System Design and Balance Control of a Bipedal Leg-wheeled Robot

Zhang, Chao; Liu, Tangyou; Song, Shuang; Meng, Max Q.-H.

doi:10.1109/robio49542.2019.8961814

Cited by 21 publications

(8 citation statements)

References 12 publications

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“…In our previous works, although the controller is stabilizing, the robot cannot stand still without extra manual adjustment, which is mentioned as an unsolved problem in Zhang et al (2022). This phenomenon is common in realrobot experiments of two-wheel and wheel-legged robots (Jung and Kim, 2008;Huang et al, 2012;Zafar et al, 2019;Zhang et al, 2019;Zhou et al, 2021), but it is unnoticed if the asymptotic stability is not required strictly.…”

Section: Introductionmentioning

confidence: 99%

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach

Zhang

Wang

et al. 2023

Front. Neurorobot.

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The dynamics of a robot may vary during operation due to both internal and external factors, such as non-ideal motor characteristics and unmodeled loads, which would lead to control performance deterioration and even instability. In this paper, the adaptive optimal output regulation (AOOR)-based controller is designed for the wheel-legged robot Ollie to deal with the possible model uncertainties and disturbances in a data-driven approach. We test the AOOR-based controller by forcing the robot to stand still, which is a conventional index to judge the balance controller for two-wheel robots. By online training with small data, the resultant AOOR achieves the optimality of the control performance and stabilizes the robot within a small displacement in rich experiments with different working conditions. Finally, the robot further balances a rolling cylindrical bottle on its top with the balance control using the AOOR, but it fails with the initial controller. Experimental results demonstrate that the AOOR-based controller shows the effectiveness and high robustness with model uncertainties and external disturbances.

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Section: Introductionmentioning

confidence: 99%

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach

Zhang

Wang

et al. 2023

Front. Neurorobot.

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“…At present, scholars have carried out a lot of research on the stability and high obstacle crossing ability of wheel-legged robots in unstructured terrain and have achieved a series of results: Liu et al (2019Liu et al ( , 2022 and Zhang et al (2019) proposed a bipedal wheeled robot SR600 for logistics in scenarios such as distribution and home services, it can change height while maintaining dynamic balance. The size design of the human body can make it better interact with people.…”

Section: Introductionmentioning

confidence: 99%

Design and dynamic analysis of jumping wheel-legged robot in complex terrain environment

et al. 2022

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Wheel-legged robots have fast and stable motion characteristics on flat roads, but there are the problems of poor balance ability and low movement level in special terrains such as rough roads. In this paper, a new type of wheel-legged robot with parallel four-bar mechanism is proposed, and the linear quadratic regulator (LQR) controller and fuzzy proportion differentiation (PD) jumping controller are designed and developed to achieve stable motion so that the robot has the ability to jump over obstacles and adapt to rough terrain. The amount of energy released by the parallel four-bar linkage mechanism changes with the change of the link angle, and the height of the jump trajectory changes accordingly, which improves the robot’s ability to overcome obstacles facing vertical obstacles. Simulations and real scene tests are performed in different terrain environments to verify obstacle crossing capabilities. The simulation results show that, in the pothole terrain, the maximum height error of the two hip joint motors is 2 mm for the obstacle surmounting method of the adaptive retractable wheel-legs; in the process of single leg obstacle surmounting, the maximum height error of the hip joint motors is only 6.6 mm. The comparison of simulation data and real scene experimental results shows that the robot has better robustness in moving under complex terrains.

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“…In [20], a navigational two-wheeled self-balancing robot control using a PD-PI controller based on the Kalman filter algorithm was reported. Similarly, [21] used variable structure combining proportional integral differential controllers for balance and locomotion deriving a kinematic model based on the center of gravity constraint. Lagrangian-based with Kane's approach for dynamic balancing was reported in [22].…”

Section: Introductionmentioning

confidence: 99%

Rolling Biped Polynomial Motion Planning

Ortíz¹,

Martínez‐García²

2022

Motion Planning

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This work discloses a kinematic control model to describe the geometry of motion of a two-wheeled biped’s limbs. Limb structure is based on a four-bar linkage useful to alleviate damping motion during self-balance. The robot self-balancing kinematics geometry combines with user-customized polynomial vector fields. The vector fields generate safe reference trajectories. Further, the robot is forced to track the reference path by a model-based time-variant recursive controller. The proposed formulation showed effectiveness and reliable performance through numerical simulations.

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System Design and Balance Control of a Bipedal Leg-wheeled Robot

Cited by 21 publications

References 12 publications

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach

Adaptive optimal output regulation for wheel-legged robot Ollie: A data-driven approach

Design and dynamic analysis of jumping wheel-legged robot in complex terrain environment

Rolling Biped Polynomial Motion Planning

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