Prioritized Multi-task Motion Coordination of Physically Constrained Quadruped Manipulators

Quadruped manipulators can use legs to mimic legged animals for crossing unstructured environments. They can also use a bionic arm to execute manipulation tasks. The increasing demands for such robots have pushed research progress. However, there remain challenging works in their usage of a high degree of freedom. To solve this redundant problem, we propose a novel motion coordination framework based on multi-task prioritization and null-space projection. The framework can adaptively generate optimal motion for different parts of the robot considering 3 prioritized tasks. The tasks include end-effector trajectory tracking, motion redistribution to meet physical constraints, and manipulability enhancement. The motion is then executed by a whole-body controller incorporating dynamics, inverse kinematics, multiobjective priorities, and force constraints. Experiments both in simulation and on the robot platform validate the advantages and effectiveness of the algorithm. The robot can finish robust and accurate operational space end-effector tracking with errors less than 3 cm.