Actively Coupled Sensor Configuration and Planning in Unknown Dynamic Environments

We address the problem of path-planning for an autonomous mobile vehicle, called the ego vehicle, in an unknown and time-varying environment. The objective is for the ego vehicle to minimize exposure to a spatiotemporally-varying unknown scalar field called the threat field. Noisy measurements of the threat field are provided by a network of mobile sensors. We address the problem of optimally configuring (placing) these sensors in the environment. To this end, we propose sensor reconfiguration by maximizing a reward function composed of three different elements. First, the reward includes an information measure that we call context-relevant mutual information (CRMI). Unlike typical sensor placement techniques that maximize mutual information of the measurements and environment state, CRMI directly quantifies uncertainty reduction in the ego path cost while it moves in the environment. Therefore, the CRMI introduces active coupling between the ego vehicle and the sensor network. Second, the reward includes a penalty on the distances traveled by the sensors. Third, the reward includes a measure of proximity of the sensors to the ego vehicle. We illustrate and analyze the proposed technique via numerical simulations.