A Multi-Agent Sensing Framework via Joint Motion Planning and Resource Optimization

Abstract

Multi-agent sensing for transportation systems is receiving widespread attention due to its dynamic flexibility and collaborative capabilities, where the target sensing error is limited by the spatio-temporal error caused by agent localization and formation steps. This paper considers the sensing problem of non-cooperative targets (UAVs or vehicles) by cooperative asynchronous agents (UAVs). This paper develops a framework where the formation of agents and the allocation of resources are jointly optimized. In particular, we reveal the error coupling of measurement and motion noises on target sensing accuracy by Fisher information analysis. Then we propose bandwidth allocation and agent activation strategies in the localization step, which simultaneously improve the position accuracy of agents and the quality of sensing signals. In the formation step, we design motion planning algorithms to increase sensing information about targets. Simulation results demonstrate the significant performance improvements achieved by our proposed algorithms that minimize the effects of localization and control errors on target sensing.