Organizations: School of Surveying and Mapping, Information Engineering University, Zhengzhou, 450001, China · School of Geodesy and Geomatics, Hubei Luojia Laboratory, Wuhan University, Wuhan 430079, China
Motion planning is a critical component of intelligent unmanned systems, enabling their complex autonomous operations. However, current planning algorithms still face limitations in planning efficiency due to inflexible strategies and weak adaptability. To address this, this paper proposes a multi-mode hybrid trajectory planning method for UAVs based on real-time environmental awareness, which dynamically selects the optimal planning model for high-quality trajectory generation in response to environmental changes. First, we introduce a goal-oriented spatial awareness method that rapidly assesses flight safety in the upcoming environments. Second, a multi-mode hybrid trajectory planning mechanism is proposed, which can enhance the planning efficiency by selecting the optimal planning model for trajectory generation based on prior spatial awareness. Finally, we design a lazy replanning strategy that triggers replanning only when necessary to reduce computational resource consumption while maintaining flight quality. To validate the performance of the proposed method, we conducted comprehensive comparative experiments in simulation environments. Results demonstrate that our approach outperforms existing state-of-the-art (SOTA) algorithms across multiple metrics, achieving the best performance particularly in terms of the average number of planning iterations and computational cost per iteration. Furthermore, the effectiveness of our approach is further verified through real-world flight experiments integrated with a self-developed intelligent UAV platform.
Figures & tables
Fig. 1 : Framework of the multi-mode hybrid planner.
Fig. 2 : Schematic diagram of the goal-oriented spatial awareness method. This figure illustrates three distinct perception scenarios. Specifically, the regions corresponding to green rays indicate no obstacles are detected, representing a safe environment. The areas covered by yellow rays reveal passage through narrow regions via local perception, while red rays denote scenarios where obstacle checking confirms that the rays pass through obstacles. Both the latter two cases involve obstacles that compromise the current flight safety, thereby necessitating obstacle avoidance planning.
Fig. 3 : Schematic diagram of the multi-mode hybrid planning mechanism. When no obstacles are detected by the sensing ray, as shown in (a), the system operates in fast planning mode to rapidly generate high-quality trajectories (red curves) without incorporating safety constraints. Conversely, when obstacles are detected (indicated by red collision points in (b)), the system switches to standard optimization mode. In this mode, a spatiotemporally optimized avoidance trajectory is generated within the SFC constructed based on an initial collision-free geometric path. Once newly emerged obstacles (blue solid triangles in (c)) interfere with the ongoing flight trajectory, the emergency obstacle avoidance mode is activated immediately, triggering local re-optimization to promptly compute a safe and dynamically feasible alternative trajectory (green curve).
TABLE I : Performance comparison of four methods in different environments.
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639956
Department of Engineering and Naval Architecture (DITEN), University of Genoa, 16145 Genoa, Italy · Intelligent Systems Laboratory, Department of Systems Engineering and Automation, Carlos III University of Madrid, 28911 Legan´es, Spain