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Title: Comparing Stochastic Optimization Methods for Multi-Robot, Multi-Target Tracking
This paper compares different distributed control approaches that enable a team of robots search for and track an unknown number of targets. The robots are equipped with sensors which have limited field of view (FoV) and 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 future 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). PSO and SA are traditionally used to find a single global maximum, therefore this paper describes novel formulations of PSO and SA to solve the problem of multi-target tracking. These new methods 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.  more » « less
Award ID(s):
2143312
NSF-PAR ID:
10416740
Author(s) / Creator(s):
;
Date Published:
Journal Name:
International Symposium on Distributed and Autonomous Systems
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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