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

2025-07-29

AI Summary

Deployment of robotic arms for high-value crop harvesting (e.g., chili peppers) remains heavily reliant on expert intuition, lacking a systematic, optimization-driven configuration design methodology. Method: This paper proposes an automated configuration optimization framework for dual-arm redundant robots, introducing a novel task metric based on self-motion manifolds. The framework integrates joint modeling, environment simulation, task-specific performance evaluation, and global optimization to determine the globally optimal robot placement and configuration. Contribution/Results: The approach transcends traditional heuristic paradigms, significantly enhancing deployment repeatability and end-user accessibility. In chili harvesting tasks, it achieves ≥14% improvement in reachability success rate and over 30% enhancement in dexterity compared to baseline methods, empirically validating its effectiveness and practicality in complex agricultural environments.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Algorithm Configuration
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• Optimizing manipulator placement for peak performance in robots
• Designing task-specific robots for complex agricultural environments
• Improving reachability and dexterity in redundant dual-arm manipulators

Innovation

• Four-part design optimization methodology for robots
• Novel task metrics using self-motion manifolds
• Optimized dual-arm system for pepper harvesting