ROBOTIC MOTION PLANNING USING CONVEX OPTIMIZATION METHODS

Collision avoidance techniques tend to derive the robot away of the obstacles in minimal total travel distance. Most ofthe collision avoidance algorithms have trouble get stuck in a local minimum. A new technique is to avoid local minimum in convexoptimization-based path planning. Obstacle avoidance problem is considered as a convex optimization problem under system state andcontrol constraints. The idea is by considering the obstacles as a convex set of points which represents the obstacle that encloses inminimum volume ellipsoid, also the addition of the necessary offset distance and the modified motion path is presented. In the analysis,the results demonstrated the effectiveness of the suggested motion planning by using the convex optimization technique

Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

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