In this paper, we propose a new method of grasp planning for a manipulator with a parallel jaw gripper in obstacle environment. We consider collision avoidance as a problem of motion planning in obstacle environments. In general, however, a redundant degree of freedom (DOF) is required to avoid obstacle and to grasp. In our proposal, we pay attention to redundant DOF in a grasping pose. Using redundant DOF in a grasping pose, a manipulator can avoid obstacles and grasp a target object. It does not require any redundant manipulators.
This study proposes a novel collision avoidance and motion planning framework for connected and automated vehicles based on an improved velocity obstacle (VO) method. The controller framework consists of two parts, that is, collision avoidance method and motion planning algorithm. The VO algorithm is introduced to deduce the velocity conditions of a vehicle collision. A collision risk potential field (CRPF) is constructed to modify the collision area calculated by the VO algorithm. A vehicle dynamic model is presented to predict vehicle moving states and trajectories. A model predictive control (MPC)-based motion tracking controller is employed to plan collision-avoidance path according to the collision-free principles deduced by the modified VO method. Five simulation scenarios are designed and conducted to demonstrate the control maneuver of the proposed controller framework. The results show that the constructed CRPF can accurately represent the collision risk distribution of the vehicles with different attributes and motion states. The proposed framework can effectively handle the maneuver of obstacle avoidance, lane change, and emergency response. The controller framework also presents good performance to avoid crashes under different levels of collision risk strength.
An NMPC Approach using Convex Inner Approximations for Online Motion Planning with Guaranteed Collision Avoidance