A novel method of biped walking pattern generation with predetermined knee joint motion

Ogura, Yu; Kataoka, T.; Shimomura, Kazushi; Lim, Hun-ok; Takanishi, Atsuo

doi:10.1109/iros.2004.1389838

Cited by 41 publications

(18 citation statements)

References 10 publications

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“…Feet collision is in our implementation computed by intersection of the two feet as 2D polygons. The whole-body, dynamically stable, locomotion pattern is then generated from (7) and (10) through use of the FFT-based pattern generator described in [21], [22]. The pattern generator computes a compensation of the torso trajectory, in a multibody system dynamics approach, leading to the desired ZMP trajectory.…”

Section: Locomotion Controlmentioning

confidence: 99%

Reaching for the unreachable: integration of locomotion and whole-body movements for extended visually guided reaching

Brandão

Jamone²,

Kryczka

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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We present a novel control architecture for the integration of visually guided walking and whole-body reaching in a humanoid robot. We propose to use robot gaze as a common reference frame for both locomotion and reaching, as suggested by behavioral neuroscience studies in humans. A gaze controller allows the robot to track and fixate a target object, and motor information related to gaze control is then used to i) estimate the reachability of the target, ii) steer locomotion, iii) control whole-body reaching. The reachability is a measure of how well the object can be reached for, depending on the position and posture of the robot with respect to the target, and it is obtained from the gaze motor information using a mapping that has been learned autonomously by the robot through motor experience: we call this mapping Reachable Space Map. In our approach, both locomotion and whole-body movements are seen as ways to maximize the reachability of a visually detected object, thus i) expanding the robot workspace to the entire visible space and ii) exploiting the robot redundancy to optimize reaching. We implement our method on a full 48-DOF humanoid robot and provide experimental results in the real world.

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Section: Locomotion Controlmentioning

confidence: 99%

Reaching for the unreachable: integration of locomotion and whole-body movements for extended visually guided reaching

Brandão

Jamone²,

Kryczka

et al. 2013

2013 13th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

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“…Ogura et al proposed a redundant mechanism with predetermined knee joint trajectories as initial walking parameters [8,9]. Morisawa et al proposed a pattern generation method of biped walking with the CoG (Center of Gravity) motion constrained on a parametric surface [10].…”

Section: Introductionmentioning

confidence: 99%

Walking pattern generation involving 3D waist motion for a biped humanoid robot

Liu

Urbann

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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In this paper, a method for generating a natural human-like walking pattern of a humanoid robot is proposed. Inspired by biomechanical studies on human walking, we model the walking pattern of the robot with continuous and differentiable mathematical functions. The proposed walking pattern involves three-dimensional waist motion of the robot instead of restricting the waist motion to a two-dimensional planar surface. Considering the real environment includes not only unevenness but also some degree of inclination that may cause the walking to be unstable, this paper also presents a method of controlling the body posture of the humanoid robot. The posture controller utilizing ankle joints is based on sensory feedback and modifies the reference trajectories in real time in order to stabilize the robot. We have implemented the proposed walking pattern and control method on the humanoid robot NAO. Dynamic walking experiments have verified that the proposed scheme improves the walking stability of the robot.

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“…This is particularly true in the case of bipedal walking robots with several degrees of freedom (DOFs), like the Wabian, by the Waseda University [2]. Wabian's motion patterns are computed by starting from the foot trajectory, the initial waist trajectory, the ZMP trajectory, the knee joint trajectory and a tolerance level moment error [3,4]. The programming of such a complex machine that takes into account so many variables and sensor inputs is easily prone to errors, which can cause the fall of the robot or brisk movements that can damage the robot itself.…”

Section: Introductionmentioning

confidence: 99%