Dynamic Perception-Enhanced Motion Planning and Control for UAVs Flights in Challenging Dynamic Environments

Abstract

The autonomous flights of unmanned aerial vehicles (UAVs) in unknown environments have garnered significant attention. However, most existing methods only achieve safe navigation in static environments or spacious scenes with few moving obstacles. Motivated by this open problem, this paper presents a complete system for safe and autonomous UAVs flights in unknown clustered environments with multiple dynamic obstacles. To properly represent complex dynamic environments, we develop a 3D dynamic Euclidean Signed Distance Field (ESDF) mapping method that initially segments and tracks dynamic obstacles using a novel feature-based association strategy, while fusing the remaining static obstacles into ESDF map. Then, we propose a joint trajectory planning and motion control framework for safely avoiding surrounding obstacles. Specifically, the gradient-based B-spline trajectory optimization algorithm is employed to generate a collision-free static trajectory with respect to static obstacles. To avoid dynamic obstacles while adaptively tracking the static trajectory, we utilize time-adaptive model predictive control combined with Dynamic Control Barrier Function (D-CBF), which maps the collision avoidance constraints of dynamic obstacles onto the control inputs. Extensive simulated and real-world experiments confirm that our proposed method outperforms previous approaches for UAVs flights in challenging dynamic environments.