feasibility of the algorithm is proved through software simulation and practical application.

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Local Path Planning of Mobile Robot Based on Artificial Potential Field

Cited by 16 publications

References 4 publications

Design of detection‐jamming shared waveform based on virtual force field algorithm

Design of detection‐jamming shared waveform based on virtual force field algorithm

Motion Planning for Mobile Robots—Focusing on Deep Reinforcement Learning: A Systematic Review

An obstacle avoidance strategy for complex obstacles based on artificial potential field method

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