Design of Footstep Planning Controller for Humanoid Robot in Dynamic Environment

2008 ◽  
Author(s):  
Yu Weiwei ◽  
C. Sabourin ◽  
K. Madani
Keyword(s):  
Humanoid Robot ◽  
Dynamic Environment

PUSH RECOVERY THROUGH WALKING PHASE MODIFICATION FOR BIPEDAL LOCOMOTION

2013 ◽  
Vol 10 (03) ◽  
pp. 1350022 ◽  
Author(s):  
ALBERTUS HENDRAWAN ADIWAHONO ◽  
CHEE-MENG CHEW ◽  
BINGBING LIU
Keyword(s):  
Humanoid Robot ◽  
Dynamic Environment ◽  
Bipedal Locomotion ◽  
Bipedal Robot ◽  
Dynamic Simulations ◽  
Phase Modification ◽  
Push Recovery ◽  
Course Of Action ◽  
Recovery Scheme

Push recovery is an important capability for a biped to safely maneuver in a real dynamic environment. In this paper, a generalized push recovery scheme to handle pushes from any direction that may occur at any walking phase is developed. Using the concept of walking phase modification, a series of systematic push recovery scheme that takes into account the severity of the push is presented. The result is that a bipedal robot could adapt to pushes according to the magnitude of disturbance and determine the best course of action. A number of push recovery experiments with different walking phases and push directions have been carried out using a 12-DOF humanoid robot model in dynamic simulations. The versatility and potential of the overall scheme is also demonstrated with the bipedal robot balancing on an accelerating cart.

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

2017 ◽  
Vol 12 (3) ◽  
pp. 330 ◽  
Author(s):  
Paul Erick Mendez Monroy
Keyword(s):  
Embedded System ◽  
Humanoid Robot ◽  
External Disturbance ◽  
Computational Cost ◽  
Dynamic Environment ◽  
Motion Generation ◽  
Recovery Strategy ◽  
Push Recovery ◽  
Neuro Fuzzy ◽  
Recovery Approach

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.

scholarly journals A Cerebellar Internal Models Control Architecture for Online Sensorimotor Adaptation of a Humanoid Robot Acting in a Dynamic Environment

2020 ◽  
Vol 5 (1) ◽  
pp. 80-87 ◽  
Author(s):  
Marie Claire Capolei ◽  
Nils Axel Andersen ◽  
Henrik Hautop Lund ◽  
Egidio Falotico ◽  
Silvia Tolu
Keyword(s):  
Humanoid Robot ◽  
Dynamic Environment ◽  
Internal Models ◽  
Control Architecture ◽  
Sensorimotor Adaptation

Assistive Intelligent Humanoid Robot in Human Environment

2019 ◽  
pp. 887-915
Author(s):  
Zulkifli Mohamed ◽  
Genci Capi
Keyword(s):  
Humanoid Robot ◽  
Target Location ◽  
Dynamic Environment ◽  
Robot Arm ◽  
Human Environment ◽  
Neural Controllers ◽  
Multi Objective Genetic Algorithm ◽  
Wide Range ◽  
Time Distance ◽  
Arm Motion

The number of robots operating in human environments is increasing every day. In order to operate in such environments, the robot must be able to navigate, interact with human, pick and place different objects. This chapter presents a mobile humanoid robot that is able to localize itself, navigate to the target location, and generates the arm motion based on the specific task. The robot utilizes the Laser Range Finder, camera and compass sensor for localization and navigation. In addition, the robot generates the arm motion satisfying multiple motion criteria, simultaneously. This chapter evolves neural controllers that generate the humanoid robot arm motion in dynamic environment optimizing three different objective functions: minimum time, distance and acceleration. In a single ran of Multi-Objective Genetic Algorithm, multiple neural controllers are generate and the same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions.

Humanoid Robot RGB-D SLAM in the Dynamic Human Environment

2020 ◽  
Vol 17 (02) ◽  
pp. 2050009
Author(s):  
Tianwei Zhang ◽  
Yoshihiko Nakamura
Keyword(s):  
Humanoid Robot ◽  
Dynamic Environment ◽  
Point Clouds ◽  
Humanoid Robots ◽  
Human Environment ◽  
The Public ◽  
Visual Sensing ◽  
Working Space ◽  
Localization And Mapping ◽  
Fusion Framework

Unsteady locomotion and the dynamic environment are two problems that block humanoid robots to apply visual Simultaneous Localization and Mapping (SLAM) approaches. Humans are often considered as moving obstacles and targets in humanoid robots working space. Thus, in this paper, we propose a robust dense RGB-D SLAM approach for the humanoid robots working in the dynamic human environments. To deal with the dynamic human objects, a deep learning-based human detector is combined in the proposed method. After the removal of the dynamic object, we fast reconstruct the static environments through a dense RGB-D point clouds fusion framework. In addition to the humanoid robot falling problem, which usually results in visual sensing discontinuities, we propose a novel point clouds registration-based method to relocate the robot pose. Therefore, our robot can continue the self localization and mapping after the falling. Experimental results on both the public benchmarks and the real humanoid robot SLAM experiments indicated that the proposed approach outperformed state-of-the-art SLAM solutions in dynamic human environments.

Assistive Intelligent Humanoid Robot in Human Environment

2015 ◽  
pp. 1-29
Author(s):  
Zulkifli Mohamed ◽  
Genci Capi
Keyword(s):  
Humanoid Robot ◽  
Target Location ◽  
Dynamic Environment ◽  
Robot Arm ◽  
Human Environment ◽  
Neural Controllers ◽  
Multi Objective Genetic Algorithm ◽  
Wide Range ◽  
Time Distance ◽  
Arm Motion

The number of robots operating in human environments is increasing every day. In order to operate in such environments, the robot must be able to navigate, interact with human, pick and place different objects. This chapter presents a mobile humanoid robot that is able to localize itself, navigate to the target location, and generates the arm motion based on the specific task. The robot utilizes the Laser Range Finder, camera and compass sensor for localization and navigation. In addition, the robot generates the arm motion satisfying multiple motion criteria, simultaneously. This chapter evolves neural controllers that generate the humanoid robot arm motion in dynamic environment optimizing three different objective functions: minimum time, distance and acceleration. In a single ran of Multi-Objective Genetic Algorithm, multiple neural controllers are generate and the same neural controller can be employed to generate the robot motion for a wide range of initial and goal positions.

scholarly journals A Biomimetic Control Method Increases the Adaptability of a Humanoid Robot Acting in a Dynamic Environment

2019 ◽  
Vol 13 ◽  
Author(s):  
Marie Claire Capolei ◽  
Emmanouil Angelidis ◽  
Egidio Falotico ◽  
Henrik Hautop Lund ◽  
Silvia Tolu
Keyword(s):  
Humanoid Robot ◽  
Control Method ◽  
Dynamic Environment ◽  
Biomimetic Control

Assistive Humanoid Robot Arm Motion Generation in Dynamic Environment Based on Neural Networks

2015 ◽  
Vol 3 (2) ◽  
pp. 115-121
Author(s):  
Genci Capi ◽  
Zulkifli Mohamed ◽  
Mitsuki Kitani ◽  
Shin-ichiro Kaneko
Keyword(s):  
Neural Networks ◽  
Humanoid Robot ◽  
Dynamic Environment ◽  
Robot Arm ◽  
Motion Generation ◽  
Arm Motion
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