Coordinated Motion Control for a Wheel-Leg Robot with Speed Consensus Strategy

Peng, Hui; Wang, Junzheng; Wang, Shoukun; Shen, Wei; Shi, Dawei; Liu, Dongchen

doi:10.1109/tmech.2020.2975083

Cited by 27 publications

(9 citation statements)

References 36 publications

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“…From ( 14) and (17), it is clear that designing δτ x (i.e., τ I ) is equivalent to identify the desired roll angular rate φ+ bτ and linear velocity v + τ . We thus formulate the following optimization problem.…”

Section: A Impulse Control Designmentioning

confidence: 99%

“…The value of κ is selected as small as possible to keep legterrain contact duration short. We use constant δτ x during κ and from (17) we have…”

Section: A Impulse Control Designmentioning

confidence: 99%

“…The major challenges for bikebots to navigate across these terrains include complex wheel-obstacle interactions and limited stabilization actuation [2]. It is well-known that legged locomotion has advantages to navigate on uneven terrains [15], [16] and using both wheel and leg modalities enables mobile robots to adapt to different operation environments and expand their workspace [17]. In [18], a novel leg-wheel transformable robot was designed to perform motions in wheeled, legged trotting and walking modes.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Autonomous Bikebot Control for Crossing Obstacles with Assistive Leg Impulsive Actuation

Hong¹,

Huang²,

Wang³

et al. 2022

Preprint

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As a single-track mobile platform, bikebot (i.e., bicycle-based robot) has attractive navigation capability to pass through narrow, off-road terrain with high-speed and highenergy efficiency. However, running crossing step-like obstacles creates challenges for intrinsically unstable, underactuated bikebots. This paper presents a novel autonomous bikebot control with assistive leg actuation to navigate crossing obstacles. The proposed design integrates the external/internal convertible-based control with leg-assisted impulse control. The leg-terrain interaction generates assistive impulsive torques to help maintain the navigation and balance capability when running across obstacles. The control performance is analyzed and guaranteed. The experimental results confirm that under the control design, the bikebot can smoothly run crossing multiple step-like obstacles with height more than one third of the wheel radius. The comparison results demonstrate the superior performance than those under only the velocity and steering control without leg assistive impulsive actuation.

show abstract

Section: A Impulse Control Designmentioning

confidence: 99%

“…The value of κ is selected as small as possible to keep legterrain contact duration short. We use constant δτ x during κ and from (17) we have…”

Section: A Impulse Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Autonomous Bikebot Control for Crossing Obstacles with Assistive Leg Impulsive Actuation

Hong¹,

Huang²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, the next task is to control the mobile robot to track the human movement point using predictive tracking controller. At the same time, the neural networks are used to evaluate the uncertain interaction in the tracking process [7,20,24,27,28]. Figure 2 displays the kinematic model and turning model of the robot.…”

Section: Tracking Controller Developmentmentioning

confidence: 99%

Human–robot skill transmission for mobile robot via learning by demonstration

Wang

et al. 2021

Neural Comput & Applic

Self Cite

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This paper proposed a skill transmission technique for the mobile robot via learning by demonstration. When the material is transported to the designated location, the robot can show the human-like capabilities: autonomous tracking target. In this case, a skill transmission framework is designed, which the Kinect sensor is utilized to distinguish human activity recognition to create a planned path. Moreover, the dynamic movement primitive method is implemented to represent the teaching data, and the Gaussian mixture regression is utilized to encode the learning trajectory. Furthermore, in order to realize the accurate position control of trajectory tracking, a model predictive tracking control is investigated, where the recurrent neural network is used to eliminate the uncertain interaction. Finally, some experimental tasks using the mobile robot (BIT-6NAZA) are carried out to demonstrate the effectiveness of the developed techniques in real-world scenarios.

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“…In addition, Peng et al realized the coordinated attitude control of robot by designing cooperative controllers, including centroid height controller, attitude controller and traditional impedance controller 37 . Then, the coordinated movement of the robot was realized by distributed ADRC method combined with a novel consensus algorithm 38 …”

Section: Introductionmentioning

confidence: 99%

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

Wang

2021

Intl J Robust & Nonlinear

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When considering the accuracy of tracking control, physical interaction such as structural uncertainties and external dynamics is the main challenge in actual engineering scenarios, especially for the complex robot system. In this article, a neural network‐based sliding mode tracking control scheme (SMCR) is presented for the developed four wheel‐legged robot (BIT‐NAZA) under the uncertain interaction. First, a non‐singular fast terminal function based on the kinematic model is proposed for path tracking, which improves the motion quality during the approach movement and the sliding mode movement. At the same time, it can reduce the influence of uncertain disturbances on the premise of ensuring the path tracking control accuracy via neural networks. Finally, some demonstrations using the autonomous platform of the BIT‐NAZA robot are employed to evaluate the robustness and effectiveness of the hybrid algorithm.

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Coordinated Motion Control for a Wheel-Leg Robot with Speed Consensus Strategy

Cited by 27 publications

References 36 publications

Autonomous Bikebot Control for Crossing Obstacles with Assistive Leg Impulsive Actuation

Autonomous Bikebot Control for Crossing Obstacles with Assistive Leg Impulsive Actuation

Human–robot skill transmission for mobile robot via learning by demonstration

Neural networks‐based sliding mode tracking control for the four wheel‐legged robot under uncertain interaction

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