A Novel Planning Framework for Complex Flipping Manipulation of Multiple Mobile Manipulators

TL;DR

A novel planning framework is proposed for multiple mobile manipulator systems that can greatly extend the adaptability of multiple mobile manipulator systems in complex manipulation tasks.

Abstract

During complex object manipulation, manipulator systems often face the configuration disconnectivity problem due to closed-chain constraints. Although regrasping can be adopted to get a piecewise connected manipulation, it is a challenging problem to determine whether there is a planning result without regrasping. To address this problem, a novel planning framework is proposed for multiple mobile manipulator systems. Coordinated platform motions and regrasping motions are proposed to enhance configuration connectivity. Given the object trajectory and the grasping pose set, the planning framework includes three steps. First, inverse kinematics for each mobile manipulator is verified along the given trajectory based on different grasping poses. Coverable trajectory segments are determined for each robot for a specific grasping pose. Second, the trajectory choice problem is formulated into a set cover problem, by which we can quickly determine whether the manipulation can be completed without regrasping or with the minimal regrasping number. Finally, the motions of each mobile manipulator are planned with the assigned trajectory segments using existing methods. Both simulations and experimental results show the performance of the planner in complex flipping manipulation. Additionally, the proposed planner can greatly extend the adaptability of multiple mobile manipulator systems in complex manipulation tasks.

Figures (8)

• Figure 1: The right side manipulator suffers the configuration disconnectivity due to unreachable space and self-collision, resulting in the inability to continue manipulating the object along the desired trajectory.
• Figure 2: Different motions of mobile manipulators enhance connectivity when manipulating objects. (a) Due to self-collision or joint angle limits of the robots, the expected trajectory of the object may not be completed. (b) Moving the platform to increase connectivity while keeping the grasping pose unchanged. (c) Regrasping transitioning to other configurations to enhance connectivity.
• Figure 3: The framework of our planner. The object trajectory and grasp poses serve as inputs to the planner.
• Figure 4: The whole trajectory is divided into different segments with different grasping poses for the robots based on minimizing the number of regrasping. An example of two robots.
• Figure 5: Flipping manipulation of a chair by two mobile JAKA Zu7 manipulators. Comparisons between the proposed planner and other methods.