Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

Abstract

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A* and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard.

8 pages. Published in International Conference on Robotics and Automation (ICRA) 2018. IEEE copyright

References in corpus (1)

• Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

Cited by in corpus (4)

• Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization
• RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight
• Robust and Efficient Quadrotor Trajectory Generation for Fast Autonomous Flight
• An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors