Motion control for intelligent ground vehicles based on the selection of paths using fuzzy inference

Wang, Shiwei; Panzica, Adam C.; Padır, Taşkın

doi:10.1109/tepra.2013.6556354

Cited by 2 publications

(5 citation statements)

References 17 publications

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“…These are 4 wheeled mobile robots with differential drives. The robots were programmed and configured using the Robot Operating System (ROS) and the Python scripting language [16]. Each robot ran its software independently in a decentralized fashion.…”

Section: Methodsmentioning

confidence: 99%

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A Learning Invader for the “Guarding a Territory” Game

Raslan

Schwartz

Givigi

2016

J Intell Robot Syst

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This paper explores the use of a learning algorithm in the "guarding a territory" game. The game occurs in continuous time, where a single learning invader tries to get as close as possible to a territory before being captured by a guard. Previous research has approached the problem by letting only the guard learn. We will examine the other possibility of the game, in which only the invader is going to learn. Furthermore, in our case the guard is superior (faster) to the invader. We will also consider using models with non-holonomic constraints. A control system is designed and optimized for the invader to play the game and reach Nash Equilibrium. The paper shows how the learning system is able to adapt itself. The system's performance is evaluated through different simulations and compared to the Nash Equilibrium. Experiments with real robots were conducted and verified our simulations in a real-life environment. Our results show that our learning invader behaved rationally in different circumstances.

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Section: Methodsmentioning

confidence: 99%

“…Our experiments mapped the simulation size 30unit × 30unit to 8.3meter × 8.3meter. Players' Invader : v I = 0.35meter/sec (14) Guard : v G = 0.42meter/sec (15) |ω j | = −0.5, 0, or 0.5rad/sec (16) The model that we used in Eq. 2 is not the same as the robot.…”

Section: Methodsmentioning

confidence: 99%

A Learning Invader for the “Guarding a Territory” Game

Raslan

Schwartz

Givigi

2016

J Intell Robot Syst

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“…AMARL 29 [162], VFH+ 30 [76], SND 31 [76], A-FGM 32 [85], CNN [163], [164] 4 -Navigation CNN + MGRL 33 [165], RL + APPL 34 [53], DWA [166], BDI 35 [79], [123], ANN 36 + DPP 37 [24], B-spline curves [82], Petri Nets [111], PID 38 controller + RANSAC 39 [117], DRL [37], PaCcET [72], AMCL 41 [167]- [172], Control Mechanisms 1 -Motion Control RBFNN 42 [115], ANN [173], Fuzzy inference [174], DDPG [51], Kalman Filters [86], B-RV [131], TD3 44 [26], Evolutionary algorithm [35]…”

Section: Householdmentioning

confidence: 99%

“…These systems leverage AI techniques to ensure that a robot's movements are precise, fluid, and aligned with its perceived surroundings and intentions. In the motion control sector, traditional approaches like Fuzzy inference systems [174] coexist with more modern methodologies, such as Deep Deterministic Policy Gradient (DDPG) [51] and Radial Basis Function Neural Networks (RBFNN) [115]. These approaches aim to provide both deterministic and adaptive strategies, catering to scenarios that require fixed trajectories and those that demand on-the-fly adjustments.…”

Section: -Teleoperationmentioning

confidence: 99%

“…PeopleBot [42], [91] Pioneer 3-DX [19], [36], [42], [72], [119] Clearpath Robotics Husky [28], [75], [85], [116], [174], [175], [178] Clearpath Robotics Jackal [18], [28], [53], [94] Willow Garage TurtleBot series [2], [8], [9], [12], [25], [30], [35], [70], [81], [88], [100], [105], [143], [144], [147], [148], [150], [158], [178]- [185] Willow Garage PR2 [87], [124] Roomba vacuum cleaner [86], [172] Eddie [186] Rob@work 3 [187] KUKA YouBot [138], [142] Arlobot [134] PlatypOUs [137] Ceres (Volksbot platform)…”

Section: Mobile Robotsmentioning

confidence: 99%

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Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

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Simulation plays a pivotal role in providing safely reproducible scenarios to evaluate the ever-advancing domain of computer science and robotics. It was an essential part of the pandemic when no access to physical spaces was available. The advent of AI-powered platforms in conjunction with enhanced graphics, physics and other sensory engines attracts a new breed of interdisciplinary researchers to enter the robotic field, most notably from computer science, engineering and social sciences. Integration of ROS as a uniform middleware to deploy achieved outcomes in real practice provides an opportunity to move one step closer to the sim-to-real experiences that enables researchers to test ideas beyond the close laboratory spaces. There is a lack of comprehensive evaluation of ROS-enabled simulators, and the integration of advanced AI techniques for realistic scenario replication. This paper addresses this challenge by evaluating ROSenabled simulators in the design and implementation of AI techniques through an in-depth systematic literature review (SLR). This SLR is guided by the research and commercial market demands, employing Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) frameworks with a major focus on Wheeled Mobile Robots (WMRs). We also highlight the increasing importance of game engines like Unity and Unreal in future of robotic simulations, especially under modelling close to real experiences. By comparing simulation platform features and capabilities, this paper offers guidance to developers and researchers, enabling them to select the most suitable platform for their projects efficiently that contradicts the commonly in use "one size fits all" approach. Finally, based on the thorough insights from the review, we identify and suggest some key future research directions in AI-enhanced realistic robotic simulations.

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Motion control for intelligent ground vehicles based on the selection of paths using fuzzy inference

Cited by 2 publications

References 17 publications

A Learning Invader for the “Guarding a Territory” Game

A Learning Invader for the “Guarding a Territory” Game

Emerging Trends in Realistic Robotic Simulations: A Comprehensive Systematic Literature Review

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