Team SNU’s Control Strategies to Enhancing Robot’s Capability: Lessons from the DARPA Robotics Challenge Finals 2015

Kim, Sang-Hyun; Kim, Min-Gon; Lee, Jimin; Hwang, Sung Hwan; Chae, Joonbo; Park, Beomyeong; Cho, Hyunbum; Sim, Jaehoon; Jung, Jaesug; Lee, Ho-Sang; Shin, Seokmin; Kim, Min Soo; Ahn, Joonwoo; Choi, Wonje; Lee, Yisoo; Park, Sumin; Oh, Jiyong; Lee, Yongjin; Lee, Sangkuk; Lee, Myunggi; Yi, Sangyup; Chang, Ken; Kwak, Nojun; Park, Jaeheung

doi:10.1007/978-3-319-74666-1_10

Cited by 5 publications

(1 citation statement)

References 29 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%

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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