Planning for perception and perceiving for decision: POMDP-like online target detection and recognition for autonomous UAVs

This paper studies the use of POMDP-like techniques to tackle an online multi-target detection and recognition mission by an autonomous rotorcraft UAV. Such robotics missions are complex and too large to be solved off-line, and acquiring information about the environment is as important as achieving some symbolic goals. The POMDP model deals in a single framework with both perception actions (controlling the camera's view angle), and mission actions (moving between zones and flight levels, landing) needed to achieve the goal of the mission, i.e. landing in a zone containing a car whose model is recognized as a desired target model with sufficient belief. We explain how we automatically learned the probabilistic observation POMDP model from statistical analysis of the image processing algorithm used on-board the UAV to analyze objects in the scene. We also present our "optimize-while-execute" framework, which drives a POMDP sub-planner to optimize and execute the POMDP policy in parallel under action duration constraints, reasoning about the future possible execution states of the robotic system. Finally, we present experimental results, which demonstrate that Artificial Intelligence techniques like POMDP planning can be successfully applied in order to automatically control perception and mission actions hand-in-hand for complex time-constrained UAV missions