An Adaptive Consensus Based Distributed Particle Filter For Cooperative Object Tracking

Keywords

consensus; cooperative object tracking; distributed particle filter; Renyi entropy

Abstract

Object tracking is a fundamental skill of mobile robotics. For a multi-robot system the object tracking accuracy of each robot is usually different because of the dynamic environment and the different sensing ability. The focus of cooperative object tracking is how to fuse different robots' observation to obtain more robust and accurate estimation of the object. This paper investigates this problem using an adaptive consensus based distributed particle filter algorithm. In this algorithm, each robot runs a particle filter to achieve local object tracking. Then a consensus algorithm which can establish an agreement between all robots is utilized to generalize a global posterior about the object state. To fully consider the difference between each robot's object tracking accuracy, Renyi entropy is used to adaptively adjust the weight of the consensus algorithm. The concept of Renyi entropy is utilized to measure the distance between a robot's local posterior and the global posterior and then a weight will be assigned to the robot according to the Renyi entropy. This weight will be used continuously in the next consensus step. By the proposed algorithm in this paper the accuracy and the robust of the cooperative can be improved. Finally the effectiveness of the proposed algorithm is verified by simulation results.

Publication Date

9-7-2017

Publication Title

Chinese Control Conference, CCC

Number of Pages

6169-6174

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.23919/ChiCC.2017.8028338

Socpus ID

85032172088 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85032172088

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