Fall detection and management in biped humanoid robots

Ruiz‐del‐Solar, Javier; Moya, Javier Carretero; Parra‐Tsunekawa, Isao

doi:10.1109/robot.2010.5509550

Cited by 31 publications

(7 citation statements)

References 8 publications

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“…In [6] a fall detector based on the Gait Sensitivity Norm is proposed. Following human movement based search procedure, Ruiz-del Solar et al [28] implemented a low damage fall sequence for soccer robots. However, his work also focuses the falling strategy problem.…”

Section: Humanoid Fall Predictionmentioning

confidence: 99%

Skill Memory in Biped Locomotion

André

Santos

Costa

2015

J Intell Robot Syst

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Robots must be able to adapt their motor behavior to unexpected situations in order to safely move among humans. A necessary step is to be able to predict failures, which result in behavior abnormalities and may cause irrecoverable damage to the robot and its surroundings, i.e. humans. In this paper we build a predictive model of sensor traces that enables early failure detection by means of a skill memory. Specifically, we propose an architecture based on a biped locomotion solution with improved robustness due to sensory feedback, and extend the concept of Associative Skill Memories (ASM) to periodic movements by introducing several mechanisms into the training workflow, such as linear interpolation and regression into a Dynamical Motion Primitive (DMP) system such that representation becomes time invariant and easily parameterizable. The failure detection mechanism applies statistical tests to determine the optimal operating conditions. Both training and failure testing were conducted on a DARwIn-OP inside a simulation environment to assess and validate the failure detection system proposed. Results show that the system performance in terms of the compromise between sensitivity and specificity is similar with and without the proposed mechanism, while achieving a significant data size reduction due to the periodic approach taken.

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Section: Humanoid Fall Predictionmentioning

confidence: 99%

Skill Memory in Biped Locomotion

André

Santos

Costa

2015

J Intell Robot Syst

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“…Renner and Benke implemented a method in which attitude sensor readings are modeled via sinusoids to detect velocity changes [3]. As another example, Ruiz et al processed attitude sensor information using Kalman filters to predict variations in robot configuration [4]. Both methods are efficient for small scale humanoids, but they may not be applicable to humanoids with larger sizes.…”

Section: Related Workmentioning

confidence: 99%

The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

Kormushev

Uğurlu

Colasanto

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-An automated approach is proposed which can analyze ground reaction force data from bipedal walking robots and humans. The input of the automated analysis is the raw data from force sensors mounted in the feet of a robot. The output is detailed information, such as detected single support, double support, and swing phases, their durations, timings of events like heel strikes, properties of the phase transitions and of the robot itself. The proposed approach is generic, parameter-free, model-free, robust, computationally efficient, and applicable for real-time use during walking. It can detect early indications of instability that could lead to a fall of the robot. Three real-world experiments are presented: with a compliant bipedal robot, with a stiff humanoid robot, and with a human subject.

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“…There are many studies on falling detection. Moya et al used a threshold segmentation method to detect robot falls [19]. In addition, Rajesh et al proposed a threshold interpolation method in combination with multiple sensors to detect falls in any direction [20].…”

Section: Introductionmentioning

confidence: 99%

Falling Prediction based on Machine Learning for Biped Robots

Dong

et al. 2021

J Intell Robot Syst

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Biped robots are expected to be able to work in complex environments. However, these robots will inevitably fall at times and such falls may cause injury to the robot itself or to people nearby. Therefore, it is necessary to detect that the robot is falling to be able to warn the robot in sufficient time when it is about to fall and to switch its controller to protect the vulnerable parts of the robot. Modeling and analysis of the biped robot falling problem cannot be fully accurate and many current learning-based methods rely on large quantities of fall data that are difficult to use to train fragile robots. A machine learning-based fall detection method is therefore proposed in this paper. This method requires only a small amount of training data to obtain good fall detection, making the training process on the robot platform much safer. A support vector machine is used to determine the state of the robot and the decision boundary of the stable state is updated during motion to enable the classifier to match the motion capability of the robot.

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Fall detection and management in biped humanoid robots

Cited by 31 publications

References 8 publications

Skill Memory in Biped Locomotion

Skill Memory in Biped Locomotion

The anatomy of a fall: Automated real-time analysis of raw force sensor data from bipedal walking robots and humans

Falling Prediction based on Machine Learning for Biped Robots

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