Honda Motor Co., Ltd.

Northwestern University

Florida Institute for Human and Machine Cognition, Inc.

Florida Institute for Human & Machine Cognition, Inc. (ihmc)

Ambarish Goswami

Umashankar Nagarajan

Gabriel Aguirre-Ollinger

Jimmy Chiu

Yoshiaki Sakagami

Sung-Hee Lee

Seung-kook Yun

Muhammad E Abdallah

Jerry E Pratt

Michael A Peshkin

James W Post, Ii

J Edward Colgate

John Rebula

Takashi Bannai

Kangkang Yin

Fabian Canas

Vinutha Kallem

Byung-Joo Kim

Lowell Brown

Seungkook Yun

Shivaram Kalyanakrishnan

The control method for lower-limb assistive exoskeletons assists human movement by producing a desired dynamic response on the human leg. Wearing the exoskeleton replaces the leg's natural admittance with the equivalent admittance of the coupled system formed by the leg and the exoskeleton. The control goal is to make the leg obey an admittance model defined by target values of natural frequency, resonant peak magnitude and zero-frequency response. The control achieves these objectives objective via positive feedback of the leg's angular position and angular acceleration. The method achieves simultaneous performance and robust stability through a constrained optimization that maximizes the system's gain margins while ensuring the desired location of its dominant poles.

Admittance shaping controller for exoskeleton assistance of the lower extremities

A resistive exoskeleton control system has a controller generating a positive resistance by shaping a closed loop integral admittance of a coupled human exoskeleton system wherein a frequency response magnitude of the integral admittance is lower than that of a natural human joint for desired frequencies of interest and generating an assistance ratio of approximately zero for the desired frequencies of interest.

Resistive exoskeleton control design framework

An assistive exoskeleton control system has a controller generating a positive assistance by shaping a closed loop integral admittance of a coupled human exoskeleton system to a desired assistance ratio Aby modifying a control transfer function using a cut-off frequency of a low pass filter.

Integral admittance shaping for an exoskeleton control design framework

An apparatus for assisting the balance of a user is disclosed herein. The apparatus includes a first control moment gyroscope (CMG) and a second CMG configured as a scissor pair. The first CMG includes a first flywheel and a second flywheel, a first motor that rotates the first and second flywheels, a first gimbal supporting the first and second flywheel, and a first gimbal servo to rotate the first gimbal. The second CMG includes a third flywheel and a forth flywheel, a second motor that rotates the third and fourth flywheels, a second gimbal supporting the third and fourth flywheels, and a second gimbal servo to rotate the second gimbal. The apparatus additionally includes a gyroscope controller controlling the first gimbal servo and the second gimbal servo.

Wearable scissor-paired control moment gyroscope (SP-CMG) for human balance assist

A controller and control method assists a driver with backing up of a vehicle with an attached trailer. The vehicle has a front axle with steerable front wheels controlled by the driver and a rear axle with non-steerable rear wheels. The trailer has a front axle with non-steerable front wheels and a rear axle with steerable rear wheels controlled by a trailer steering controller. The controller receives an operator-controlled vehicle steering angle and a measured hitch angle. The controller determines a trailer steering angle based on the operator-controller vehicle steering angle and the measured hitch angle. The controller continuously controls the trailer (e.g., via a steering angle of the rear wheels) to maintain a trajectory with substantially no lateral slippage.

Reverse drive assist for long wheelbase dual axle trailers

A momentum-based balance controller controls a humanoid robot to maintain balance. The balance controller derives desired rates of change of linear and angular momentum from desired motion of the robot. The balance controller then determines desired center of pressure (CoP) and desired ground reaction force (GRF) to achieve the desired rates of change of linear and angular momentum. The balance controller determines admissible CoP, GRF, and rates of change of linear and angular momentum that are optimally close to the desired value while still allowing the robot to maintain balance. The balance controller controls the robot to maintain balance based on a human motion model such that the robot's motions are human-like. Beneficially, the robot can maintain balance even when subjected to external perturbations, or when it encounters non-level and/or non-stationary ground.

Momentum-based balance controller for humanoid robots on non-level and non-stationary ground

A robot controller controls a robot during a fall to reduce the damage to the robot upon impact. The robot controller causes the robot to achieve a tripod-like posture having three contact points (e.g., two hands and one foot) so that the robot motion in arrested with its center of mass (CoM) high above the ground. This prevents a large impact caused by the transfer of potential energy to kinetic energy during the fall. The optimal locations of the three contacts are learned through a machine learning algorithm.

Humanoid fall damage reduction

A jackknife warning condition controller and control method notifies a driver of a potential jackknife situation while backing up a vehicle with an attached trailer. The vehicle has a front axle with steerable front wheels controlled by the driver and a rear axle with non-steerable rear wheels. The trailer has a front axle with non-steerable front wheels and a rear axle with steerable rear wheels controlled by a trailer steering controller. The jackknife controller receives an operator-controlled vehicle steering angle and a measured hitch angle. The jackknife warning condition controller determines a directional jackknife warning condition and compares the measured hitch angle with the determined directional jackknife warning condition. If the measured hitch angle satisfies the directional jackknife warning condition then a notification is sent to the driver.

Jackknife condition for backing up of a vehicle attached to a long wheelbase dual axle trailers

A method for stability control may include receiving vehicle characteristics of a vehicle of a vehicle-trailer system, trailer characteristics of a trailer of the vehicle-trailer system, or steering inputs for the vehicle. The method may include determining a prediction based on yaw rate deviation for the vehicle determined from the vehicle characteristics, trailer characteristics, or steering inputs or hitch rate oscillation of a hitch coupling the vehicle to the trailer determined from the vehicle characteristics, trailer characteristics, or steering inputs, where the prediction may be indicative of a likelihood of instability for the vehicle-trailer system. The method may include generating control actions based on the prediction and hitch rate feedback from the hitch of the vehicle-trailer system or lateral hitch force feedback.

Method and system for stability control

A humanoid robot fall controller controls motion of a robot to minimize damage when it determines that a fall is unavoidable. The robot controller detects a state of the robot during the fall and determines a desired rotational velocity that will allow the robot to re-orient itself during the fall to land on a predetermined target body segment (e.g., a backpack). The predetermined target body segment can be specially designed to absorb the impact of the fall and protect important components of the robot.

Damage reduction control for humanoid robot fall

A robot controller controls a robot to maintain balance in response to an external disturbance (e.g., a push) on level or non-level ground. The robot controller determines a predicted stepping location for the robot such that the robot will be able to maintain a balanced upright position if it steps to that location. As long as the stepping location predicted stepping location remains within a predefined region (e.g., within the area under the robot's feet), the robot will maintain balance in response to the push via postural changes without taking a step. If the predicted stepping location moves outside the predefined region, the robot will take a step to the predicted location in order to maintain its balance.

Humanoid robot push recovery on level and non-level ground

A system and method is disclosed for predicting a fall of a robot having at least two legs. A learned representation, such as a decision list, generated by a supervised learning algorithm is received. This learned representation may have been generated based on trajectories of a simulated robot when various forces are applied to the simulated robot. The learned representation takes as inputs a plurality of features of the robot and outputs a classification indicating whether the current state of the robot is balanced or falling. A plurality of features of the current state of the robot, such as the height of the center of mass of the robot, are determined based on current values of a joint angle or joint velocity of the robot. The current state of the robot is classified as being either balanced or falling by evaluating the learned representation with the plurality of features of the current state of the robot.

Machine learning approach for predicting humanoid robot fall

A system and method is disclosed for controlling a robot having at least two legs, the robot falling down from an upright posture and the robot located near a plurality of surrounding objects. A plurality of predicted fall directions of the robot are determined, where each predicted fall direction is associated with a foot placement strategy, such as taking a step, for avoiding the surrounding objects. The degree to which each predicted fall direction avoids the surrounding objects is determined. A best strategy is selected from the various foot placement strategies based on the degree to which the associated fall direction avoids the surrounding objects. The robot is controlled to implement this best strategy.

Humanoid fall direction change among multiple objects

A system and method is disclosed for controlling a robot that is falling down from an upright posture. Inertia shaping is performed on the robot to avoid an object during the fall. A desired overall toppling angular velocity of the robot is determined. The direction of this velocity is based on the direction from the center of pressure of the robot to the object. A desired composite rigid body inertia of the robot is determined based on the desired overall toppling angular velocity. A desired joint velocity of the robot is determined based on the desired composite rigid body inertia. The desired joint velocity is also determined based on a composite rigid body inertia Jacobian of the robot. An actuator at a joint of the robot is then controlled to implement the desired joint velocity.

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Fremont, CA, United States of America

Ambarish Goswami