Planning Optimal Trajectories for Mobile Manipulators under End-effector Trajectory Continuity Constraint

Mobile manipulators have been employed in many applications which are usually performed by multiple fixed-base robots or a large-size system, thanks to the mobility of the mobile base. However, the mobile base also brings redundancies to the system, which makes trajectory planning more challenging. One class of problems recently arising from mobile 3D printing is the trajectory-continuous tasks, in which the end-effector is required to follow a designed continuous trajectory (time-parametrized path) in task space. This paper formulates and solves the optimal trajectory planning problem for mobile manipulators under end-effector trajectory continuity constraint, which allows considerations of other constraints and trajectory optimization. To demonstrate our method, a discrete optimal trajectory planning algorithm is proposed to solve mobile 3D printing tasks in multiple experiments.

Task-Space Clustering for Mobile Manipulator Task Sequencing

Mobile manipulators have gained attention for the potential in performing large-scale tasks which are beyond the reach of fixed-base manipulators. The Robotic Task Sequencing Problem for mobile manipulators often requires optimizing the motion sequence of the robot to visit multiple targets while reducing the number of base placements. A two-step approach to this problem is clustering the task-space into clusters of targets before sequencing the robot motion. In this paper, we propose a task-space clustering method which formulates the clustering step as a Set Cover Problem using bipartite graph and reachability analysis, then solves it to obtain the minimum number of target clusters with corresponding base placements. We demonstrated the practical usage of our method in a mobile drilling experiment containing hundreds of targets. Multiple simulations were conducted to benchmark the algorithm and also showed that our proposed method found, in practical time, better solutions than the existing state-of-the-art methods.