US 2026/0277222 A1 · CONTROLLING ROBOT USING MULTI-MODEL FORCE TRACKING

Abstract

A system and method for force-aware motion control in a robot utilizing probabilistic force estimation and adaptive control strategies. The system employs an Interacting Multiple-Model (IMM) Kalman Filter to estimate external forces acting on the robot by processing force pseudo-measurements derived from joint encoder data and robot dynamics. The IMM filter utilizes multiple probabilistic filters, each corresponding to a distinct motion mode—swing, stance, or collision—and updates mode probabilities by integrating force pseudo-measurements and robot dynamics to refine external force estimation. The estimated forces are used to refine contact mode classification and dynamically adjust the robot's trajectory in response to disturbances. The controller integrates reflex motion control and admittance motion control, wherein reflex motion control generates real-time corrective actions to counteract unexpected external disturbances, while admittance motion control applies an admittance model to regulate the robot's motion in compliance with external forces.

Claims20

Dependent claims

A probabilistic feedback controller for controlling an operation of a robot using a probabilistic filter employing an interacting multiple-model (IMM) framework, comprising:

at least one processor;
and a memory having instructions stored thereon that, when executed by the at least one processor, cause the controller to:
process digital representations of measurements of a motion of the robot to estimate force pseudo-measurements of an external force acting on the robot during the motion;
execute an IMM filter configured to recursively estimate the external force using multiple probabilistic filters tracking the external force according to the force pseudo-measurements, each of the multiple probabilistic filters is associated with distinct dynamics of a mode of the motion of the robot to produce a force estimation of the mode and a probability of the mode, wherein the IMM filter combines the force estimation of different modes according to their probabilities to produce the estimate of the external force;
and
control the operation of the robot based on the estimate of the external force using one or both of a reflex motion control and an admittance motion control.

3 more elements

A method for controlling a quadrupedal robot using probabilistic force estimation and adaptive motion control, wherein the method uses a processor coupled with stored instructions implementing the method, wherein the instructions, when executed by the processor carry out steps of the method, comprising:

processing digital representations of measurements of the motion of the robot to estimate force pseudo-measurements corresponding to external forces acting on the robot;
executing an interacting multiple-model (IMM) filter to recursively estimate external forces, wherein the IMM filter comprises multiple probabilistic filters, each associated with a distinct motion mode of the robot, including swing, stance, and collision modes;
determining the probability of each motion mode being active based on the estimated force pseudo-measurements and robot dynamics, wherein the IMM filter updates mode probabilities and refines the external force estimation by combining the outputs of the mode-specific probabilistic filters;
and
generating motion control commands based on the estimated external forces, wherein the controller applies one or both of reflex motion control and admittance motion control to modify the trajectory of the quadrupedal robot in response to disturbances and control the robot according the modified trajectory.

2 more elements

Dependencies are inferred from the claim text.

Controlling robot using multi-model force tracking

Life of this patent

Controlling robot using multi-model force tracking

Life of this patent

Abstract

Claims20