Design and analysis of a whole-body controller for a velocity controlled robot mobile manipulator

Li, Mantian; Yang, Zeguo; Zha, Fusheng; Wang, Xin; Wang, Pengfei; Li, Ping; Ren, Qinyuan; Chen, Fei

doi:10.1007/s11432-019-2741-6

Cited by 11 publications

(3 citation statements)

References 44 publications

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“…For example, Aydogmus and Boztas controlled the linear and angular velocities of a mobile robot by using the pure pursuit algorithm [1]. In another study, the design and analysis of a wholebody controller were realized for a velocitycontrolled robot mobile manipulator [2]. The velocity of an omnidirectional wheeled mobile robot was controlled using computed voltage control with visual feedback [3].…”

Section: Introductionmentioning

confidence: 99%

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Bıngol

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Kinematic information such as position, velocity, and acceleration is critical to determine the three-dimensional state of the robot in space. In this study, it is aimed to estimate as visual the linear and angular velocity of a mobile robot. Additionally, another aim of this study is to determine the suitability of the concatenation or subtraction layer in the Convolutional Neural Network (CNN) that will make this estimate. For these purposes, first, a simulation environment was created. 9000 pairs of images and necessary velocity information were collected from this simulation environment for training. Similarly, 1000 pairs of images and velocity information were gathered for validation. Four different CNN models were designed and these models were trained and tested using these datasets. As a result of the test, the lowest average error for linear velocity estimation was calculated as 0.93e-3m/s and angular velocity estimation was measured as 4.37e-3rad/s. It was observed that the results were sufficient for linear and angular velocity prediction according to statistical analysis of errors. In addition, it was observed that the subtraction layer can be used instead of the concatenation layer in the CNN architectures for hardware-limited systems. As a result, visual velocity estimation of mobile robots has been achieved with this study and the framework of CNN models has been drawn for this problem.

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Section: Introductionmentioning

confidence: 99%

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Bıngol

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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“…The mobile manipulator robots have gradually become mainstream robots; they combine the mobile platform and the manipulator robot to allow for the completion of complex tasks [1,2]. Compared with fixed manipulator and single mobile robots, mobile manipulator robots can utilize a larger workspace and have advanced dexterity [3,4].…”

Section: Introductionmentioning

confidence: 99%

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

2022

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In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance.

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“…De Luca et al [15] proposed a comprehensive theory to deal with modeling and redundancy resolution for non-holonomic mobile manipulators. A multi-tasks whole-body regulating strategy based on velocity control was proposed by Li et al [16] for a highly redundant mobile manipulator. The mobile manipulator follows the predefined end-effector trajectory while avoiding low-priority control primitives by using null-space projection.…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Control on Non-holonomic Mobile Manipulation for Grapevine Winter Pruning Automation

Teng¹,

Fernandes²,

Gatti³

et al. 2021

Preprint

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Mobile manipulators that combine mobility and manipulability, are increasingly being used for various unstructured application scenarios in the field, e.g. vineyards. Therefore, coordinated motion of the mobile base and manipulator is an essential feature of the overall performance. In this paper, we explore a whole-body motion controller of a robot which is composed of a 2-DoFs non-holonomic wheeled mobile base with a 7-DoFs manipulator (non-holonomic wheeled mobile manipulator, NWMM) This robotic platform is designed to efficiently undertake complex grapevine pruning tasks. In the control framework, a task priority coordinated motion of the NWMM is guaranteed. Lower-priority tasks are projected into the null space of the top-priority tasks so that higher-priority tasks are completed without interruption from lower-priority tasks. The proposed controller was evaluated in a grapevine spur pruning experiment scenario.

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Design and analysis of a whole-body controller for a velocity controlled robot mobile manipulator

Cited by 11 publications

References 44 publications

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Layer Selection for Subtraction and Concatenation: A Method for Visual Velocity Estimation of a Mobile Robot

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

Whole-Body Control on Non-holonomic Mobile Manipulation for Grapevine Winter Pruning Automation

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