Legged Robots That Balance

Raibert, Marc H.; Tello, Ernest R.

doi:10.1109/mex.1986.4307016

Cited by 1,953 publications

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“…Although these solutions may be used for qualitative assessments (Ghigliazza et al, 2003), any quantitative result seems highly questionable. Bearing in mind that the spring-mass model is employed to devise general control schemes for running machines (Raibert, 1986;Saranli and Koditschek, 2003) and to investigate animal and human locomotion (He et al, 1991;Farley et al, 1991Farley et al, , 1993Seyfarth et al, 2001), this leaves a rather unsatisfactory state.…”

Section: Closed Form Representations Of the Stance Phase Dynamicsmentioning

confidence: 99%

“…In consequence, not only biomechanical studies investigating hopping (Farley et al, 1991;Seyfarth et al, 2001) or running (He et al, 1991;Farley et al, 1993), but also fast legged robots driven by model-based control algorithms (Raibert, 1986;Saranli and Koditschek, 2003) rely on this plant. Yet still, even for the simple springmass model, parametric insights remain obscured as the dynamics of the stance phase are non-integrable (Whittacker, 1904).…”

Section: Introductionmentioning

confidence: 99%

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Spring-mass running: simple approximate solution and application to gait stability

Geyer

Seyfarth

Blickhan

2005

Journal of Theoretical Biology

251

232

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The planar spring-mass model is frequently used to describe bouncing gaits (running, hopping, trotting, galloping) in animal and human locomotion and robotics. Although this model represents a rather simple mechanical system, an analytical solution predicting the center of mass trajectory during stance remains open. We derive an approximate solution in elementary functions assuming a small angular sweep and a small spring compression during stance. The predictive power and quality of this solution is investigated for model parameters relevant to human locomotion. The analysis shows that (i), for spring compressions of up to 20% (angle of attack X60 ; angular sweep p60 ) the approximate solution describes the stance dynamics of the center of mass within a 1% tolerance of spring compression and 0:6 tolerance of angular motion compared to numerical calculations, and (ii), despite its relative simplicity, the approximate solution accurately predicts stable locomotion well extending into the physiologically reasonable parameter domain. (iii) Furthermore, in a particular case, an explicit parametric dependency required for gait stability can be revealed extending an earlier, empirically found relationship. It is suggested that this approximation of the planar spring-mass dynamics may serve as an analytical tool for application in robotics and further research on legged locomotion. r

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Section: Closed Form Representations Of the Stance Phase Dynamicsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spring-mass running: simple approximate solution and application to gait stability

Geyer

Seyfarth

Blickhan

2005

Journal of Theoretical Biology

251

232

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“…The idea of using dynamic systems for movement generation is not new: motor pattern generators in neurobiology [13,14], pattern generators for locomotion [15,16] [24,25]. As shown in …”

Section: Introductionmentioning

confidence: 99%

Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics

Schaal

Adaptive Motion of Animals and Machines

431

217

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“…For the balance control the attitude can be described by the roll and pitch angles [1]. Normally, the attitude estimation is achieved by the integration of several sensors, such as gyroscopes, inclinometers and accelerometers onboard the robot.…”

Section: Introductionmentioning

confidence: 99%

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

et al. 2015

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In this paper an Extended Kalman Filter (EKF) is used in order to estimate the real state of a humanoid robot (HRP-2 robot in our case study) using the combination of the information coming from the encoders (kinematics) and from the Inertial Measurement Unit (IMU). The integration of the kinematic information into the Kalman filtering process allows a good estimation of the attitude and reduces the complexity of the problem to the use of simple kinematic transformations, even considering the existence of accelerations and mechanical flexibilities in the robot. The EKF estimator presented here is an open solution directly applicable to any humanoid robot, which is the main contribution of our approach. Experimental results are given showing the good performance of the method.Key words: Humanoid robot, Attitude estimation, Kalman filtering, Sensor integration, Kinematics simplicity Otvoreno rješenje za estimaciju položaja humanoidnog robota integracijom senzora i proširenim Kalmanovim filtrom. U ovomečlanku koristi se prošireni Kalmanov filtar (EKF) za estimaciju stanja humanoidnog robota (HRP-2 robot) kombiniranjem informacija iz enkodera (kinematika) i inercijalne mjerne jedinice (IMU). Integracijom kinematičkih informacija u proces Kalmanovog filtra ostvaruje se dobra estimacija položaja i smanjenje složenosti problema jednostavnim kinematičkim transformacijama,čak i u slučaju postojanja akceleracija te mehaničkih savijanja robota. EKF predstavljen u ovomečlanku otvoreno je rješenje primjenjivo na bilo kojem humanoidnom robotu, što je i glavni doprinos predloženog pristupa. Ekperimentalni rezultati pokazuju dobro ponašanje predložene metode.

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Legged Robots That Balance

Cited by 1,953 publications

References 0 publications

Spring-mass running: simple approximate solution and application to gait stability

Spring-mass running: simple approximate solution and application to gait stability

Dynamic Movement Primitives -A Framework for Motor Control in Humans and Humanoid Robotics

Open Solution for Humanoid Attitude Estimation through Sensory Integration and Extended Kalman Filtering

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