Manipulator Generative Design Optimization for Orchard Environments

Abstract

Manipulators are essential for advancing orchard robotics tasks such as pruning and harvesting, which require precise, dexterous motion in cluttered and unstructured environments. Off-the-shelf industrial arms, while readily available, often lack the reach and dexterity required for these settings. In this paper we present a simulation-driven, multi-objective optimization framework for task-specific manipulator kinematics, leveraging the NSGA-II evolutionary algorithm and physics-based evaluation. Candidate designs are encoded with high-level parameters -- joint type, axis orientation, link length, and joint count -- then automatically generated as URDF models and evaluated in simulation for reachability, manipulability, torque demand, and motion planning cost. Trade-offs are revealed on a Pareto front, enabling exploration across diverse designs. The framework is demonstrated on a real-world tree pruning task, using collected 3D scans of expert-pruned trees and an automated prune point identification pipeline to generate target points to guide the optimization. Results show that the proposed approach produces task-specific manipulator designs with improved workspaces and reduced operational constraints compared to a commercial industrial arm, offering a viable pathway toward deployable agricultural manipulation systems.