Capture Point: A Step toward Humanoid Push Recovery

Pratt, Jerry; Carff, John; Drakunov, S.; Goswami, Ambarish

doi:10.1109/ichr.2006.321385

Cited by 900 publications

(646 citation statements)

References 12 publications

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“…We have used the Capture Point [19] trajectory to evaluate the steadiness of the tipping motion. The capture point is a point on the ground to which the biped, when subjected to a perturbation, can step to and stop without requiring another step.…”

Section: Fall Trigger Vs Control Triggermentioning

confidence: 99%

Generalized direction changing fall control of humanoid robots among multiple objects

Nagarajan

Goswami

2010

2010 IEEE International Conference on Robotics and Automation

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Abstract-Humanoid robots are expected to share human environments in the future and it is important to ensure safety of their operation. A serious threat to safety is the fall of a humanoid robot, which can seriously damage both the robot and objects in its surrounding. This paper proposes a strategy for planning and control of fall. The controller's objective is to prevent the robot from hitting surrounding objects during a fall by modifying its default fall direction.We have earlier presented such a direction-changing fall controller in [1]. However, the controller was applicable only when the robot's surrounding contained a single object. In this paper we introduce a generalized approach to humanoid falldirection control among multiple objects. This new framework algorithmically establishes a desired fall direction through assigned scores, considers a number of control options, and selects and executes the best strategy. The fall planner is also able to select "No Action" as the best strategy, if appropriate. The controller is interactive and is applicable for fall occurring during upright standing or walking. The fall performance is continuously tracked and can be improved in real-time. The planning and control algorithms are demonstrated in simulation on an ASIMO-like humanoid robot.

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Section: Fall Trigger Vs Control Triggermentioning

confidence: 99%

Generalized direction changing fall control of humanoid robots among multiple objects

Nagarajan

Goswami

2010

2010 IEEE International Conference on Robotics and Automation

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“…In most of the locomotion control related methodologies, applied to the full-scale humanoid robots, the whole robot is simplified either to the single mass [1], [2] or to the set of masses [3]. Most of the methods eventually results in the gait pattern, which is the set of reference trajectories that completely define the spatial configuration of the robot in time.…”

Section: Introductionmentioning

confidence: 99%

“…The first approach is based on the single-mass models while the second is based on the multibody dynamics. The former one pays strong attention to the natural dynamics of the locomotion and uses simple models, often being an inverted pendulum with extension of the foot to account for the ground contact or the rotating mass to account for the upper body inertia [2], [4], [5]. However, since they do not include the full body dynamics in the motion generation process, they are prone to the modeling errors resulting from the self-motion and have to rely on the feedback control during the execution of motion.…”

Section: Introductionmentioning

confidence: 99%

Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration

Kryczka

Kormushev

Hashimoto

et al. 2013

2013 10th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

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-We propose a two-stage gait pattern generation scheme for the full-scale humanoid robots, that considers the dynamics of the system throughout the process. The fist stage is responsible for generating the preliminary motion reference, such as step position, timing and trajectory of Center of Mass (CoM), while the second stage serves as dynamics filter and modifies the initial references to make the pattern stable on the full-scale multi-degree-of-freedom humanoid robot. The approach allows employment of easy to use models for motion generation, yet the use of the dynamics filtering ensures that the pattern is safe to execute on the real-world humanoid robot. The paper contains description of two approaches used in the first and second stage, as well as experimental results proving the effectiveness of the method. The fast calculation time and the use of the system's dynamic state as initial conditions for pattern generation makes it a good candidate for the real-time gait pattern generator.

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“…[11], [17] [7]. Generally, these strategies are classified into three groups: Ankle, hip and stepping.…”

Section: Introductionmentioning

confidence: 99%

Walking Motion Generation and Neuro-Fuzzy Control with Push Recovery for Humanoid Robot

Méndez-Monroy

2017

INT J COMPUT COMMUN

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Push recovery is an essential requirement for a humanoid robot with the objective of safely performing tasks within a real dynamic environment. In this environment, the robot is susceptible to external disturbance that in some cases is inevitable, requiring push recovery strategies to avoid possible falls, damage in humans and the environment. In this paper, a novel push recovery approach to counteract disturbance from any direction and any walking phase is developed. It presents a pattern generator with the ability to be modified according to the push recovery strategy. The result is a humanoid robot that can maintain its balance in the presence of strong disturbance taking into account its magnitude and determining the best push recovery strategy. Push recovery experiments with different disturbance directions have been performed using a 20 DOF Darwin-OP robot. The adaptability and low computational cost of the whole scheme allows is incorporation into an embedded system.

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Capture Point: A Step toward Humanoid Push Recovery

Cited by 900 publications

References 12 publications

Generalized direction changing fall control of humanoid robots among multiple objects

Generalized direction changing fall control of humanoid robots among multiple objects

Hybrid gait pattern generator capable of rapid and dynamically consistent pattern regeneration

Walking Motion Generation and Neuro-Fuzzy Control with Push Recovery for Humanoid Robot

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