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Subject: path planning

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    Citation - Scopus: 3
    Reciprocal Altruism-Based Path Planning Optimization for Multi-Agents
    (Institute of Electrical and Electronics Engineers Inc., 2022) Maeedi,A.; Khan,M.U.; Irfanoglu,B.
    This paper investigates solutions for the fundamental yet challenging problem of path planning of autonomous multi-agents. The novelty of the proposed algorithm, reciprocal altruism-based particle swarm optimization (RAPSO), lies in the introduction of information sharing among the agents. The RAPSO utilizes kinship relatedness among the agents during the optimization process to reciprocate the significant data. The concept of reciprocation is introduced to ensure that all agents remain in close contact through information exchange. The amount of exchange depends upon their physical location in the search space and their associated health indicator. Agents are classified as donors, recipients, or un-active concerning their health indicator and their positions. The ability of RAPSO to keep all agents closer to local optima through reciprocal altruism is evaluated for path planning optimization problem. Simulation results show that the RAPSO is very competitive when compared with the canonical PSO. The results of the generalized simulation scenario also prove its potential in solving path planning problems in robotics. © 2022 IEEE.
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    Article
    Citation - WoS: 9
    Citation - Scopus: 13
    Escaping Local Minima in Path Planning Using a Robust Bacterial Foraging Algorithm
    (Mdpi, 2020) Abdi, Mohammed Isam Ismael; Khan, Muhammad Umer; Gunes, Ahmet; Mishra, Deepti
    The bacterial foraging optimization (BFO) algorithm successfully searches for an optimal path from start to finish in the presence of obstacles over a flat surface map. However, the algorithm suffers from getting stuck in the local minima whenever non-circular obstacles are encountered. The retrieval from the local minima is crucial, as otherwise, it can cause the failure of the whole task. This research proposes an improved version of BFO called robust bacterial foraging (RBF), which can effectively avoid obstacles, both of circular and non-circular shape, without falling into the local minima. The virtual obstacles are generated in the local minima, causing the robot to retract and regenerate a safe path. The proposed method is easily extendable to multiple robots that can coordinate with each other. The information related to the virtual obstacles is shared with the whole swarm, so that they can escape the same local minima to save time and energy. To test the effectiveness of the proposed algorithm, a comparison is made against the existing BFO algorithm. Through the results, it was witnessed that the proposed approach successfully recovered from the local minima, whereas the BFO got stuck.