Synthesizing Modular Manipulators For Tasks With Time, Obstacle, And Torque Constraints

TL;DR

This paper presents an automated framework for designing and controlling modular serial chain robots to perform complex tasks involving time, obstacle avoidance, and torque constraints, optimizing for specific task requirements.

Contribution

It introduces a novel optimization-based method that synthesizes both the design and control parameters of modular manipulators from task specifications.

Findings

Successfully synthesized a robot to navigate a constrained environment while holding an object.

Demonstrated the framework's ability to handle complex task constraints.

Validated the approach on a real-world-like scenario.

Abstract

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.