Team THOR's adaptive autonomy for disaster response humanoids

McGill, Stephen G.; Yi, Seung‐Joon; Lee, Daniel D.

doi:10.1109/humanoids.2015.7363589

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…Most of the existing approaches have achieved this goal by relying on teleoperation. [16][17][18][19][20][21][22] Supervisory steering and gas commands are sent to the robot to drive the car in Karumanchi et al 23 ; DeDonato and colleagues 24 propose a hybrid solution, with teleoperated steering and autonomous speed control. The velocity of the car, estimated with stereo cameras, is fed back to a proportional integral (PI) controller, whereas light imaging, detection, and ranging (LIDAR), IMU, and visual odometry data support the operator during the steering procedures.…”

Section: Problem Formulation and Proposed Approachmentioning

confidence: 99%

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Autonomous car driving by a humanoid robot

Paolillo

Gergondet

Cherubini

et al. 2017

Journal of Field Robotics

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Enabling a humanoid robot to drive a car requires the development of a set of basic primitive actions. These include walking to the vehicle, manually controlling its commands (e.g., ignition, gas pedal, and steering) and moving with the whole body to ingress/egress the car. We present a sensor‐based reactive framework for realizing the central part of the complete task, consisting of driving the car along unknown roads. The proposed framework provides three driving strategies by which a human supervisor can teleoperate the car or give the robot full or partial control of the car. A visual servoing scheme uses features of the road image to provide the reference angle for the steering wheel to drive the car at the center of the road. Simultaneously, a Kalman filter merges optical flow and accelerometer measurements to estimate the car linear velocity and correspondingly compute the gas pedal command for driving at a desired speed. The steering wheel and gas pedal reference are sent to the robot control to achieve the driving task with the humanoid. We present results from a driving experience with a real car and the humanoid robot HRP‐2Kai. Part of the framework has been used to perform the driving task at the DARPA Robotics Challenge.

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Section: Problem Formulation and Proposed Approachmentioning

confidence: 99%

“…In fact, according to (18), asymptotic convergence of x v andx m to zero implies convergence of x and to zero, achieving the desired path following task.…”

Section: Steering Controlmentioning

confidence: 99%

Autonomous car driving by a humanoid robot

Paolillo

Gergondet

Cherubini

et al. 2017

Journal of Field Robotics

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“…On the other hand, teams that adopted "Atlas" adhered to the bipedal platform though massive update of its kinematic and dynamics implemented by Boston Dynamics (DeDonato et al, 2015;Fallon et al, 2015;Johnson et al, 2015;Knoedler, Dimitrov, Conn, Gennert, & Padir, 2015). Team Thor and Team AIST-Nedo also kept the original bipedal platform, "Thor" and "HRP-2Kai," with the minor changes (Cisneros et al, 2015;McGill, Yi, & Lee, 2015;Yi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Sohn

Jang

et al. 2017

Journal of Field Robotics

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This paper presents a technical overview of Team DRC-Hubo@UNLV's approach to the 2015 DARPA Robotics Challenge Finals (DRC-Finals). The Finals required a robotic platform that was robust and reliable in both hardware and software to complete tasks in 60 min under degraded communication. With this point of view, Team DRC-Hubo@UNLV integrated methods and algorithms previously verified, validated, and widely used in the robotics community. For the communication aspect, a common shared memory approach that the team adopted to enable efficient data communication under the DARPA controlled network is described. A new perception head design (optimized for the tasks of the Finals) and its data processing are then presented. In the motion planning and control aspect, various techniques, such as wheel-driven navigation, zero-momentpoint (ZMP) -based locomotion, and position-based manipulation and controls, are described in this paper. By introducing strategically critical elements and key lessons learned from DRC-Trials 2013 and the testbed of Charleston, we also illustrate how DRC-Hubo has evolved successfully toward the DRC-Finals. C 2017 Wiley Periodicals, Inc.

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“…When these connections become latent or otherwise poor, the role of the human operator must necessarily be reduced. The most common method for accommodating poor communications is to increase autonomous functions, either in the robotic system, in the user interfaces, or both [7,8]. As autonomous functionalities take on more decision making roles for a task, the control mode transitions from teleoperation to supervisory control.…”

Section: Introductionmentioning

confidence: 99%

Supervisory control of a humanoid robot in microgravity for manipulation tasks

Farrell

Strawser

Hambuchen

et al. 2017

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Abstract-Teleoperation is the dominant form of dexterous robotic tasks in the field. However, there are many use cases in which direct teleoperation is not feasible such as disaster areas with poor communication as posed in the DARPA Robotics Challenge, or robot operations on spacecraft a large distance from Earth with long communication delays. Presented is a solution that combines the Affordance Template Framework for object interaction with TaskForce for supervisory control in order to accomplish high level task objectives with basic autonomous behavior from the robot. TaskForce, is a new commanding infrastructure that allows for optimal development of task execution, clear feedback to the user to aid in off-nominal situations, and the capability to add autonomous verification and corrective actions. This framework has allowed the robot to take corrective actions before requesting assistance from the user. This framework is demonstrated with Robonaut 2 removing a Cargo Transfer Bag from a simulated logistics resupply vehicle for spaceflight using a single operator command. This was executed with 80% success with no human involvement, and 95% success with limited human interaction. This technology sets the stage to do any number of high level tasks using a similar framework, allowing the robot to accomplish tasks with minimal to no human interaction.

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Team THOR's adaptive autonomy for disaster response humanoids

Cited by 15 publications

References 24 publications

Autonomous car driving by a humanoid robot

Autonomous car driving by a humanoid robot

Technical Overview of Team DRC‐Hubo@UNLV's Approach to the 2015 DARPA Robotics Challenge Finals

Supervisory control of a humanoid robot in microgravity for manipulation tasks

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