Neuromuscular model-based sensing and control paradigm for a robotic leg

• US 9,975,249 B2
• Filed: 12/22/2015
• Issued: 05/22/2018
• Est. Priority Date: 04/18/2012
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Herr, Hugh M., Endo, Ken, Krishnaswamy, Pavitra, Markowitz, Jared, Eilenberg, Michael Frederick, Wang, Jing
• Primary Examiner(s): Mott, Adam R
• Application Number:
  US14/978,445
• Publication Number:
  US 20160207201A1
• Time in Patent Office: 882 Days
• CPC Class Codes: B25J 9/1694 characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
• CPC Class Codes: B62D 57/032 with alternately or sequentially lifted supporting base and legs; with alternately or sequentially lifted feet or skid B62D57/024 takes precedence
• CPC Class Codes: G05B 2219/40324 Simulation, modeling of muscle, musculoskeletal dynamical system
• CPC Class Codes: Y10S 901/01 Mobile robot

Abstract

A neuromuscular model-based controller for a robotic limb having at least one joint includes a neuromuscular model having a muscle model, muscle geometry and reflex feedback loop to determine at least one torque or impedance command to be sent to the robotic limb. One or more parameters that determine relation between feedback data and activation of the muscle model are adjusted consequent to sensory data from at least one of an intrinsic sensor and an extrinsic sensor. A controller in communication with the neuromuscular model is configured to receive the at least one torque or impedance command and controls at least one of position, torque and impedance of the robotic limb joint.

Claims

1. A neuromuscular model-based controller for controlling at least one robotic limb joint of a robotic limb, the controller comprising:

a) at least one extrinsic sensor that detects at least one extrinsic signal of a subject wearing the robotic limb;

b) a neuromuscular model in communication with the at least one extrinsic sensor, the neuromuscular model comprising parameters that are adjusted by activation dynamics in response to the at least one extrinsic signal from the extrinsic sensor, the neuromuscular model thereby determining at least one of a position command, a torque command and an impedance command; and

c) a control system in communication with the neuromuscular model, whereby the control system receives at least one of the position command, the torque command and the impedance command from the neuromuscular model and controls at least one of position, torque and impedance of the robotic limb joint.

2. The neuromuscular model-based controller of claim 1, wherein the neuromuscular model includes a muscle model, whereby parameters that determine the relation between muscle feedback data and activation of the muscle model are adjusted consequent to the at least one extrinsic signal of the extrinsic sensor, wherein the muscle feedback data include at least one of muscle length, muscle contractile velocity, and muscle force.

3. The neuromuscular model-based controller of claim 1, wherein the at least one extrinsic signal is an electromyographic signal.

4. The neuromuscular model-based controller of claim 1, wherein the position command is generated by integration of the torque command.

5. The neuromuscular model-based controller of claim 3 wherein the control system compares the position command to a robotic joint state and applies a feedback control to the robotic limb.

6. The neuromuscular model-based controller of claim 1, wherein the extrinsic sensor is a brain sensor.

7. The neuromuscular model-based controller of claim 1, wherein the extrinsic sensor is an extrinsic neural sensor.

8. The neuromuscular model-based controller of claim 7, wherein the extrinsic neural sensor includes at least one member selected from the group consisting of:

an electrode to measure an electromyographic signal of biological limb musculature;

peripheral neural implants for efficient sensing of motor neuron axons; and

central brain implants for sensing of brain motor commands.

9. The neuromuscular model-based controller of claim 1, further including a reflex architecture.