Continuous Robotic Tracking of Dynamic Targets in Complex Environments Based on Detectability

Target tracking is a fundamental task in the domain of robotics. The effectiveness of target tracking hinges upon various factors, such as tracking distance, occlusions, collision avoidance, etc. However, few existing works can simultaneously tackle these considerations of tracking single and multiple targets in complex environments. In this study, the interaction mechanism of target tracking between the robot, the environment and the targets is analyzed, and a general measure named detectability is introduced to correlate the tracking performance for guiding robotic motion planning. Based on the detectability measure, the robotic motion planning framework based on Model Predictive Control (MPC) is proposed to achieve continuous and robust tracking of single, two and three targets in complex environments. Simulations and experiments are performed and verify the performances of our method better than the state-of-the-art methods.