Actively Coupled Sensor Configuration and Planning in Unknown Dynamic Environments

TL;DR

This paper presents a novel sensor configuration and planning method for autonomous vehicles in unknown, dynamic environments, optimizing sensor placement to reduce path uncertainty through active coupling and information maximization.

Contribution

It introduces a new active sensor reconfiguration approach using context-relevant mutual information to improve path planning in dynamic environments.

Findings

The proposed method effectively reduces uncertainty in path cost.

Sensor placement optimization improves vehicle safety and efficiency.

Numerical simulations validate the approach's effectiveness.

Abstract

We address the problem of path-planning for an autonomous mobile vehicle, called the ego vehicle, in an unknown andtime-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. Weaddress 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 informa tion measure that we call context-relevant mutual information (CRMI). Unlike typical sensor placement techniques that maxi mize 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. Although we do not consider communication issues in this paper, such proximity is of relevance for future work that addresses communications between the sensors and the ego vehicle. We illustrate and analyze the proposed technique via numerical simulations.