2021/05/09 by Siddharth Mayya, Ragesh K. Ramachandran, Mayya, Siddharth +7
Computer Science · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Reinforcement Learning in Robotics #Robotics (cs.RO) #Target Tracking and Data Fusion in Sensor Networks
paper · pdf · doi:10.48550/arxiv.2105.03813
openalex publication_date 2021/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider a scenario where a team of robots with heterogeneous sensors must track a set of hostile targets which induce sensory failures on the robots. In particular, the likelihood of failures depends on the proximity between the targets and the robots. We propose a control framework that implicitly addresses the competing objectives of performance maximization and sensor preservation (which impacts the future performance of the team). Our framework consists of a predictive component -- which accounts for the risk of being detected by the target, and a reactive component -- which maximizes the performance of the team regardless of the failures that have already occurred. Based on a measure of the abundance of sensors in the team, our framework can generate aggressive and risk-averse robot configurations to track the targets. Crucially, the heterogeneous sensing capabilities of the robots are explicitly considered in each step, allowing for a more expressive risk-performance trade-off. Simulated experiments with induced sensor failures demonstrate the efficacy of the proposed approach.