Robust visual measurement planning in multi-robot systems

Abstract

This paper develops a non-myopic planning scheme that robustly maximizes the quality of the acquired information in an uncertain multi-camera multi-target vision system. To devise a robust plan, the probabilistic uncertainties associated with the system states are propagated through a non-linear quality metric utilizing the Unscented Transform. The metric considers different contributing factors that affect the quality of the observations for Pan-Tilt-Zoom cameras, such as the resolving ability as a function of distance, occlusion and the observation quality of different sides of the targets. The robust planning algorithm is formulated as a Mixed Integer Second Order Cone Program which employs the propagated statistics of the perception qualities at different time samples. Exploiting the proposed formulation, the trade-off between robustness and performance can be controlled by the confidence value parameter. This adds the capability of reaching suitable compromises to maximize observation quality despite system uncertainties. Extensive simulations confirm the effectiveness of the proposed planning scheme for a typical multi-agent surveillance application.