Introduction to Humanoid Robotics

Kajita, Shin; Hirukawa, Hirohisa; Harada, Kensuke; Yokoi, Kazuhito

doi:10.1007/978-3-642-54536-8

Cited by 271 publications

(183 citation statements)

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“…Vukobratovic [12] specified a zero moment point (ZMP) with respect to which the dynamic reaction force at the contact of the foot with the ground does not produce any moment in the horizontal direction, and constitutes the point where the total of horizontal inertia and gravity forces equals zero [13].…”

Section: Simple Dynamic Modelmentioning

confidence: 99%

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Ogata

Mita

Shimizu

et al. 2015

IEICE Trans. Inf. & Syst.

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SUMMARYSome unilateral lower-limb amputees, have through continued exertion, increase the foot reaction force of the sound leg. The asymmetric gait with a prosthetic leg may thus negatively affect the musculoskeletal health of the leg on the healthy side. Therefore, it is important for these amputees to learn how to adjust the balance of each foot load in training. The aim of this study is to develop a training support system visualizing floor-reaction forces using a color-depth sensor. The pose of the entire body of the amputee is measured by the depth sensor, and the floor reaction force is estimated based on Zero Moment Point (ZMP), which is calculated using the center of mass of the amputee. Evaluation experiments of the proposed method were performed and they confirmed the effectiveness of the estimation method and the training with the visualization of reaction force.

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Section: Simple Dynamic Modelmentioning

confidence: 99%

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Ogata

Mita

Shimizu

et al. 2015

IEICE Trans. Inf. & Syst.

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“…Nunez planned COM above the ankle to prevent it from falling [22]. Ugurlu proposed a ZMP-based jumping controller design method [23,24]. The ZMP is defined as the point on the ground about which the sum of all the moments of the active forces equals zero.…”

Section: Introductionmentioning

confidence: 99%

Motion Planning for Bipedal Robot to Perform Jump Maneuver

Jiang

Chen

et al. 2018

Applied Sciences

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Abstract:The remarkable ability of humans to perform jump maneuvers greatly contributes to the improvements of the obstacle negotiation ability of humans. The paper proposes a jumping control scheme for a bipedal robot to perform a high jump. The half-body of the robot is modeled as three planar links and the motion during the launching phase is taken into account. A geometrically simple motion was first conducted through which the gear reduction ratio that matches the maximum motor output for high jumping was selected. Then, the following strategies to further exploit the motor output performance was examined: (1) to set the maximum torque of each joint as the baseline that is explicitly modeled as a piecewise linear function dependent on the joint angular velocity; (2) to exert it with a correction of the joint angular accelerations in order to satisfy some balancing criteria during the motion. The criteria include the location of ZMP (zero moment point) and the torque limit. Using the technique described above, the jumping pattern is pre-calculated to maximize the jump height. Finally, the effectiveness of the proposed method is evaluated through simulations. In the simulation, the bipedal robot model achieved a 0.477-m high jump.

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“…There are many research efforts aimed at enabling robots to perform household tasks such as cooking and serving food in daily-life environments [1][2][3][4][5]. Reaching target objects is the beginning of many such household tasks.…”

Section: Introductionmentioning

confidence: 99%

Real-time whole-body motion generation using torso posture regression and center of mass

et al. 2018

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For household humanoid robots, reaching as much workspace as possible with their hands is an important issue because the locations of target objects may range from the floor to above the robot's head. At the same time, to adapt to the constantly-changing household environment, inverse kinematics for the whole body must be solved in real time. In this paper, to achieve real-time motion generation for a humanoid robot, we propose a method of separating the inverse kinematics calculation into simpler problems. Using regression to estimate the torso orientation, we independently solve inverse kinematics for the lower body and both arms. First, using the target pose of both hands as input, we calculate the orientation of the torso and determine the target position of the center of mass considering the reachability of both arms. At each control step, we calculate the joint angles of the lower body from the position of the center of mass, feet poses, and torso orientation. Then, we calculate the joint angles of both arms. In experiments, we apply the proposed method to a human-size humanoid robot for reaching low-height positions while hunkering down. The proposed inverse kinematics solver is ten times faster than the numerical solution using the Jacobian matrix. We also verify the applicability of the proposed method using a sequence of random target positions for the hands as input.

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Introduction to Humanoid Robotics

Cited by 271 publications

References 0 publications

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Training Assist System of a Lower Limb Prosthetic Visualizing Floor-Reaction Forces Using a Color-Depth Sensing Camera

Motion Planning for Bipedal Robot to Perform Jump Maneuver

Real-time whole-body motion generation using torso posture regression and center of mass

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