MMH-Planner: Multi-Mode Hybrid Trajectory Planning Method for UAV Efficient Flight Based on Real-Time Spatial Awareness

2026-03-03

AI Summary

Existing UAV trajectory planning methods often struggle to balance efficiency and safety due to rigid strategies and insufficient environmental adaptability. This work proposes a goal-directed, real-time spatial-awareness-based multimodal hybrid trajectory planning framework that dynamically evaluates the safety of the forward environment and adaptively selects the optimal planning model to generate high-quality trajectories. To reduce computational overhead, an inertia-based replanning mechanism is introduced, which triggers replanning only when necessary. The approach innovatively integrates multimodal planning with on-demand replanning, significantly enhancing planning efficiency while ensuring flight safety. Comprehensive simulations and real-world flight experiments demonstrate that the proposed method outperforms state-of-the-art algorithms in key metrics, including average number of planning iterations and per-iteration computational cost.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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.

Problem

• trajectory planning
• UAV
• planning efficiency
• environmental adaptability
• real-time spatial awareness

Innovation

• multi-mode hybrid planning
• real-time spatial awareness
• lazy replanning
• UAV trajectory planning
• goal-oriented perception