This paper compares different distributed control approaches which enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have a limited field of view (FoV) and they are required to explore the environment. The team uses a distributed formulation of the Probability Hypothesis Density (PHD) filter to estimate the number and the position of the targets. The resulting target estimate is used to select the subsequent search locations for each robot. This paper compares Lloyd’s algorithm, a traditional method for distributed search, with two typical stochastic optimization methods: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). This paper presents novel formulations of PSO and SA to solve the multi-target tracking problem, which more effectively trade off between exploration and exploitation. Simulations demonstrate that the use of these stochastic optimization techniques improves coverage of the search space and reduces the error in the target estimates compared to the baseline approach.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Comparing Stochastic Optimization Methods for Multi-robot, Multi-target Tracking


    Additional title:

    Springer Proceedings in Advanced Robotics


    Contributors:

    Conference:

    International Symposium on Distributed Autonomous Robotic Systems ; 2022 ; Montbéliard, France November 28, 2022 - November 30, 2022



    Publication date :

    2024-02-01


    Size :

    16 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    PSTG-based multi-label optimization for multi-target tracking

    Chen, Jiahui / Sheng, Hao / Li, Chao et al. | British Library Online Contents | 2016


    PSTG-based multi-label optimization for multi-target tracking

    Chen, Jiahui / Sheng, Hao / Li, Chao et al. | British Library Online Contents | 2016


    PSTG-based multi-label optimization for multi-target tracking

    Chen, Jiahui / Sheng, Hao / Li, Chao et al. | British Library Online Contents | 2016


    Coordinated Multi-Robot Exploration : Hybrid Stochastic Optimization Approach

    Gul, Faiza / Mir, Suleman / Mir, Imran | AIAA | 2022


    Decentralized Target Tracking based on Multi-Robot Cooperative Triangulation

    Dias, André / Capitan, J. / Merino, L. et al. | BASE | 2015

    Free access