Real-Time Coverage Path Planning for Fixed-Wing Aerial Robots Using Partial Gradient-Based MPC and Augmented Dubins Trajectories

Trajectory Planning for Unmanned Surface Vehicles in Complex Environments via CasADi-Based Optimal Control

Results indicate that the proposed formulation can generate safe, feasible, and smooth USV trajectories in representative complex environments.

Nonlinear Model Predictive Control for Trajectory Tracking of Differentially Flat Fixed-Wing Aerial Systems

Planning and control of fixed-wing Unmanned Aerial Vehicles (UAVs) are challenging due to nonlinear dynamics, aerodynamic limits, and environmental disturbances. Differential flatness offers a principled way to generate fast, feasible trajectories, but its use has largely been confined to model-free controllers, which lack predictive capabilities and demand tuning. In this paper, we propose a unified framework that integrates differential flatness-based trajectory generation with Nonlinear Model Predictive Control (NMPC), combining computationally efficient planning with predictive, constraint-aware control. To further improve robustness, we introduce a wind-aware sampling strategy embedded within the NMPC framework, enabling the generation of dynamically feasible reference trajectories that proactively account for wind disturbances while strictly enforcing aerodynamic and control input constraints. We validate the proposed framework through extensive simulations and real-world flight experiments, demonstrating improved tracking accuracy and robustness for complex trajectories, particularly when using the proposed wind-aware sampling strategy under strong wind conditions.

A TRAJECTORY PLANNING METHOD FOR FIXED-WING AIRCRAFT IN STATIC NO-FLY ZONE ENVIRONMENTS

To address the difficulty of simultaneously satisfying heading continuity, minimum turning-radius constraints, and safe obstacle avoidance in trajectory planning for fixed-wing aircraft operating in static no-fly zone (NFZ) environments, a horizontal trajectory planning model is formulated by incorporating position, heading, curvature constraints, and safety margins around NFZs. A planning method combining weighted kinematic A* with Dubins terminal connection is then proposed. The method jointly represents planar position and heading angle as the search state and expands nodes using constant-curvature motion primitives that satisfy the prescribed curvature constraints. A weighted Dubins distance is employed to guide the search toward the target, while a Dubins curve is used within the target neighborhood to connect the current state to the desired terminal state. Simulation results demonstrate that, in scenarios involving multiple circular NFZs, the proposed method can generate continuous collision-free trajectories that satisfy the prescribed initial and terminal headings, safety-clearance requirements, and minimum turning-radius constraints. The resulting trajectory length increases only moderately relative to the straight-line distance, while the explored nodes are primarily concentrated near feasible passages. These results validate the effectiveness of the proposed method for trajectory planning of fixed-wing aircraft in static NFZ environments.

Model Predictive Path Integral for Agile UAV Control with Online Collision Avoidance

Trajectory planning of unmanned aerial vehicles in uncertain environments based on model predictive control and dual-model control

Aiming at the difficulties of autonomous trajectory planning for unmanned aerial vehicles (UAVs) in unknown and dynamically uncertain environments, such as insufficient prediction ability, easy falling into local optimum, poor environmental adaptability, and information scarcity caused by passive obstacle avoidance, traditional single-model predictive control is difficult to balance computational efficiency and uncertainty compensation. Therefore, this paper proposes a UAV trajectory planning method based on Model Predictive Control dual-model cooperative control, which organically integrates model predictive control with the exploration-exploitation strategy. The method adopts a dual architecture of nominal model + online learning model: the simplified nominal model ensures real-time solution efficiency, and the online learning model dynamically compensates for unmodeled dynamics and environmental disturbances. By introducing information gain and uncertainty attenuation indicators into the MPC optimization objective, the UAV is guided to actively explore unknown areas, avoid local optimum traps, and achieve a dynamic balance between exploration and exploitation. Simulation verification based on MATLAB and CasAdi toolbox shows that the proposed method can significantly reduce environmental uncertainty, improve trajectory tracking accuracy and active obstacle avoidance ability, and exhibit stronger robustness, decision-making intelligence and flight efficiency in complex unstructured environments.

A Simulation-Based Dynamic Path Planning Approach for Low-Altitude Unmanned Aerial Vehicles in Inspection Scenarios

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.