Potential Field Path Planning and Potential Field Path Tracking

Asil, Unal; Uslu, Erkan

doi:10.1109/asyu50717.2020.9259808

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…CAMShift 11 [93], HOG 12 + SVM [129], Actor-Critic RL [140], YOLOv3-Tiny + KCF 13 [2], RRT [17], [143], [144], DDPG 19 [15], [145], [146], GFE 20 [147], A3C [148], Dyna-Q algorithm [11], [149], SARL* 22 [150], ROAMFREE [69], Two-stage stochastic programming [81], TEB 23 + A* [95], [127] 2 -Global Path Planning IDE 24 [16], Dijkstra's algorithm [119], [120], [151], SARSA [152], DQL [8], [153], [154],…”

Section: Householdmentioning

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%

“…The categorisation of WMRs into "Mobile Robots", "Self-driving Cars", and "Wheelchairs", as detailed in Table 5, presents several insights into the current state and direction of AI-driven perception and control research. The significance of the "Mobile Robot" category, particularly the TurtleBot series [2], [8], [9], [12], [25], [30], [81], [88], [100], [105], [143], [144], [147], [148], [150], [158], [178]- [181], indicates a strong focus on exploration and automation in unstructured environments. This trend may reflect the broader scientific interest in space exploration, disaster recovery, and autonomous navigation.…”

Section: Rq3: Which Ros-enabled Wmrs Are Employed In Ai-driven Percep...mentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Householdmentioning

confidence: 99%

Section: Mobile Robotsmentioning

confidence: 99%

Section: Rq3: Which Ros-enabled Wmrs Are Employed In Ai-driven Percep...mentioning

confidence: 99%

See 1 more Smart Citation

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

Kargar,

Yordanov,

Harvey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Some methods [20][21][22] use rapidly exploring random tree to calculate paths to deal with dynamic scenes. Although the artificial potential field, 23,24 execution extended rapidly-exploring random trees, 25 and dynamic A* 26 methods can be applied to dynamic environments, they are not effective for complex heterogeneous dynamic scenes.…”

Section: Traditional Path Planning Algorithmmentioning

confidence: 99%

Heterogeneous group path planning algorithm based on data and mechanism model

Li,

2023

Computer Animation & Virtual

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It is a challenging task to find a feasible path from the start to the end for heterogeneous group and avoid collision between dynamic agents and static obstacles. The existing methods are usually applicable to static simple scenes or the type of scenes including a single kind of agents, and it is difficult to meet the high dynamic heterogeneous group movements. To address the above issues, we propose a hybrid driven heterogeneous group path planning method based on data and mechanism model. Data and mechanism model are combined to drive movements of heterogeneous groups. The experimental results show that our method can describe movements of heterogeneous groups more realistically and solve the collision avoidance of heterogeneous groups well. We quantitatively evaluate our method using metrics such as the number of inflection points and the average turning angle. Average turning angle has decreased by 59.50% on average over prior methods. Number of inflection points has decreased by 69.19% on average over prior methods.

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“…However, in decentralized multi-agent path planning, each agent moves without taking into consideration the behavior of other agents. In addition, the multi-agent path planning problem is processed via the use of different optimization methods such as Genetic Algorithm [16,17], Particle Swarm Optimization (PSO) [18,19], as well as Artificial Potential Field [20,21].…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Dynamic Leader-Follower Path Planning Applied to the Multi-Pursuer Multi-Evader Game

Souidi,

Ledmi,

Maarouk

et al. 2023

cai

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Multi-agent collaborative path planning focuses on how the agents have to coordinate their displacements in the environment to achieve different targets or to cover a specific zone in a minimum of time. Reinforcement learning is often used to control the agents' trajectories in the case of static or dynamic targets. In this paper, we propose a multi-agent collaborative path planning based on reinforcement learning and leader-follower principles. The main objectives of this work are the development of an applicable motion planning in a partially observable environment, and also, to improve the agents' cooperation level during the tasks' execution via the creation of a dynamic hierarchy in the pursuit groups. This dynamic hierarchy is reflected by the possibility of reattributing the roles of Leaders and Followers at each iteration in the case of mobile agents to decrease the task's execution time. The proposed approach is applied to the Multi-Pursuer Multi-Evader game in comparison with recently proposed path planning algorithms dealing with the same problem. The simulation results reflect how this approach improves the pursuit capturing time and the payoff acquisition during the pursuit.

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Potential Field Path Planning and Potential Field Path Tracking

Cited by 4 publications

References 9 publications

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

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

Heterogeneous group path planning algorithm based on data and mechanism model

Multi-Agent Dynamic Leader-Follower Path Planning Applied to the Multi-Pursuer Multi-Evader Game

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