Synthesizing Modular Manipulators For Tasks With Time, Obstacle, And Torque Constraints
This work addresses the challenge of selecting appropriate modular robot designs from a large search space for specific tasks, which is incremental as it builds on existing modular robotics and optimization methods.
The authors tackled the problem of automatically synthesizing both the design and controls for modular serial chain manipulators given complex task specifications, including 3D waypoints, time constraints, loads, and obstacles, by encoding these as a constrained optimization in kinematics and dynamics, and demonstrated it on a task involving navigation while holding an object.
Modular robots can be tailored to achieve specific tasks and rearranged to achieve previously infeasible ones. The challenge is choosing an appropriate design from a large search space. In this work, we describe a framework that automatically synthesizes the design and controls for a serial chain modular manipulator given a task description. The task includes points to be reached in the 3D space, time constraints, a load to be sustained at the end-effector, and obstacles to be avoided in the environment. These specifications are encoded as a constrained optimization in the robot's kinematics and dynamics and, if a solution is found, the formulation returns the specific design and controls to perform the task. Finally, we demonstrate our approach on a complex specification in which the robot navigates a constrained environment while holding an object.