A vision-based intelligent path following control of a four-wheel differentially driven skid steer mobile robot

Torque distribution strategy for a four in-wheel-motors independently driving electrical vehicle in skid steering is researched. By kinematics and dynamics analysis of skid steering, a skid steering control system is designed, and torque distribution strategy is established on turning in different radius. The skid steering control system is modelled by MATLAB/Simulink and CarSim, and the dynamic performances of different radius are respectively co-simulated, and the torque distribution rule is analyzed. The results show that the torque distribution strategy is feasible.

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“…12(a). (5) Running steering in different steering radius-the motors of both sides output the same direction driving torque.…”

Section: Simulation Results and Analysismentioning

confidence: 99%

Research on skid steering control strategy for four-in-wheel-motor drive electrical vehicle

Zhai

Wang

Feng

2014

2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

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“…Novel application of a laser range finder with vision system for wheeled mobile robot was presented by Chun et al [19], where their research presents a trajectory planning strategy of a wheeled mobile robot in an obstructed environment. A vision-based intelligent path following control of a four-wheel differentially driven skid steer mobile robot was given by Nazari and Naraghi [20]. In this work, a Fuzzy Logic Controller (FLC) for path following of a four-wheel differentially skid steer mobile robot is presented.…”

Section: Study Backgroundmentioning

confidence: 99%

Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces

Mattar¹

2019

Applications of Mobile Robots

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Learning mobile robot space and navigation behavior, are essential requirements for improved navigation, in addition to gain much understanding about the navigation maps. This chapter presents mobile robots feature-based SLAM behavior learning, and navigation in complex spaces. Mobile intelligence has been based on blending a number of functionaries related to navigation, including learning SLAM map main features. To achieve this, the mobile system was built on diverse levels of intelligence, this includes principle component analysis (PCA), neuro-fuzzy (NF) learning system as a classifier, and fuzzy rule based decision system (FRD).

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“…Em (NAZARI and NARAGHI, 2008) é apresentado um modelo cinemático para robô movido por 4 rodas e assume-se que as velocidades das rodas laterais são sincronizadas. Nesse modelo são consideradas algumas características que talvez não sejam triviais de determinar, como o CG centro de gravidade do robô ou mesmo que possam variar conforme o trajeto, como o ICR.…”

Section: Modelo Cinemático De Robôs Móveis Diferenciaisunclassified

Modelagem, simulação e controle de robô móvel autônomo.

Libânio¹

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This work presents a modeling and control of a mobile differential robot. The main objective was to implement a Proportional Integral controller tuned by the Direct Synthesis method, use a velocity trajectory controller and perform predetermined paths. For this, the coefficients of the motors were determined and validated and it was also made the verification of speeds sensors and electrical sensors of the motors. The secondary objective was to use a controller based on robot dynamics, using the adaptive nonlinear model based on reference velocity equations and the dynamic model based on Newton Euler equations is also presented, but its practical implementation was not feasible with the pole allocation linear controller due to the high voltage values delivered by the controller. To determine the parameters of the dynamic models, system identification tests were performed and then the least squares algorithm was used. Genetic algorithms were used to determine the gains of reference speed controllers and several simulations were performed to understand the robot's behavior during the execution of paths. In this dissertation, tests for monitoring the robot's position and speed in a linear path and in a circle are presented, where there is a comparison between the monitoring of the robot and the LIDAR sensor using a PI controller and a trajectory control.Tests are also presented with dynamic velocity controllers moving in a straight line, an attempt to execute a circular path and the realization of the square path, in addition to the simulations presented. Some aspects that hindered the implementation of the dynamic controller were noted, such as the deviation in linear tracking, noise in the velocities and the large error in the angular velocity tracking. The work presented the initial stages of modeling and implantation of a robotic system in which, for low speed conditions, there is a satisfactory behavior in the execution of the paths. In the tests with dynamic speed controllers, it was evident that noise in the system may have caused the non-desired implementation, in addition to possible physical constructive interference of the robotic system.

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A vision-based intelligent path following control of a four-wheel differentially driven skid steer mobile robot

Cited by 11 publications

References 10 publications

Research on skid steering control strategy for four-in-wheel-motor drive electrical vehicle

Research on skid steering control strategy for four-in-wheel-motor drive electrical vehicle

Mobile Robot Feature-Based SLAM Behavior Learning, and Navigation in Complex Spaces

Modelagem, simulação e controle de robô móvel autônomo.

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