Efficient Sampling-based Multirotors Kinodynamic Planning with Fast Regional Optimization and Post Refining

For real-time multirotor kinodynamic planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a bidirectional global sampling process. The incorporation of the local optimization shows significantly improved success rates and less planning time in various types of challenging environments. We further present a refinement module utilizing the same framework as the regional optimizer. It comprehensively investigates the resulting trajectory of the global sampling and improves its smoothness with nearly negligible computation effort. Benchmark results illustrate that our proposed method can better exploit a previous trajectory compared to the state-of-the-art ones. The planning methods are applied to generate trajectories for a quadrotor system in simulation and real-world, and their capability is validated in real-time applications.

Authors

• Hongkai Ye
• Neng Pan
• Qianhao Wang
• Chao Xu 0001
• Fei Gao 0011

Keywords

• Computational modeling
• Refining
• Benchmark testing
• Real-time systems
• Planning
• Trajectory
• Space exploration
• Kinodynamic Planning
• Local Optimum
• Global Processing
• Planning Methods
• Planning Time
• Improve Success Rates
• Optimization Problem
• Time Duration
• Free Space
• High Success Rate
• Solution Space
• Drag Force
• Path Planning
• Quadratic Programming
• Global Plan
• Distance Map
• Time Allocation
• Trajectory Optimization
• Planning Framework
• Control Constraints
• Part Of Trajectory
• Time Budget
• Trajectory Duration
• Positive Definite
• Smooth Trajectory
• Iterative Solution
• Iterative Optimization Process