Gait optimization of biped robots based on human motion analysis

Lim, In-Sik; Kwon, Ohung; Park, Jong Hyeon

doi:10.1016/j.robot.2013.08.014

Cited by 40 publications

(24 citation statements)

References 9 publications

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“…Therefore, monitoring and controlling the ZMP/CoP is fundamental to assure the robot's balance during its walking cycle. Mimicking a human's ZMP trajectory on a bipedal robot has been proven to be a good strategy in order to obtain a more stable and human-like gait cycle compared to a traditional approach (Ferreira et al, 2009a(Ferreira et al, , 2009b(Ferreira et al, , 2010Griffin et al, 2018;Lima et al, 2014;. While a traditional ZMP trajectory tries to maintain it at the center of the foot during the single support phase, a human ZMP/CoP trajectory presents a gradual weight transfer from the heel to the toe during the single support phase (see Figure 1).…”

Section: Related Workmentioning

confidence: 99%

The NAO Robot in Slippery Scenarios: A Strategy

Franco

Coimbra

Crisóstomo

et al. 2021

J INFORM SYSTEMS ENG

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In this paper, a strategy for adapting the NAO robot to different floors with different slipperiness degrees is presented while following a desired human-like Zero Moment Point trajectory. The robot's gait is generated based on the HRSP software package and it aims to be as human-like as possible. The gait parameters such as the step length and walking speed are optimized in order to generate gaits with adequate RCoF for the floor's ACoF, which minimizes the slipping probability, and as such avoiding undesired falls. This choice of gait parameters is based on the analysis of the ground reaction forces and human behavior. Also, gait adaptation is further improved in order to follow a desired Zero Moment Point trajectory, through the use of a controller that offsets the hip and ankle joint angles. The novelty of this work lies on the fact that machine learning techniques are used to adapt the gait parameters and joint corrections to make the robot more resistant to both slipping and external disturbances.

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Section: Related Workmentioning

confidence: 99%

The NAO Robot in Slippery Scenarios: A Strategy

Franco

Coimbra

Crisóstomo

et al. 2021

J INFORM SYSTEMS ENG

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“…Furthermore, provided that the linearization ofẋ(t) = f (x(t)) + g(x(t))u(t) about ξ opt,i is controllable on [0, T ], the mapping β(·) is invertible. Thus, a Newton method for root finding is applied on the final state constraint equation β(x T ) − x f = 0 in order to find the value of x T such that the solution to (9) is equivalent to the solution to (5). In particular, at the iteration i + 1, we update the value of x T according to the following rule:…”

Section: Enforcing the Final State Constraintmentioning

confidence: 99%

“…Typical cost functions are (i) the distance from a desired state-input curve (which does not satisfy the dynamics) and (ii) the energy injected into the system. For example, for humanoid robot design, the distance from a desired (but unfeasible) human-like walking pattern ( [4], [5]) is often considered. Additional challenges arise when the trajectory generation problem is addressed for (underactuated) mechanical systems with impacts.…”

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confidence: 99%

An optimal control approach to the design of periodic orbits for mechanical systems with impacts

Spedicato

Notarstefano

2017

Nonlinear Analysis: Hybrid Systems

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In this paper we study the problem of designing periodic orbits for a special class of hybrid systems, namely mechanical systems with underactuated continuous dynamics and impulse events. We approach the problem by means of optimal control. Specifically, we design an optimal control based strategy that combines trajectory optimization, dynamics embedding, optimal control relaxation and root finding techniques. The proposed strategy allows us to design, in a numerically stable manner, trajectories that optimize a desired cost and satisfy boundary state constraints consistent with a periodic orbit. To show the effectiveness of the proposed strategy, we perform numerical computations on a compass biped model with torso.

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“…An unbounded number of applications use the techniques of trajectory generation aiming to speculate, plane and formalize the optimum path especially in robotics applications which are the subject of this research. Important applications in robotics such as the robots that use legs [1][2][3][4], navigation robots [5][6][7][8][9][10][11][12] and robot's manipulators [13][14][15][16][17][18][19][20][21][22][23] are in dire need of using the techniques of optimal trajectory in order to optimize the usage of these applications according to the estimates of peripheral conditions.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

Almasri¹,

Mk²

2021

Preprint

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Trajectory Optimization (TO) is the sequence of processes that are considered in order to produce the best path that mends the overall performance or reduces the consumption of the resources where the restriction system remains maintained. In this survey, an inclusive review of the latest advancements in modeling and optimization of trajectory generation in robotic applications will be discussed broadly. In recent times, numerous studies have employed optimal control techniques involving direct and indirect methods in order to convert the authentic Trajectory Optimization problem into a constrained parameter optimization problem. Moreover, a huge variety of optimization algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), Sequential Quadratic Programming (SQP), and Particle Swarm Optimization (PSO) are used aiming to find the optimal solutions for trajectory planning. It is observable that, minimizing the jerk, energy consumption, and execution time among the most widely used design objectives, on the other hand, the robot’s joints configurations and the motor torques are the most used design variables. This paper aims to review the fundamental techniques and their coincident robotic applications in the field of trajectory optimization aiming to afford some steering for related researchers.

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Gait optimization of biped robots based on human motion analysis

Cited by 40 publications

References 9 publications

The NAO Robot in Slippery Scenarios: A Strategy

The NAO Robot in Slippery Scenarios: A Strategy

An optimal control approach to the design of periodic orbits for mechanical systems with impacts

Trajectory Optimization in Robotic Applications, Survey of Recent Developments

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