Online Trajectory Generation With Local Replanning for 7-DoF Serial Manipulator in Unforeseen Dynamic Environments

In this letter, we focus on online motion planning for manipulators in dynamic obstacle environments. An analytical geometry-based inverse kinematics solution for generalized types of 7-DoF anthropomorphic manipulators is presented to work as the basis of high-efficiency collision avoidance planning. We present a real-time collision avoidance method that intuitively evaluates the distance and plans the obstacle avoidance motion of the manipulator links in Cartesian space, then maps the motion to joint space via the analytical inverse kinematics. The collision avoidance method can be directly applied to a low-level controller with a control cycle of 1ms. Finally, we extend this method to a local planner and incorporate it into the trajectory optimization framework to achieve global manipulation planning and online local replanning to avoid unforeseen dynamic obstacles. The motion planning framework can generate smooth and time-efficient trajectories considering the mechanical limits of the manipulator. Simulations and real-world tests are conducted to validate the effectiveness of the proposed methods and the motion planning framework.