Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

Boztas, Gullu; Aydoğmuş, Ömür

doi:10.17694/bajece.983350

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…1. The observation state encompasses several crucial variables as given in (1). These observation state variables are highlighted in purple in Fig.…”

Section: Definitions Of Robot and Environmentmentioning

confidence: 99%

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Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Aydogmus,

Yilmaz

2024

IEEE Access

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In this research, an optimization methodology was introduced for improving bipedal robot locomotion controlled by reinforcement learning (RL) algorithms. Specifically, the study focused on optimizing the Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithms. The optimization process utilized the Tree-structured Parzen Estimator (TPE), a Bayesian optimization technique. All RL algorithms were applied to the same environment, which was created within the OpenAI GYM framework and known as the bipedal walker. The optimization involved the fine-tuning of key hyperparameters, including learning rate, discount factor, generalized advantage estimation, entropy coefficient, and Polyak update parameters. The study comprehensively analyzed the impact of these hyperparameters on the performance of RL algorithms. The results of the optimization efforts were promising, as the fine-tuned RL algorithms demonstrated significant improvements in performance. The mean reward values for the 10 trials were as follows: PPO achieved an average reward of 181.3, A2C obtained an average reward of −122.2, SAC reached an average reward of 320.3, and TD3 had an average reward of 278.6. These outcomes underscore the effectiveness of the optimization approach in enhancing the locomotion capabilities of the bipedal robot using RL techniques.

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“…1. The observation state encompasses several crucial variables as given in (1). These observation state variables are highlighted in purple in Fig.…”

Section: Definitions Of Robot and Environmentmentioning

confidence: 99%

“…In recent years, mobile robots have become increasingly integrated into our daily lives, and they come in various types. In general, robots can be classified into two main categories: wheeled robots and legged robots [1]. Furthermore, it's possible to combine wheeled and legged components to create various types of robots [2].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Aydogmus,

Yilmaz

2024

IEEE Access

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“…The implementation of pure pursuit algorithm (PPA) in the MATLAB/Simulink application computes the linear velocity and angular velocity of the robot based on its current pose and a set of waypoints [24]. The PPA involves two inputs and two outputs, as depicted in Figure 5(b).…”

Section: Agv Control Algorithmmentioning

confidence: 99%

AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance

Ketsayom,

Maneetham,

Crisnapati

2024

IJEECS

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This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.

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“…Mobile robots are typically divided into two main categories: wheeled robots [7] and legged robots [8]. Additionally, there are various approaches to combining wheeled and legged components to create different types of robots [9].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Digital Simulator Design for Differential Drive Mobile Robot using FPGA

Sarac,

Aydoğmuş

2024

Balkan Journal of Electrical and Computer Engineering

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This paper presents a real-time simulation of a differential drive mobile robot (DDMR). The permanent magnet DC motors that drive the robot’s left and right wheels were modeled and executed in real-time on a Field Programmable Gate Array (FPGA) based co-simulator platform, interfacing with the Webots robot simulator, which simulates the DDMR on the PC side. The electrical parameters, which are not available in robot simulators, were simulated and measured by the proposed co-simulator system in real-time under various environmental conditions and trajectories of the robot. Parameters such as current, voltage, and torque were measured instantaneously, enabling a more realistic simulation. Additionally, the cycle time of the robot simulator was determined to be 32 ms, and the developed FPGA-based simulation operated at approximately 2000 times the speed of the robot simulator. The results demonstrate the applicability of the developed platform in robotic applications.

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Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

Cited by 7 publications

References 24 publications

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

AGV maneuverability simulation and design based on pure pursuit algorithm with obstacle avoidance

Real-Time Digital Simulator Design for Differential Drive Mobile Robot using FPGA

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