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.
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Algorithm for searching and tracking an unknown and varying number of mobile targets using a limited FoV sensor
We study the problem of searching and tracking a collection of moving targets using a robot with a limited Field-of-View (FoV) sensor. The actual number of targets present in the environment is not known a priori. We propose a search and tracking framework based on the concept of Bayesian Random Finite Sets (RFSs). Specifically, we generalize the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter which was previously applied for only tracking problems to allow for simultaneous search and tracking. The proposed framework can extract individual target tracks as well as estimate the number and spatial density of the targets. We also show how to use Gaussian Process (GP) regression to extract and predict non-linear target trajectories in this framework. We demonstrate the efficacy of our techniques through representative simulations where we also compare the performance of two active control strategies.
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- Award ID(s):
- 1637915
- NSF-PAR ID:
- 10042903
- Date Published:
- Journal Name:
- Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)
- Page Range / eLocation ID:
- 6246 to 6252
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICRA.2017.7989740
