A distributed real-time trajectory-planning method that integrates a distributed model predictive control framework with an adaptive Gaussian collocation strategy (DA-GCMPC) was developed, which achieves lower computation time and better trajectory quality metrics under the tested simulation settings.
Abstract
Challenges in trajectory planning are encountered by fixed-wing unmanned aerial vehicle (UAV) swarms operating in environments with unknown obstacles. In this study, a distributed real-time trajectory-planning method that integrates a distributed model predictive control (DMPC) framework with an adaptive Gaussian collocation strategy (DA-GCMPC) was developed. This method leverages a distributed iterative computational framework based on DMPC to reformulate trajectory planning as an optimal control problem. To address the fixed-resolution limitation of conventional distributed MPC formulations, a complexity-aware adaptive collocation mechanism is introduced. The novelty of the method lies in adapting the collocation transcription resolution of each local MPC problem according to the instantaneous planning complexity. This mechanism selects the collocation type online according to maneuvering demand, obstacle density risk, and neighboring-UAV interaction risk, enabling the planner to balance real-time computation and constraint-handling capability under limited perception. We decomposed the UAV energy consumption and formulated the total energy consumption of the swarm as the objective function. An optimal control sequence was derived using the Gaussian collocation method by integrating obstacle avoidance constraints for fixed-wing UAVs and environmental limitations. Comparative simulations against the implemented fixed-discretization interior-point and SQP baselines showed that the proposed DA-GCMPC method achieved lower computation time and better trajectory quality metrics under the tested simulation settings, with average per-step computation times below 80 ms. In addition, an eight-UAV semi-physical hardware-in-the-loop validation was conducted to verify the real-time executability of the proposed method in a closed-loop flight control system.
Trajectory planning is a key enabling technology for UAV swarms operating in complex and obstacle-rich environments. This paper proposes a distributed nonlinear model predictive control (NMPC)-based trajectory planning method for UAV swarms, where terminal target reaching, prescribed formation maintenance, obstacle avoidance, and inter-UAV collision avoidance are incorporated into a unified predictive optimization framework. To reduce the online computational burden, a control parameterization strategy is introduced to describe the control input using M control segments, thereby reducing the dimension of the online decision variables. Furthermore, an exact-penalty-based constraint transcription method is developed to transform the original constrained optimal control problem into a lower-complexity finite-dimensional nonlinear programming problem, while efficiently handling velocity constraints, obstacle avoidance constraints, and inter-UAV collision avoidance constraints. Simulation results for a three-UAV swarm in a cluttered environment demonstrate that the proposed method can generate dynamically feasible and collision-free trajectories, while enabling the swarm to reach the assigned target positions and preserve the desired formation within a certain formation error. Furthermore, real UAV swarm flight experiments were conducted to further validate the practical feasibility and online applicability of the proposed distributed NMPC framework for UAV swarm trajectory planning.
Yingting Cui, Tong-Xin Zeng, Bin Li· Drones· 0 citations
Traditional target-oriented task allocation and path planning methods often struggle to balance real-time responsiveness to dynamic task alterations with multi-UAV cooperative operations in complex urban environments under meteorological disturbances. To address these challenges, this paper proposes a dynamic path planning method for low-altitude Unmanned Aerial Vehicles (UAVs) tailored for urban inspection missions. Integrating an improved Discrete Particle Swarm Optimization (DPSO) algorithm with a decoupled Soft Actor–Critic (SAC) and B-spline smoothing framework, the proposed approach optimizes upper-level task allocation and lower-level trajectory planning within a 3D joint meteorological-obstacle feasible region. For task scheduling, an improved DPSO algorithm embedded with a spatial topology guidance mechanism dynamically coordinates task flows governed by Poisson processes. effectively addressing the spatial blindness and fragmented route assignments typical of conventional discrete optimization. Concurrently, local trajectory replanning executes receding-horizon spatial exploration via SAC deep reinforcement learning, followed by B-spline refinement to strictly enforce UAV kinematic limits, systematically bridging continuous-space exploration with low-level flight compliance to overcome the kinematic infeasibility common in pure learning-based models. Validated through extensive Monte Carlo comparative simulations (N=50) and further verified by a high-fidelity AirSim dynamic physics engine, the results demonstrate that: (1) The improved DPSO constrains the average response latency for high-priority emergency tasks to within 40 s even under 50 concurrent dynamic tasks. (2) The lower-level replanning achieves an average execution time of 3.60±0.18 s and a path success rate of 95.8±1.2%, in numerical tests, while maintaining a 96.2% kinematic feasibility rate under realistic rigid-body inertia and aerodynamic drag. While the current 3.60 s latency presents a potential bottleneck for millisecond-level dynamic emergency reactions, the developed framework offers a highly effective and safe closed-loop dynamic scheduling solution that lays a rigorous computational foundation for low-altitude urban inspections.
Changqi Yang, Hongjie Hu, Yi Ai· Drones· 0 citations
Coverage Path Planning (CPP) for fixed-wing aerial robots remains challenging in dynamic and partially unknown environments due to the need to simultaneously satisfy coverage completeness, kinematic feasibility, and real-time adaptability. Conventional approaches typically rely on pre-defined geometric patterns or simplified motion models, which limit their effectiveness when encountering environmental disturbances or unforeseen obstacles. This paper presents a hybrid real-time CPP framework that integrates an offline coverage strategy with an online optimisation and control scheme. In the offline phase, a back-and-forth coverage pattern is generated based on the geometric properties of the region and sensor characteristics, ensuring full nominal coverage. During execution, this trajectory is adaptively refined using a Model Predictive Control (MPC) formulation augmented by a policy gradient-based update mechanism and an augmented Dubins path smoothing strategy. The MPC framework explicitly accounts for vehicle dynamics, actuator limitations, and obstacle avoidance constraints, while the policy gradient component improves the responsiveness of the optimisation process under rapidly changing conditions. The augmented Dubins formulation enables smooth and dynamically feasible transitions, allowing the vehicle to deviate from and reliably return to the nominal coverage path after disturbance or avoidance manoeuvres. Simulation results in cluttered environments with static and dynamic obstacles demonstrate that the proposed approach achieves improved tracking consistency and smoother trajectories, while maintaining real-time feasibility. Moreover, the proposed approach reduces the maximum computational burden by approximately 0.35 s compared to the conventionally utilized optimization algorithm. These results highlight the potential of the framework for practical deployment in fixed-wing aerial coverage missions operating in uncertain environments.
Mohammad Khaneghaei, Benyamin Ebrahimi, D. Asadi et al.· Aerospace· 0 citations
Unmanned Aerial Vehicle Path Planning (UAVPP) in obstacle-rich environments requires trajectories that are collision-free, threat-aware, and feasible under practical flight constraints. This study proposes an Intelligent Cooperative Differential Evolution approach, reffered to as MuCDEA, to improve the adaptability and robustness of conventional Differential Evolution (DE) for UAVPP. MuCDEA integrates complementary mechanisms from JADE, CoDE, EPSDE, SaDE, MIDE, and SHADE through adaptive strategy selection and cooperative evolution. The optimization model combines path-length (fuel) cost and threat exposure with explicit pitch and yaw constraints that enforce actuator-feasible maneuvering bounds. The proposed framework is evaluated on 20 benchmark UAVPP cases covering 2D and 3D scenarios with varying obstacle distributions and pathh discretization levels, and it is compared against 11 state-of-the-art DE variants and several widely used optimization methods using the CEC-2022 ranking methodology. Results show that the cooperative configuration MuCDEA24 achieves the best overall ranking and consistently produces feasible trajectories across the tested cases, indicating that cooperative DE strategies provide an effective and controller-compatible solution for constrained UAVPP.
H. Bouchekara, Y. A. Sha’aban, M. S. Shahriar et al.· Actuators· 0 citations