Abstract:
Although agriculture is a highly mechanized industry, essential and high-value subsectors such as horticulture and floriculture remain heavily reliant on manual labor because they require complex, contact-rich, and highly selective handling of both plants and produce. The variability and density of tree-canopy clutter further complicate the automation process, making robot performance difficult to quantify consistently and preventing the development of a single, universally effective automation solution. Modular and reconfigurable robots (MRRs) can help address this challenge by reducing the cost of creating custom robots tailored to specific task requirements. However, determining the optimal robot design configuration for an MRR system remains a complex and unintuitive process, even for experts. This thesis addresses the problem of automating the robot design process by introducing a systematic design framework that unifies deterministic and consistent task-performance metrics with global optimization methods primarily targeting agricultural manipulation tasks.

The first contribution targets the challenge of computing self-motion manifolds (SMMs), which are global inverse-kinematics solutions for redundant manipulators. We solve this problem using Runge-Kutta solvers after posing the underlying ordinary differential equation problem in a form we call the SMM Initial Value Problem (SMM-IVP). The SMM-IVP is able to trace the manipulator’s self-motion configuration space reliably. Compared to existing predictor-corrector and linear step-corrector approaches, the SMM-IVP exhibits improved convergence behavior and numerical stability. For design applications, the SMM-IVP acts as a global inverse-kinematics procedure that provides consistent and initialization-independent performance characterization, which is essential for design optimization in the cluttered conditions typical of agricultural manipulation.

Building on the first contribution, the second contribution develops a general framework for formulating and solving robot-design optimization problems. In parallel, we introduce new SMM-based performance metrics that more accurately characterize dexterity metrics for redundant manipulators. We apply this framework and the new metrics to a manipulator placement optimization problem for a dual-arm pepper-harvesting system, and we show that it produces highly performant, non-intuitive configurations that outperform both human-expert designs and conventional dexterity-based baselines.

The third contribution grounds our design methods in real-world tree geometry data and directly addresses the inherent heterogeneity of robot performance. To this end, we introduce a lexicographic design optimization framework for tuple-valued task metrics, allowing robot performance to be represented as a set of multiple criteria ordered according to designer-specified priorities. This representation preserves the semantic meaning of each criterion, enables explicit hierarchical prioritization, and provides a principled alternative to ad-hoc scalarization methods.

Together, these advances establish a reproducible foundation for task-driven robot design optimization. The methods integrate kinematic modeling, performance evaluation, and global optimization into a single, coherent pipeline that extends beyond agricultural manipulation. More broadly, this work supports the practical deployment of modular, reconfigurable manipulators by lowering the barriers to designing task-specific robot designs for the highly cluttered conditions like those found in agricultural robot manipulation.

Thesis Committee Members:
George Kantor (Chair), CMU
Oliver Kroemer, CMU
Zeynep Temel, CMU
Changying (Charlie) Li, University of Florida

Thesis Draft