Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 36, Issue 13-14, Pages 1540-1553Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364917709507
Keywords
Autonomous agents; path planning for multiple mobile robot systems; sensor fusion; surveillance systems
Categories
Funding
- ONR (MURI grant) [N00014-07-1-0829, N00014-09-1-1051, N00014-09-1-1031]
- ARO [W911NF-13-1-0350]
- NSF [IIS-1426840, IIS-1566247]
- TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program - MARCO
- TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program - DARPA
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Target tracking is a fundamental problem in robotics research and has been the subject of detailed studies over the years. In this paper, we generate a data-driven target model from a real-world dataset of taxi motions. This model includes target motion, appearance, and disappearance from the search area. Using this target model, we introduce a new formulation of the mobile target tracking problem based on the mathematical concept of random finite sets. This formulation allows for tracking an unknown and dynamic number of mobile targets with a team of robots. We show how to employ the probability hypothesis density filter to simultaneously estimate the number of targets and their positions. Next, we present a greedy algorithm for assigning trajectories to the robots to allow them to actively track the targets. We prove that the greedy algorithm is a two-approximation for maximizing submodular tracking objective functions. We examine two such functions: the mutual information between the estimated target positions and future measurements from the robots and a new objective that maximizes the expected number of targets detected by the robot team. We provide extensive simulation evaluations to validate the performance of our data-driven motion model and to compare the behavior and tracking performance of robots using our objective functions.
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