Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight (1907.01531v2)

Abstract: In this paper, we propose a robust and efficient quadrotor motion planning system for fast flight in 3-D complex environments. We adopt a kinodynamic path searching method to find a safe, kinodynamic feasible and minimum-time initial trajectory in the discretized control space. We improve the smoothness and clearance of the trajectory by a B-spline optimization, which incorporates gradient information from a Euclidean distance field (EDF) and dynamic constraints efficiently utilizing the convex hull property of B-spline. Finally, by representing the final trajectory as a non-uniform B-spline, an iterative time adjustment method is adopted to guarantee dynamically feasible and non-conservative trajectories. We validate our proposed method in various complex simulational environments. The competence of the method is also validated in challenging real-world tasks. We release our code as an open-source package.

• The paper introduces a hybrid method that combines kinodynamic path searching with B-spline optimization to generate safe and fast trajectories.
• The paper refines initial trajectories through iterative B-spline time adjustments, enhancing trajectory smoothness and dynamic feasibility with limited computation.
• The paper validates its approach through extensive simulations and real-world experiments, demonstrating efficiency and reliability for aggressive UAV navigation.

Overview of "Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight"

This paper discusses a methodology for generating robust and efficient trajectories for quadrotor UAVs operating in complex three-dimensional environments. The primary focus is on developing a motion planning system that addresses the challenges of safe, fast, and autonomous quadrotor flight by ensuring kinodynamic feasibility and aggressive trajectory generation within the constraints of limited computational resources.

The authors introduce a hybrid approach that combines a kinodynamic path searching method and B-spline trajectory optimization. The initial trajectory is generated using a kinodynamic path search that ensures safety and minimum-time travel in a discretized control space. This trajectory is later refined through B-spline optimization, which leverages Euclidian distance fields and dynamic constraints to improve trajectory smoothness and clearance. This complete method allows the generation of dynamically feasible and non-conservative trajectories by iteratively adjusting time allocations in the B-spline representation.

The paper presents strong numerical validation of the proposed system. Experiments conducted in various simulated and real-world complex environments demonstrate its efficiency, robustness, and high success rate in generating aggressive motions under dynamic constraints. The implementation is made publicly available, promoting further research and practical application in UAV path planning.

Key Contributions

1. Systematic Approach: The integration of kinodynamic path searching with B-spline optimization addresses the core issues of safety, feasibility, and speed in trajectory generation.
2. Efficient Optimization: The paper presents a B-spline optimization strategy that is both computationally efficient and rapidly converging, making it viable for real-time applications involving fast re-planning.
3. Dynamic Feasibility and Non-Conservativeness: The system adopts a novel iterative time adjustment method, ensuring kinodynamic feasibility without imposing overly conservative dynamics constraints.

Implications and Future Directions

The implications of this research are significant for the UAV domain, especially in applications requiring high-speed navigation in unpredictable environments. By overcoming computational challenges and improving trajectory safety and feasibility, this methodology could enhance UAV automation across numerous sectors, including search-and-rescue operations, industrial inspection, and autonomous urban mobility.

Future work could expand this system's capabilities to include larger-scale environments and dynamic scenarios involving obstacles or other UAVs. Additionally, exploring extensions to multi-agent systems could lead to optimized trajectory generation in swarm robotics, facilitating coordinated flight patterns and task allocation among multiple UAVs.

This paper represents a step forward in UAV trajectory generation, combining existing kinematic and computational frameworks to create a more reliable and versatile solution for real-world autonomous flight challenges.