Giho Jang

Index finger prosthesis control method according to human intention

The paper proposes a control method of an index finger prosthesis by using two electromyographic signals measured on skin surfaces of two muscles (flexor digitorum superficialis and the extensor indicis) in a lower arm. We assume that the mass of the human index finger is sufficiently small for its dynamic effect to be neglected and the initial burst part of electromyographic signal generated by human intention precedes the onset of actual displacement of the index finger. In order to make the control input for the index finger prosthesis, the root mean squaring operation is applied firstly to the measured electromyographic signal, the thresholding operation is secondly utilized to extract the initial burst part, the synergistic signal is thirdly constructed by using the difference between two processed electromyographic signals, and then the desired displacement is derived from the scaled synergistic signal and numerical integration. Physical limits of the utilized actuator such as displacement and speed limits are considered for practical control use. Finally we show the effectiveness of the proposed control method through experimental result.

EMG-Based Continuous Control Scheme With Simple Classifier for Electric-Powered Wheelchair

This paper presents an electromyographic (EMG)-based continuous control scheme including simple classifier for an electric-powered wheelchair, ultimately for quadriplegics. The proposed scheme utilizes three EMG signals as inputs for the muscle-computer interface. Since zygomaticus major muscles and transversus menti muscle of human face are able to move independently as well as to adjust contractile forces voluntarily, the surface EMG signals on these muscles are utilized for the electric-powered wheelchair control system. To extract the envelopes of the signal waveforms and to reflect the moving average activities, the root-mean-squares (RMS) operation and normalization are subsequently employed as initial signal processing. Then, an activation vector containing three normalized RMS signals is obtained in real time. The activation vector is applied to the simple classifier for finding out the motion command. Both desired linear acceleration and angular velocity are yielded from the linear combinations of the classification result and the magnitude of activation vector. Finally, desired wheel velocities of the wheelchair control system are obtained by using the integration and differential inverse kinematics. The effectiveness of the proposed control scheme is verified through several experiments such as avoiding obstacle cones and navigating long distance by the users.

EMG-based continuous control method for electric wheelchair

This paper presents a continuous control method of electric wheelchair based upon surface electromyographic signals (EMG), ultimately, for quadriplegics. The proposed method utilizes two EMG signals as inputs for the muscle-computer interfaces (MCI). Since Zygomaticus major muscles located in the right and left sides of human face are able to excise individually and to control contractile forces voluntarily, the surface EMG signals of both muscles satisfy core requirements for the development of EMG-based electric wheelchair control system, such as independent and continuous speed control of two wheels. For this, the envelopes of the signal waveforms are first extracted to reflect the moving average activities by using RMS (root mean squares) operations. Also, in order to obtain the desired linear and angular velocities of the electric wheelchair, the RMS signals are processed sequentially as follows; normalizing the RMS signals and then determining the control inputs of the electric wheelchair. Finally, the effectiveness of the proposed control scheme is verified through several experiments.

Rock-paper-scissors prediction experiments using muscle activations

Human motion prediction is becoming more and more important issue in the filed of wearable robots or biorobotics. This paper provides an initial experimental result for human motion prediction. In detail, the prediction method for ternary choice among rock-paper-scissors is presented using temporal patterns of muscle activations (Electromyography, in short EMG) controlling hand motion of subject. Initial burst part of EMG is prior to the onset of actual movement by dozens to hundreds milliseconds. Using this property, the proposed method makes the ternary choice prediction among rock-paper-scissors as soon as 10% motion variation of any finger is detected. It is shown experimentally that the success rate of the proposed prediction method is over 95%.

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

This paper presents a technical overview of Team [emailxa0protected]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 [emailxa0protected] 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-moment-point 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.

A humanoid doing an artistic work - graffiti on the wall

Graffiti work using a humanoid with artistic technique can convey the value of artists work to people. Previous work that focused on drawing an image on a canvas accurately has not contained artistic processes like performance, drawing skills and other artists intents at the time of creation. To combine such artistic processes, the work in this paper utilizes whole-body motion of a humanoid to paint an image on a wall using Pointillism. However, a biped humanoid consists of high Degree Of Freedom (DOF) system and is very sensitive to internal and external disturbances from interaction with environments. As is the case when graffitiing on a wall. Most notably, the vibration from impact contacts and mechanical uncertainties limit the humanoid in graffitiing properly. This paper presents an approach to realize drawing a large image on a wall through real-time motion planning for printing, artificial compliance, and a disturbance controller.

Strategies for driving and egress for the vehicle of a humanoid robot in the drc finals 2015

This paper presents various strategies for humanoid vehicle driving and egress tasks. For driving, a tele-operating system that controls a robot based on a human operator’s commands is built. In addition, an autonomous assistant module is developed for the operator. Normal position control can result in severe damage to robots when they egress from vehicles. To prevent this problem, another approach that mixes various joint control techniques is adopted in this study. Additionally, a footplate is newly designed and attached to the vehicle floor for the ground landing phase of the egress task. The attached plate enables the robot to step down onto the ground in a safe manner. For stable locomotion, a balance controller is designed for the humanoid. For the design of the controller, the robot is modeled using an inverted pendulum that consists of a spring and a damper. Then, a state feedback controller (with pole placement and a state observer) is built based on the simplified model. Many approaches that are presented in this paper were successfully applied to a full-sized humanoid, DRC-HUBO+, in the DARPA Robotics Challenge Finals, which were held in the United States in 2015.

Team DRC-Hubo@UNLV in 2015 DARPA Robotics Challenge Finals

This chapter presents a technical overview of Team DRC-Hubo@UNLVs 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-moment point (ZMP)-based locomotion, and position-based manipulation and controls, are described in this chapter. 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.

Balancing via Position Control

This chapter describes the balancing scheme based on the position control using the kinematic resolution method of center of mass (CoM) Jacobian. First, the simplified rolling sphere model is introduced for bipedal robots. Second, the kinematic resolution method of CoM Jacobian having the embedded task motion makes a humanoid robot to be balanced automatically during the execution of embedded task motion; indeed it offers the ability of whole-body coordination Y. Choi ( ) School of Electrical Engineering, Hanyang University, Ansan, South Korea e-mail: cyj@hanyang.ac.kr Y. Oh Center for Robotics Research, Korea Institute of Science and Technology (KIST), Seoul, South Korea e-mail: oyh@kist.re.kr; yonghwanoh.87@gmail.com G. Jang Department of Mechanical Engineering, University of Nevada, Las Vegas, Nevada, USA e-mail: smile.giho@gmail.com

Infinitely differentiable and continuous trajectory planning for mobile robot control

A smooth trajectory planning is necessary for smooth motion control of robots. In this paper, a conventional cubic trajectory planning for symmetric curve (S-curve) is extended to an infinitely differentiable and continuous trajectory planning which is derived from smooth jerk functions. Simulation results are provided to show the effectiveness of the smooth trajectory planning proposed for mobile robots.