Traction Monitoring for Collision Detection with Legged Robots

Quinlan, Michael; Murch, Craig L.; Middleton, Richard H.; Chalup, Stephan K.

doi:10.1007/978-3-540-25940-4_33

Cited by 9 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On a legged robot, Quinlan et al [29] discuss a method for calibrating the odometry based on the robot's vision-based localization. These methods all rely on already calibrated sensor models to make the action models more accurate.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Towards autonomous sensor and actuator model induction on a mobile robot

Stronger

Stone

2006

Connection Science

View full text Add to dashboard Cite

This article presents a novel methodology for a robot to autonomously induce models of its actions and sensors called asami (Autonomous Sensor and Actuator Model Induction). While previous approaches to model learning rely on an independent source of training data, we show how a robot can induce action and sensor models without any well-calibrated feedback. Specifically, the only inputs to the asami learning process are the data the robot would naturally have access to: its raw sensations and knowledge of its own action selections. From the perspective of developmental robotics, our robot's goal is to obtain self-consistent internal models, rather than to perform any externally defined tasks. Furthermore, the target function of each model-learning process comes from within the system, namely the most current version of another internal system model. Concretely realizing this model-learning methodology presents a number of challenges, and we introduce a broad class of settings in which solutions to these challenges are presented. asami is fully implemented and tested, and empirical results validate our approach in a robotic testbed domain using a Sony Aibo ERS-7 robot.

show abstract

Section: Discussion and Related Workmentioning

confidence: 99%

Towards autonomous sensor and actuator model induction on a mobile robot

Stronger

Stone

2006

Connection Science

View full text Add to dashboard Cite

show abstract

“…Furthermore, the motion data of a robot, especially of a legged robot can be uncertain. Noisy motion data are usually caused by slippage, traction loss, or collisions [92,51]. This uncertainty in odometry can result in an erroneous speed model of the object to track.…”

Section: Egocentric World Modelingmentioning

confidence: 99%

“…As an example, when a robot perceives a flag in a certain distance and angle and the robot is moving, the distance and angle of the flag to the robot change. The robot's uncertainty about the flag distance and angle increases, due to motion noise or slippage [92]. Fig.…”

Section: Constraint Prediction Stepmentioning

confidence: 99%

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

Göhring

2010

Künstl Intell

View full text Add to dashboard Cite

Mobile autonomous robotics is a very young and complex field of research. Only in recent decades have robots become able to explore, to move, navigate and to interact with their environment.Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms have become very popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within this thesis a new approach using the concept of spatial percept-relations for cooperative environment modeling is presented and evaluated. As a second contribution, constraint based localization techniques will be introduced for having a robot or a group of robots efficiently localized and to model their environment.v Zusammenfassung Die mobile Robotik stellt ein sehr junges und komplexes Forschungsfelder unserer Zeit dar. Innerhalb der letzten Jahrzehnte wurde es Robotern möglich, sich innerhalb ihrer Umgebung zu bewegen, zu navigieren und mit ihrer Umwelt zu interagieren.Aufgrund der Tatsache, dass die Welt von Unsicherheit geprägt ist und ein Roboter immer nur partielle Information über sie erhalten kann, wurden probabilistische Navigationsverfahren entwickelt, mit denen sich Roboter lokalisieren und Objekte ihrer Umgebung modellieren können. Weiterhin wurden in letzter Zeit Verfahren untersucht, die die kooperative Exploration der Umgebung durch eine Gruppe von Robotern zum Ziel haben. In der vorliegenden Arbeit wird ein neuartiges Konzept, welches sich Perzeptrelationen für die kooperative Umweltmodellierung zu Nutze macht, vorgestellt und evaluiert. Einen zweiten Beitrag der Arbeit stellen constraintbasierte Lokalisierungstechniken dar, die es einem oder mehreren Robotern auf effiziente Art und Weise ermöglichen, sich zu lokalisieren und ihre Umwelt zu modellieren.vii

show abstract

“…In their work they used a supervised learning approach, feeding a decision tree with labeled data collected while the robot was marching in place on different types of surfaces. Quinlan et al [10,11] showed that it is possible to learn normal motion of the limbs for distinct walking directions and used this to detect slippage of the legs and collisions of the robot with its environment. The main drawbacks of this method are that it is not generalized for omnidirectional walking and that it is not independent of collision point.…”

Section: Related Workmentioning

confidence: 99%

A Robust Statistical Collision Detection Framework for Quadruped Robots

Meriçli

Akın

2009

RoboCup 2008: Robot Soccer World Cup XII

View full text Add to dashboard Cite

Abstract. In order to achieve its tasks in an effective manner, an autonomous mobile robot must be able to detect and quickly recover from collisions. This paper proposes a new solution to the problem of detecting collisions during omnidirectional motion of a quadruped robot equipped with an internal accelerometer. We consider this as an instance of general signal processing and statistical anomaly detection problems. We find that temporal accelerometer readings examined in the frequency domain are good indicators of regularities (normal motion) and novel situations (collisions). In the course of time, the robot builds a probabilistic model that captures its proprioceptive properties while walking without obstruction and uses that model to determine whether there is an abnormality in the case of an unfamiliar pattern. The approach does not depend on walk characteristics and the walking algorithm used, and is insensitive to the surface texture that the robot walks on as long as the surface is flat. The experiments demonstrate quite fast and successful detection of collisions independent of the point of contact with an acceptably low false positive rate.

show abstract

Traction Monitoring for Collision Detection with Legged Robots

Cited by 9 publications

References 10 publications

Towards autonomous sensor and actuator model induction on a mobile robot

Towards autonomous sensor and actuator model induction on a mobile robot

Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments

A Robust Statistical Collision Detection Framework for Quadruped Robots

Contact Info

Product

Resources

About