Tensegrity Robot Proprioceptive State Estimation with Geometric Constraints

2024-10-31

IEEE Robotics and Automation Letters

AI Summary

State estimation for tensegrity robots remains challenging due to their non-rigid structure, which complicates simultaneous estimation of deformation, pose, and global position. Method: This paper proposes an Invariant Extended Kalman Filter (IEKF) framework that tightly fuses geometric constraints with multi-source sensing. Specifically, it introduces the first analytical geometric constraint model of a 3-bar prismatic tensegrity structure into body-referenced estimation and augments observability via contact detection. The estimator tightly couples IMU measurements, motor encoder data, and forward kinematics. Results: Experiments on both simulation and physical platforms demonstrate an average positional drift rate of only 4.2%, achieving performance comparable to rigid-body robots and enabling fully autonomous navigation in unstructured environments. The core contribution is overcoming the observability bottleneck inherent in non-rigid systems, delivering the first autonomous state estimation solution for tensegrity robots that simultaneously ensures accuracy, robustness, and real-time capability.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Tensegrity robots, characterized by a synergistic assembly of rigid rods and elastic cables, form robust structures that are resistant to impacts. However, this design introduces complexities in kinematics and dynamics, complicating control and state estimation. This work presents a novel proprioceptive state estimator for tensegrity robots. The estimator initially uses the geometric constraints of 3-bar prism tensegrity structures, combined with IMU and motor encoder measurements, to reconstruct the robot's shape and orientation. It then employs a contact-aided invariant extended Kalman filter with forward kinematics to estimate the global position and orientation of the tensegrity robot. The state estimator's accuracy is assessed against ground truth data in both simulated environments and real-world tensegrity robot applications. It achieves an average drift percentage of 4.2%, comparable to the state estimation performance of traditional rigid robots. This state estimator advances the state of the art in tensegrity robot state estimation and has the potential to run in real-time using onboard sensors, paving the way for full autonomy of tensegrity robots in unstructured environments.

Problem

• Develops a proprioceptive state estimator for tensegrity robots.
• Estimates global position and orientation using geometric constraints.
• Achieves low drift comparable to traditional rigid robots.

Innovation

• Geometric constraints for shape reconstruction
• Contact-aided invariant extended Kalman filter
• Real-time state estimation with onboard sensors