A new framework for mobile robot trajectory tracking using depth data and learning algorithms

Alamiyan-Harandi, Farinaz; Derhami, Vali; Jamshidi, Fatemeh

doi:10.3233/jifs-171043

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…RL is a promising machinelearning framework where an agent interacts within a given environment by applying actions and receiving signals, which are interpreted as rewards and punishments. Via the interactions, the agents learn an optimal policy, a probability distribution over the available actions that maximizes the total obtained rewards for each visited environment state (Alamiyan-Harandi, Derhami, & Jamshidi, 2018;Rasheed, Yau, Noor, Wu, & Low, 2020;Sutton, Barto, et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Inequity aversion reduces travel time in the traffic light control problem

Hassanjani¹,

Alamiyan-Harandi²,

Ramazi³

2023

Preprint

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The traffic light control problem is to improve the traffic flow by coordinating between the traffic lights. Recently, a successful deep reinforcement learning model, CoLight, was developed to capture the influences of neighboring intersections by a graph attention network. We propose IACoLight that boosts up to 11.4% the performance of CoLight by incorporating the Inequity Aversion (IA) model that reshapes each agent's reward by adding or subtracting advantageous or disadvantageous reward inequities compared to other agents. Unlike in the other applications of IA, where both advantageous and disadvantageous inequities are punished by considering negative coefficients, we allowed them to be also rewarded and explored a range of both positive and negative coefficients. Our experiments demonstrated that making CoLight agents averse to inequities improved the vehicles' average travel time and rewarding rather than punishing advantageous inequities enhanced the results.

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Section: Introductionmentioning

confidence: 99%

Inequity aversion reduces travel time in the traffic light control problem

Hassanjani¹,

Alamiyan-Harandi²,

Ramazi³

2023

Preprint

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“…35,36 The learning algorithm is also applied to the motion control gradually. [37][38][39] In terms of the problem of model error and tracking dependence, an intelligent vehicle model transfer trajectory planning method is proposed based on the deep reinforcement learning to obtain the effective the control action and trajectory sequences. 40 In the papers mentioned above, the adaptability of the control system to parametric uncertainties and external disturbances is improved through the control algorithms with strong robustness.…”

Section: Introductionmentioning

confidence: 99%

Adaptive trajectory tracking control strategy of intelligent vehicle

Zhang

Zhao

Zhu

et al. 2020

International Journal of Distributed Sensor Networks

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The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode control algorithm is adopted to design the preview controller to eliminate the effects of uncertainties and realize high accuracy of the target trajectory tracking. According to the real-time deviations of lateral position and lateral attitude angle, the feedback controller is designed based on the fuzzy control algorithm. For improving the adaptability to the multiple dynamic states, the extension theory is introduced to design the coordination controller to adjusting the control proportions of the preview controller and the feedback controller to the front wheel steering angle. Simulation results verify the adaptability, robustness, accuracy of the control strategy under which the intelligent vehicle has good handling stability.

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