Rat-UAV Navigation: Cyborg Rat Autonomous Navigation With the Guidance of a UAV

Controlling the cyborg rat with the guidance of an unmanned aerial vehicle (UAV) is important yet challenging. UAV can provide a mobile bird's eye view (BEV) with multiple shooting angles to expand the motion space of the cyborg rat. However, uncertainty is a critical problem arising from the obscure and blurry imaging from UAV and the locomotion willingness of cyborg rats. To solve this problem, we propose the collaborative Rat-UAV navigation (RUN) paradigm. The uncertainty in RUN is formulated using the partially observed Markov decision processes (POMDP) in a perceptual-control framework. First, perceptual uncertainties in rat pose estimation and environmental modeling are reduced. Second, control policies are delivered to maximize rewards and minimize control uncertainties until the cyborg rat reaches the target. To achieve this, RUN can accurately detect tiny rats with fewer parameters, dynamically plan paths with partial observations from the UAV, and stably control the rat using a locomotion willingness transition graph in a large practical arena. Our framework is tested in real-world experiments. For the first time, a cyborg rat successfully completed a navigation task with the guidance of a UAV. Note to Practitioners —This paper studies, for the first time, the problem of navigating a cyborg rat with the guidance of an unmanned aerial vehicle (UAV). Most existing researches on the autonomous navigation of cyborg rats are conducted in fully observed environments with fixed cameras, disregarding uncertainties in complex environmental perception and rat control. In this paper, we propose a collaborative Rat-UAV navigation (RUN) paradigm, which formulates uncertainties using the partially observed Markov decision processes (POMDP). Specifically, RUN enhances the cyborg rat's interaction with the UAV in the navigation environment. First, it narrows the gap between the rat's perceived and actual states by utilizing keypoint detection, dynamic environment modeling, and locomotion willingness estimation from the view of the UAV. Secondly, RUN constructs a policy generation module to maximize the reward of controlling the cyborg rat, based on a locomotion willingness transition graph. Experimental results demonstrate that our framework effectively guides the cyborg rat through a large maze, followed by six targets, with the assistance of the UAV. In future researches, we will investigate the real-time prediction of rat locomotion willingness using multimodal neuroethological recordings, towards a bidirectional brain computer interface between rats and UAVs.