A Novel GUI Design for Comparison of ROS-Based Mobile Robot Local Planners

Gurevin, Bilal; Gulturk, Furkan; Yildiz, Muhammed; Pehlivan, Ihsan; Nguyen, Trung Thanh; Caliskan, Fatih; Boru, Baris; Yildiz, Mustafa Zahid

doi:10.1109/access.2023.3327705

Cited by 2 publications

(1 citation statement)

References 33 publications

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“…The most rational approach is to create the robot within a simulation environment and conduct RL-based algorithm training. Several robot simulators are available in the literature, with popular options including Gazebo [15], Webots [16], CoppeliaSim [17], and IsaacSim [18]. These simulators offer realistic visualization with real dynamics.…”

Section: A Background and Related Workmentioning

confidence: 99%

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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Section: A Background and Related Workmentioning

confidence: 99%

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Aydogmus,

Yilmaz

2024

IEEE Access

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Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments

Moysiadis,

Benos,

Karras

et al. 2024

AgriEngineering

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In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

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A Novel GUI Design for Comparison of ROS-Based Mobile Robot Local Planners

Abstract: In the making of this study, the project numbered 118C142 within the scope of TUBITAK-2244 and the international research support numbered 1059B142200681 within the scope of TUBITAK-2214A were utilized.

Cited by 2 publications

References 33 publications

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Comparative Analysis of Reinforcement Learning Algorithms for Bipedal Robot Locomotion

Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments

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