A Systematic Robot Design Optimization Methodology with Application to Redundant Dual-Arm Manipulators

TL;DR

This paper presents a comprehensive methodology for optimizing robot design, specifically applied to dual-arm manipulators for agricultural harvesting, improving task success and dexterity through simulation and novel metrics.

Contribution

It introduces a systematic, four-part optimization framework for designing task-specific robots, incorporating new metrics based on manipulator redundancy.

Findings

Reachability success increased by at least 14%

Dexterity improved by over 30%

Framework effectively optimizes dual-arm robotic systems

Abstract

One major recurring challenge in deploying manipulation robots is determining the optimal placement of manipulators to maximize performance. This challenge is exacerbated in complex, cluttered agricultural environments of high-value crops, such as flowers, fruits, and vegetables, that could greatly benefit from robotic systems tailored to their specific requirements. However, the design of such systems remains a challenging, intuition-driven process, limiting the affordability and adoption of robotics-based automation by domain experts like farmers. To address this challenge, we propose a four-part design optimization methodology for automating the development of task-specific robotic systems. This framework includes (a) a robot design model, (b) task and environment representations for simulation, (c) task-specific performance metrics, and (d) optimization algorithms for refining configurations. We demonstrate our framework by optimizing a dual-arm robotic system for pepper harvesting using two off-the-shelf redundant manipulators. To enhance performance, we introduce novel task metrics that leverage self-motion manifolds to characterize manipulator redundancy comprehensively. Our results show that our framework achieves simultaneous improvements in reachability success rates and improvements in dexterity. Specifically, our approach improves reachability success by at least 14\% over baseline methods and achieves over 30\% improvement in dexterity based on our task-specific metric.