Browsing by Author "Do, Brian H."

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Citation - WoS: 0
Citation - Scopus: 0
A Motion Planner for Growing Reconfigurable Inflated Beam Manipulators in Static Environments
(Ieee-inst Electrical Electronics Engineers inc, 2025) Stroppa, Fabıo; Zaghloul, Omar H. A.; Do, Brian H.; Stroppa, Fabio
Soft growing robots have the potential to be useful for complex manipulation tasks and navigation for inspection or search and rescue. They are designed with plant-like properties, allowing them to evert and steer multiple links and explore cluttered environments. However, this variety of operations results in multiple paths, which is one of the biggest challenges faced by classic pathfinders. In this letter, we propose a motion planner based on A$<^>*$ search specifically designed for soft growing manipulators operating on predetermined static tasks. Furthermore, we implemented a stochastic data structure to reduce the algorithm's complexity as it explores alternative paths. This allows the planner to retrieve optimal solutions over different tasks. We ran demonstrations on a set of three tasks, observing that this stochastic process does not compromise path optimality.
Citation - WoS: 9
Task-Specific Design Optimization and Fabrication for Inflated-Beam Soft Robots with Growable Discrete Joints
(Ieee, 2022) Stroppa, Fabıo; Wang, Karen; Do, Brian H.; Stroppa, Fabio; Coad, Margaret M.; Okamura, Allison M.; Liu, C. Karen
Soft robot serial chain manipulators with the capability for growth, stiffness control, and discrete joints have the potential to approach the dexterity of traditional robot arms, while improving safety, lowering cost, and providing an increased workspace, with potential application in home environments. This paper presents an approach for design optimization of such robots to reach specified targets while minimizing the number of discrete joints and thus construction and actuation costs. We define a maximum number of allowable joints, as well as hardware constraints imposed by the materials and actuation available for soft growing robots, and we formulate and solve an optimization problem to output a planar robot design, i.e., the total number of potential joints and their locations along the robot body, which reaches all the desired targets, avoids known obstacles, and maximizes the workspace. We demonstrate a process to rapidly construct the resulting soft growing robot design. Finally, we use our algorithm to evaluate the ability of this design to reach new targets and demonstrate the algorithm's utility as a design tool to explore robot capabilities given various constraints and objectives.