A Theoretical Review of Mobile Robot Locomotion based on Mecanum Wheels

Yadav, Pankaj Singh; Agrawal, Vandana; Mohanta, J. C.; Ahmed, MD Faiyaz

doi:10.5109/4794163

Cited by 4 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…The Internet of Things (IoT) field is experiencing rapid growth in the world of wireless telecommunications [1]. Its purpose is to enable computers to interact with their surroundings and access information about devices and objects without the need for direct human involvement [2]. According to a report [3], IoT is characterized by moderate data transmission rates, affordability, and operates within the frequency range of 100MHz to 5.8GHz.…”

Section: Introductionmentioning

confidence: 99%

Empowering Elderly Individuals through the Intelligent Shopping Trolley

Mokayef,

Hidayab,

Summakieh

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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In this research, we have developed a prototype that aims to improve the weekly shopping experience for senior citizens. Our system tracks the movements of elderly individuals, eliminating the need for them to exert physical force in pushing or pulling the shopping trolley. To achieve this, we utilize a combination of sensors, including a gyroscope and magnetometer, to estimate the user's walking distance and direction. Additionally, we employ WiFi fingerprinting to accurately determine the user's position. Our experiments have yielded satisfactory results in terms of tracking accuracy and the overall functionality of the system. By addressing the specific challenges faced by senior citizens during the routine and essential process of grocery shopping, our smart shopping trolley concept seeks to enhance their experience. Through the integration of tracking technology and sensor-based solutions, we aim to make shopping more convenient and comfortable for elderly individuals. The positive outcomes observed in our experiments validate the effectiveness and feasibility of this approach.

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Section: Introductionmentioning

confidence: 99%

Empowering Elderly Individuals through the Intelligent Shopping Trolley

Mokayef,

Hidayab,

Summakieh

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

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“…It is designed and modeled in human like and walking by the knowledge of the human gait. Controlling the bipedal walking robot is difficult due to its nonlinearity and complexity 2,9) . It refers to the intricate and dynamic nature of their movements, such as multi-body systems consisting of multiple interconnected links and joints and underactuated, meaning they have fewer actuators (motors) than degrees of freedom, which makes controlling them challenging.…”

Section: Introductionmentioning

confidence: 99%

A Trajectory Control for Bipedal Walking Robot Using Stochastic-Based Continuous Deep Reinforcement Learning

Surriani,

Wahyunggoro,

Adha Imam Cahyadi

2023

Evergreen

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The bipedal walking robot is an advanced anthropomorphic robot that can mimic the human ability to walk. Controlling the bipedal walking robot is difficult due to its nonlinearity and complexity. To solve this problem, recent studies have applied various machine learning algorithms based on reinforcement learning approaches, however most of them rely on deterministic-policybased strategy. This research proposes Soft Actor Critic (SAC), which has stochastic policy strategy for controlling the bipedal walking robot. The option thought deterministic and stochastic policy affects the exploration of DRL algorithm. The SAC is a Deep Reinforcement Learning (DRL) based algorithm whose improvement obtained through the augmented entropy-based expected return allows the SAC algorithm to learn faster, gain exploration ability, and still ensure convergence. The SAC algorithm's performance is validated with a bipedal robot to walk towards the straight-line trajectory. The number of the reward and the cumulative reward during the training is used as the algorithm's performance evaluation. The SAC algorithm controls the bipedal walking robot well with a total reward of 384,752.8.

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