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Vision-Guided Grasp Planning for Prosthetic Hands in Unstructured Environments

Shifa Sulaiman, Akash Bachhar, Ming Shen, Simon Bøgh

TL;DR

The paper tackles dexterous prosthetic grasping in unstructured environments by marrying vision with per-finger planning. It introduces a modular pipeline that uses BVH-AABB perception to create tight 3D object models, online RRT*-generated fingertip trajectories, and DLS inverse kinematics to compute feasible finger configurations, with per-finger independence enabling adaptive grasps. Validation in simulation and on the Linker Hand O7 demonstrates high segmentation accuracy (~90%), low pose error (~0.13 cm), and grasp success around 90% under real-time constraints, illustrating the approach's practicality for embedded prosthetics. The work lays a foundation for autonomous, dexterous manipulation and points to future work integrating tactile sensing and closed-loop control to further enhance robustness and user independence.

Abstract

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the objects point cloud. Each finger grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. This modular pipeline enables per-finger grasp planning and supports real-time adaptability in unstructured environments. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform.

Vision-Guided Grasp Planning for Prosthetic Hands in Unstructured Environments

TL;DR

The paper tackles dexterous prosthetic grasping in unstructured environments by marrying vision with per-finger planning. It introduces a modular pipeline that uses BVH-AABB perception to create tight 3D object models, online RRT*-generated fingertip trajectories, and DLS inverse kinematics to compute feasible finger configurations, with per-finger independence enabling adaptive grasps. Validation in simulation and on the Linker Hand O7 demonstrates high segmentation accuracy (~90%), low pose error (~0.13 cm), and grasp success around 90% under real-time constraints, illustrating the approach's practicality for embedded prosthetics. The work lays a foundation for autonomous, dexterous manipulation and points to future work integrating tactile sensing and closed-loop control to further enhance robustness and user independence.

Abstract

Recent advancements in prosthetic technology have increasingly focused on enhancing dexterity and autonomy through intelligent control systems. Vision-based approaches offer promising results for enabling prosthetic hands to interact more naturally with diverse objects in dynamic environments. Building on this foundation, the paper presents a vision-guided grasping algorithm for a prosthetic hand, integrating perception, planning, and control for dexterous manipulation. A camera mounted on the set up captures the scene, and a Bounding Volume Hierarchy (BVH)-based vision algorithm is employed to segment an object for grasping and define its bounding box. Grasp contact points are then computed by generating candidate trajectories using Rapidly-exploring Random Tree Star algorithm, and selecting fingertip end poses based on the minimum Euclidean distance between these trajectories and the objects point cloud. Each finger grasp pose is determined independently, enabling adaptive, object-specific configurations. Damped Least Square (DLS) based Inverse kinematics solver is used to compute the corresponding joint angles, which are subsequently transmitted to the finger actuators for execution. This modular pipeline enables per-finger grasp planning and supports real-time adaptability in unstructured environments. The proposed method is validated in simulation, and experimental integration on a Linker Hand O7 platform.

Paper Structure

This paper contains 11 sections, 21 equations, 17 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Materials and Methods
  4. Description of Linker Hand O7 Prosthetic hand
  5. Axis-Aligned Bounding Box (AABB)
  6. AABB-Based Grasp Planning Strategy
  7. Results
  8. Simulation study
  9. Experimental validation
  10. Discussion
  11. Conclusion

Figures (17)

  • Figure S1: Methodology
  • Figure S2: Linker Hand O7
  • Figure S3: Simulation environment showing Linker Hand, cylindrical object, and cameras
  • Figure S4: Simulation results using camera 1 (a)Gazebo initial setup (b)Rviz initial setup (c)Grasping depicted in Gazebo (d)Grasping depicted in Rviz
  • Figure S5: Simulation results using camera 1
  • ...and 12 more figures