3D Path Planning for UAVs Based on an Improved DOA

MRFO-based 3D path planning for UAV rescue missions in complex mountainous environments

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.

Research on multi-objective path planning for UAVs in complex environments based on improved adaptive genetic algorithm

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.

DQN-based 3D path planning for UAVs in urban airspace

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.

A Modified Seagull Optimization Algorithm with Latin Hypercube Sampling and Lévy Flight for 3D Path Planning of UAV

UAV path planning in complex urban environments faces significant challenges due to dense obstacles, narrow corridors, and stringent safety requirements. To address these issues, this paper proposes LLSOA, a modified Seagull Optimization Algorithm that integrates Latin Hypercube Sampling (LHS) for population initialization and Lévy Flight for global search. The key innovation lies in the problem-driven design: LHS ensures uniform coverage in dense urban maps, while Lévy Flight helps escape local optima. Compared with four state-of-the-art swarm intelligence algorithms (DBO, GWO, PIO, and PSO) across four urban scenarios, LLSOA achieves the best comprehensive fitness. Considering multiple constraints including path length, curvature, collision avoidance, and obstacle-avoidance logic, the trajectories generated by LLSOA show competitive overall performance, with no unsafe points recorded in the test scenarios and the best fitness values among the compared algorithms, albeit with a slight trade-off in path length. High-fidelity AirSim simulations with GPS/IMU noise further demonstrate that the planned trajectories remain within engineering acceptable limits. Compared with the noise-free baseline, the maximum trajectory deviation increases by 2.3% and the average deviation increases by 2.8% under high GPS/IMU noise. The main contributions are: (1) a problem-driven LLSOA that combines LHS and Lévy Flight, specifically tailored to dense urban environments; (2) theoretical analysis and simulation verification demonstrating its feasibility for multi-constraint path planning under the tested conditions; (3) high-fidelity (UE+AirSim) validation showing that the generated trajectories retain stability even under realistic sensor noise.

TDO-MSF: A turn-deviation optimized multi-stage framework for smooth three-dimensional UAV trajectories

Path planning remains a major challenge for the autonomous navigation of Unmanned Aerial Vehicles (UAVs). Despite significant advances in graph-based, sampling-based, metaheuristic, and learning-based path-planning approaches, many existing methods still produce excessive turning points, large path deviations, and trajectories that are misaligned with UAV maneuverability constraints. To address these limitations, this paper presents a Turn-Deviation Optimized Multi-Stage Framework (TDO-MSF) for UAV path planning. The proposed framework integrates a bidirectional A* search algorithm, a turn-deviation reduction strategy, and a direction-constrained neighborhood expansion mechanism to reduce unnecessary directional changes during path generation. A multiscale inflection correction method is employed to eliminate redundant turning points, while a curvature-constrained adaptive-radius smoothing technique is applied to improve trajectory continuity and maneuverability. In addition, a trajectory-guided Enhanced Artificial Potential Field (E-APF) algorithm is incorporated for obstacle avoidance, along with constraints on velocity, acceleration, jerk, and turning radius. The proposed framework is evaluated in various 3D benchmark environments with different obstacle densities and configurations. Its performance is compared with those of the original A*, Improved A*, Improved RRT, Improved PSO, ICSSA, and the baseline MSF-MTPO algorithms using both conventional path-planning and trajectory-quality metrics. Experimental results indicate that the proposed TDO-MSF achieves an average path-length reduction of approximately 2.08% and a 29.25% reduction in the number of turns compared with the benchmark methods. Furthermore, it improves trajectory smoothness and obstacle-avoidance performance while maintaining curvature-constrained and trajectory-guided obstacle-avoidance capabilities.

Improved Hybrid A* Algorithm Based on Iterative Acceleration for Autonomous Vehicles