Motion planning on Mobile Robots using Differential Flatness

Veslin, E. Y.; Slama, Jules Ghislain; Dutra, Max Suell; Lengerke, Omar

doi:10.1109/tla.2011.6129695

Cited by 8 publications

(2 citation statements)

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“…There are, however, some issues, including the lack of control over the image space, which means that the object may leave the field of view of the camera during navigation, as well as the pose estimation algorithm generally causing errors [11]. Using the flatness concept [12][13][14][15][16][17], we propose a new approach to solve these problems. While servoing, the object leaves the camera's field of view.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Control Based on 3D Visual Servoing: A New Approach Combining Pose Estimation by Neural Network and Differential Flatness

et al. 2022

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This paper deals with 3D visual servoing applied to mobile robots in the presence of measurement disturbances, caused in particular by target occlusion. We propose a new approach based on the flatness concept. In 3D visual servoing, the task is performed out of image coordinate space and targets may leave the camera field of view during navigation (servoing). Forced to navigate blindly during one or more periods of time, the robot will use our new open-loop control algorithm inspired by the flatness concept. The 3D visual servoing method is performed using robot pose estimation. This estimation generally contains some errors. The exact position of the robot is therefore not guaranteed, and robust feedback control is necessary to reject these errors in the input. To solve this problem, we propose a new pose estimation method that uses neural networks. We reduce the complexity of the architecture of the neural networks used (the number of variables to estimate) by proving that the location and the orientation of the robot can be ensured by using a single point in the image coordinate space for mobile robots with two degrees of freedom. To show the efficiency of the proposed algorithm, we use the RVCTOOLS MATLAB toolbox.

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Section: Introductionmentioning

confidence: 99%

Mobile Robot Control Based on 3D Visual Servoing: A New Approach Combining Pose Estimation by Neural Network and Differential Flatness

et al. 2022

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“…The position control and trajectory tracking of mobile robots with non-holonomic constraints has been a problem of great interest to many researchers and the literature provides various methods for dealing with it [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Nevertheless, in an agricultural context, some previous approaches have attempted to solve these issues [11][12][15][16], but none have truly succeeded.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Based on the Selection of Fuzzy Behaviours for following Trajectories in Crops

Urrea

Muñoz

2016

International Journal of Advanced Robotic Systems

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This article addresses the problem of trajectory tracking in crops by a weed sprayer mobile robot (WSMR). This problem arises because to fumigate, the robot must follow a predefined path and avoid any obstacles it may encounter. To achieve both trajectory tracking and obstacle avoidance, a control scheme based on different behaviours is proposed, which consists essentially of an adaptive controller with a reference model for trajectory tracking and a fuzzy reactive for obstacle avoidance. Each of these controllers is executed according to the selection of the fuzzy behaviour controller, which uses information delivered by anticollision sensors located on the robot. As a result of the implementation of this behaviour-based architecture and by means of computer simulations and experimental laboratory tests, the WSMR demonstrates the capability of autonomously following a desired trajectory between the rows of a crop in the presence of obstacles. The results are evaluated by taking into account trajectory tracking curves and the operating requirements of each controller, as well as the application of different errors indices for quantitatively evaluating the proposed control scheme.

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