The study shows that the improvement of opposition-based learning plays the most significant role in all three mechanisms and shows an efficient multi-UAV path planning for urban aerial vehicles that outperform current solutions and can be readily integrated into learning-based approaches and deployed on physical hardware.
Abstract
Classical path planning algorithms such as A* applied to multi-UAV systems are unable to cope with multi-UAV complex scenarios in urban low altitude. This is due to the limitations in computational speed and convergence at a local optimal which are unable to guarantee the optimal flight energy and mission success rate. For such purposes, the Upgraded Gooseneck Barnacle Optimization (UGBO) algorithm is proposed based on the concept of swarm intelligence meta-heuristic intelligence with three mechanisms of improvements. They are opposing-based learning for better diversity of initial population, self-adaptive population adjust to balancing explore and exploit, and a forbidden strategy to get out of the local optimal. An improved energy consumption model considering aerobic drag, and wind impacts are employed, with clear multi-UAV coordination in the process through horizontal layer and proximity deconfliction in three-dimensional space, so the trajectory will be physically realizable and collision-free. Simulation in a 100 m × 100 m × 50 m simulation environment including fifteen to twenty static obstacles, and three to five dynamic obstacles show the UGBO achieve the path length nine hundred fifty meters, flight energy four hundred eighty units, calculation time twelve seconds, and average optimal fitness 0.85. These are, compared to other three classical algorithms A*, ACO, and PSO, improved by 19.5-33.1%, 19.3-33.3%, and 25.0-112.5%, respectively. Also, comprehensive experimental comparison with the state-of-the-art research such as PR-DQN, APF-RRT, T-DRL, and AHMP demonstrates that UGBO perform well with 3.1-17.4% improvement from those four approaches. Through ablating test, the study show that the improvement of opposition-based learning plays the most significant role in all three mechanisms. The results show an efficient multi-UAV path planning for urban aerial vehicles that outperform current solutions and can be readily integrated into learning-based approaches and deployed on physical hardware.
To address the challenges of three-dimensional (3D) flight path planning for Unmanned Aerial Vehicles (UAVs) in complex urban environments, this paper proposes a reinforcement learning approach based on the Deep Q-Network (DQN) algorithm. The method enables intelligent flight path planning within a discretized 3D urban space, dynamically avoiding obstacles in real-time through the UAV's sensory perception. The UAV agent is trained in a simulated 100×100×20 virtual urban environment, with training scenarios categorized into high, medium, and low difficulty levels to progressively enhance the agent's decision-making capabilities. Throughout the training process, a greedy strategy is adopted to balance the exploration of new potential paths and the exploitation of known optimal routes. Once over 80% of the UAV agents successfully reach their designated target points, the training program automatically advances to the next difficulty level. Experimental results validate the effectiveness of the proposed method, demonstrating its superior obstacle avoidance capabilities and exceptional energy optimization performance in complex urban settings.
Yang Li, Xinjie Qian, Yanxiu Wang et al.· International Conference on...· 0 citations
The Improved Dhole Optimization Algorithm is proposed, which enhances the original DOA framework by integrating a logistic-map-based chaotic mapping, a dynamic chaotic perturbation mechanism, and an adaptive stage-division strategy, and significantly outperforms the original DOA in terms of convergence speed and final path optimality.
Weiqi Feng, Hongyu Chen, Yu-Jie Fu et al.· Aerospace· 0 citations
To address the limitations of traditional path planning methods in complex terrains, such as poor safety, low efficiency, and insufficient adaptability, this paper proposes a three-dimensional UAV path planning method based on the Manta Ray Foraging Optimization (MRFO) algorithm for rescue missions in complex mountainous environments. First, a three-dimensional safe map model is constructed by integrating terrain information and no-fly zone constraints. On this basis, multiple flight constraints including flight altitude, no-fly zone avoidance, climbing gradient, turning slope, and overload are comprehensively considered. A multi-objective weighted cost function is designed to evaluate path length, altitude stability, and path smoothness. Furthermore, the MRFO algorithm simulates three foraging behaviors of manta rays: chain foraging, spiral foraging, and somersault foraging. Combined with adaptive weight adjustment and boundary handling strategies, efficient path optimization is achieved. Simulation results demonstrate that the proposed method can rapidly generate safe, smooth, and energy-efficient rescue paths that satisfy UAV dynamic constraints. The approach significantly improves mission execution efficiency and safety in complex mountainous environments.
Xingkun Wu, Jingyi Huang· International Conference on...· 0 citations
With the popularization of unmanned aerial vehicles (UAVs) in scenarios such as military reconnaissance, logistics transportation, and post-disaster rescue, Generating optimal flight paths that guarantee both safety and timeliness amidst high-density barriers and unknown environmental factors presents a formidable challenge in autonomous navigation. Standard genetic algorithms are often hindered by premature convergence, suboptimal local solutions, and diminished genetic diversity in later iterations. In response to these challenges, we propose an Improved Adaptive Genetic Algorithm (IAGA) that synergizes an adaptive mutation strategy with an elite retention framework.This method first constructs a comprehensive cost model combining path length and obstacle threat potential field based on the grid concept, thereby converting the nonlinear path planning problem into a computable mathematical form; secondly, it designs a linearly decreasing adaptive mutation operator that can dynamically adjust the mutation probability according to the population evolution stage, thus maintaining a strong global search ability in the early stages of the algorithm and strengthening local fine search in the later stages; finally, it introduces an elite retention strategy to ensure that excellent individuals are retained and participate in subsequent evolution, avoiding population degradation. Simulation experiments conducted in a Python environment show that in a complex obstacle environment of 100m × 100m× 100m, the IAGA algorithm can effectively plan the optimal path, and the convergence speed is significantly improved compared to traditional genetic algorithms, verifying the effectiveness and robustness of this method in handling multi-constraint path planning problems.
Qian Wan, Tian-En Lu, Liquan Huang et al.· International Conference on...· 0 citations
The Circle-SPM chaotic map is introduced to optimize the population initialization process, effectively mitigating the premature convergence caused by uneven distribution and a lack of population diversity.
Jian Deng, Honghai Zhang, Zeyu Liu et al.· Cluster Computing· 0 citations
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