Adaptive and Risk-Aware Target Tracking with Heterogeneous Robot Teams
This work addresses the challenge of robust target tracking in adversarial environments for robotics teams, though it appears incremental as it builds on existing control and risk-aware methods.
The paper tackles the problem of tracking hostile targets with a heterogeneous robot team while managing sensor failures induced by proximity to targets, proposing a control framework that balances performance and sensor preservation, with simulated experiments demonstrating its efficacy.
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.