Paper Info
Title
Posture optimization strategy for a statically stable robot traversing rough terrain
Abstract
This paper presents a posture optimization algorithm for a six-legged walking robot. During walking on rough terrain and planning its motion the robot has to determine the horizontal position, distance to the ground, and inclination of the platform. The proposed posture optimization algorithm is based on the Particle Swarm Optimization method. The algorithm increases the stability margin and maximizes the possible motion range of the robot (by maximizing the kinematic margin of each leg). The computation of the kinematic margin is performed by using an analytical function obtained with the Gaussian approximation. The Gaussian-based approximation significantly decreases the time consumed by the algorithm and allows to implement the posture optimization procedure on the real robot. The posture optimization is used as a part of the RRT-based motion planer to find a full-body path while climbing the obstacles.
Year
DOI
Venue
2012
10.1109/IROS.2012.6385548
Intelligent Robots and Systems
Keywords
Field
DocType
Gaussian processes,legged locomotion,motion control,particle swarm optimisation,path planning,position control,robot kinematics,stability,terrain mapping,Gaussian approximation,RRT-based motion planer,analytical function,ground distance,kinematic margin,motion planning,motion range,obstacle climbing,particle swarm optimization,platform inclination,posture optimization strategy,robot horizontal position,rough terrain,six-legged walking robot,stability margin,statically stable robot
Motion control,Kinematics,Computer science,Control theory,Control engineering,Gaussian process,Artificial intelligence,Motion planning,Particle swarm optimization,Computer vision,Robot calibration,Robot kinematics,Robot
Conference
ISSN
ISBN
Citations 
2153-0858
978-1-4673-1737-5
4
PageRank 
References 
Authors
0.49
8
2
Name
Order
Citations
PageRank
Dominik Belter110016.31
Piotr Skrzypczynski214825.07