To investigate human response to robot adaptation at the motion-planning level, we devised a human subject experiment in which participants worked cooperatively with a robot to perform a collaborative task within a shared workspace.
In HDRC3, the essential generic functionalities of a UAV are isolated for effective integration of low-level (navigational subsystem, low-level control with motion planning) and high-level (mission planning and execution) functionalities.
The system integrates the candidate assembly sequences computed in the assembly level incrementally and recursively with graph searching and motion planning in the motion level and plans the assembly sequences and motions integratedly for assembly tasks.
For each sequence, we perform motion planning in the motion level to manipulate the base object and to assemble the other object to the base.
In this paper, we propose a novel repetitive motion planning (RMP) scheme at the joint-acceleration level (termed, the acceleration-level RMP scheme, the ARMP scheme), which incorporates joint-angle limits, joint-velocity limits and joint-acceleration limits.
We propose an integrated assembly and motion planning system which is done in both assembly level and motion level.
In the case of the global path planning for motion under certain maps, the computational time of that method becomes expensive when it is applied to high-level of the motion planning.
In the motion level, the system performs motion planning recursively for each candidate assembly sequence and finds the motions to manipulate the base object as well as to assemble the other object to the base.
If the motion planning algorithms cannot find a path in the motion level, we roll back to the assembly level and try another candidate assembly sequence or try a different base until a solution is found or failure is reported.
Particularly, this paper concerns the skill level and proposes a modeling method of criteria of skilled operators for motion planning of a redundant manipulator.
This paper presents a hybrid control framework for the motion planning of a multi-agent system including N robotic agents and M objects, under high level goals.
