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.
The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while accounting for dynamic risk factors such as weather conditions, communication reliability, and battery autonomy. This paper introduces the MOAR path planner which addresses the complexities of evolving risks during missions. It offers real-time trajectory adaptation while concurrently optimizing safety, time, and energy. The planner employs a risk-aware cost function that integrates pre-computed cost maps, the new concepts of damage and insertion costs, and an adaptive speed planning framework. With that, the optimal path is searched in a graph using a discrete representation of the state and action spaces. The method is evaluated through simulations and real-world flight tests. The results show the capability to generate real-time trajectories spanning a broad range of evaluation metrics: around 90% of the range occupied by popular algorithms. The proposed framework contributes by enabling UAVs to navigate more autonomously and reliably in critical missions.
Louis Petit, Alexis Lussier Desbiens
the Createk Design Lab, University of Sherbrooke, Sherbrooke, J1K 2R1, Canada
Autonomous exploration in unknown environments is key for mobile robots, helping them perceive, map, and make decisions in complex areas. However, current methods often rely on frequent global optimization, suffering from high computational latency and trajectory oscillation, especially on resource-constrained edge devices. To address these limitations, we propose SCOPE, a novel framework that incrementally constructs a real-time skeletal graph and introduces Implicit Unknown Region Analysis for efficient spatial reasoning. The planning layer adopts a hierarchical on-demand strategy: the Proximal Planner generates smooth, high-frequency local trajectories, while the Region-Sequence Planner is activated only when necessary to optimize global visitation order. Comparative evaluations in simulation demonstrate that SCOPE achieves competitive exploration performance comparable to state-of-the-art global planners, while reducing computational cost by an average of 86.9%. Real-world experiments further validate the system's robustness and low latency in practical scenarios.
Kai Li, Shengtao Zheng, Linkun Xiu +4
Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639956
This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-UAV trajectory design from a repeated combinatorial optimization problem into a hierarchical probabilistic inference problem. In the offline phase, a genetic-algorithm planner with repulsive-force collision avoidance (GA--RF) generates expert demonstrations, which are abstracted into Mission, Route, and Motion dictionaries. These dictionaries are used to learn a probabilistic world model that captures how expert mission allocations induce route orders and how route orders induce motion-level behaviors. During online operation, the UAV swarm evaluates candidate actions by forming posterior beliefs over symbolic states and minimizing KL-divergence-based abnormality indicators with respect to expert-derived reference distributions. This enables mission allocation, route insertion, motion adaptation, and collision-aware replanning without rerunning the offline optimizer. Bayesian state estimators, including EKF and PF modules, are integrated at the motion level to improve trajectory correction under uncertainty. Simulation results show that the proposed framework preserves expert-like planning structure while producing smoother and more stable behavior than modified Q-learning. Additional validation using real-flight UAV trajectory data demonstrates that the learned world model can correct symbolic predictions under noisy and non-smooth observations, supporting its applicability to adaptive UAV swarm autonomy.
Kaleem Arshid, Ali Krayani, Lucio Marcenaro +2
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