Extended Optimization: A Composite Motion Consensus Planning Scheme for Multi‐Mobile Manipulator System Via Streaming Neural Network

In order to break the traditional single‐end planning for multi‐mobile manipulator systems, a composite motion consensus planning (CMCP) scheme is proposed and analyzed. In the treatment of traditional motion schemes, it is common to plan the end‐effector. The platform route is usually irregular and uncontrolled. Distinct from the traditional scheme, the CMCP scheme not only makes dual‐ends (end‐effectors and platforms) independent planning, but also serves as a heuristic idea to transform control methods to equality constraints allows multi‐mobile manipulator system to achieve consensus. To achieve this goal, the velocity‐level kinematic models of dual‐ends are constructed separately. Then, the CMCP scheme is proposed via designed multiple constraints and solved by the designed streaming neural network. Experimental results demonstrate the effectiveness of CMCP scheme. Traditional single‐end planning is expanded and optimized.