Probabilistic Radio-Visual Active Sensing for Search and Tracking
This work is significant for mobile robotics and search and rescue missions, aiming to improve the efficiency and robustness of target detection and localization.
This paper addresses the problem of actively searching for and tracking a radio-emitting target using an aerial robot equipped with a radio receiver and a camera. It proposes a Recursive Bayesian Estimation scheme that combines visual and radio measurements, demonstrating higher robustness and efficiency compared to visual-only and radio-only baselines in numerical analyses.
Active Search and Tracking for search and rescue missions or collaborative mobile robotics relies on the actuation of a sensing platform to detect and localize a target. In this paper we focus on visually detecting a radio-emitting target with an aerial robot equipped with a radio receiver and a camera. Visual-based tracking provides high accuracy, but the directionality of the sensing domain may require long search times before detecting the target. Conversely, radio signals have larger coverage, but lower tracking accuracy. Thus, we design a Recursive Bayesian Estimation scheme that uses camera observations to refine radio measurements. To regulate the camera pose, we design an optimal controller whose cost function is built upon a probabilistic map. Theoretical results support the proposed algorithm, while numerical analyses show higher robustness and efficiency with respect to visual and radio-only baselines.