Predicted Step Viability: a stability criterion for biped gait

Rossi, Luis Filipe; Parik-Americano, Pedro; Simões, Ivan Fischman Ekman; Forner-Cordero, Arturo

doi:10.1007/s40430-019-2052-9

Cited by 4 publications

(4 citation statements)

References 39 publications

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“…In the PSV, the current step is planned to guarantee that the next step exists on which the robot is capable of either reducing or maintaining the capture point distance [10]. This multistage optimization problem guarantees the stability of the robot and the viability of subsequent steps, along with minimizing energy consumption.…”

Section: Predicted Step Viabilitymentioning

confidence: 99%

“…Finally, segment 3 models the torso of the robot, which contains most of the mass and thus governs the CoM position. The PSV method was implemented as described by Rossi et al in [10], and a set of simulations were performed to map the initial and end-step conditions of the biped for each initial configuration. The result of the algorithm, which means whether or not the robot managed to reduce the distance to the capture point (i.e., the step is recoverable), is also registered for posterior analysis.…”

Section: Robot Modelmentioning

confidence: 99%

“…Later, the predicted step viability (PSV) was proposed in [10], inspired by the N-SC idea and the human ability to recover from perturbations, such as tripping. In this way, a gait is considered stable if the biped is able to reach a capture point in a finite time.…”

Section: Introductionmentioning

confidence: 99%

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Biped Gait Stability Classification Based on the Predicted Step Viability

Parik-Americano,

Igual,

Driemeier

et al. 2024

Biomimetics

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In this paper, we address the challenge of ensuring stability in bipedal walking robots and exoskeletons. We explore the feasibility of real-time implementation for the Predicted Step Viability algorithm (PSV), a complex multi-step optimization criterion for planning future steps in bipedal gait. To overcome the high computational cost of the PSV algorithm, we performed an analysis using 11 classification algorithms and a stacking strategy to predict if a step will be stable or not. We generated three datasets of increasing complexity through PSV simulations to evaluate the classification performance. Among the classifiers, k Nearest Neighbors, Support Vector Machine with Radial Basis Function Kernel, Decision Tree, and Random Forest exhibited superior performance. Multi-Layer Perceptron also consistently performed well, while linear-based algorithms showed lower performance. Importantly, the use of stacking did not significantly improve performance. Our results suggest that the feature vector applied with this approach is applicable across various robotic models and datasets, provided that training data is balanced and sufficient points are used. Notably, by leveraging classifiers, we achieved rapid computation of results in less than 1 ms, with minimal computational cost.

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Section: Predicted Step Viabilitymentioning

confidence: 99%

Section: Robot Modelmentioning

confidence: 99%

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Biped Gait Stability Classification Based on the Predicted Step Viability

Parik-Americano,

Igual,

Driemeier

et al. 2024

Biomimetics

Self Cite

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“…Also derived from the capture point concept, Rossi (ROSSI et al, 2019) developed a stability criterion: the Predicted Step Viability. This criterion uses the full dynamics of the simplified robot (such as the five link model Rabbit (CHEVALLEREAU et al, 2003)) to design the controller.…”

Section: Literature Reviewmentioning

confidence: 99%

Biped gait controller with active perturbation recovery.

Simões¹

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The goals of this work are to develop a biped gait controller and an active fall recovery algorithm, both based on the capture point stability theory with the criterion of N-step capturability, validate this algorithms via simulations; and to design a biped robot that represents gait on the sagittal plane, capable of serving as a test-bed for legged locomotion research. The effectiveness of the controller and fall recovery are assessed through simulations, with the designed robot, that imposes different types of perturbations, such as: pushes, ramps and irregular terrain. The controller ability to modulate the robot's velocity is also demonstrated. The fall recovery algorithm is addressed as a solution to increase robustness without significant detriment to energy efficiency. The controller was proved successful in the simulations, it only demonstrated speed limitation on descending slopes. The fall recovery manages to significantly increase robustness of a standard, energy efficient gait.

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