Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Hewawasam, H.S.; Ibrahim, M. Yousef; Kahandawa, Gayan; Choudhury, T. A.

doi:10.1017/s0263574721000588

Cited by 5 publications

(4 citation statements)

References 38 publications

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“…Navigation algorithms capable of navigating robots in dynamic environments are proposed in [40]- [42]. The algorithm presented in [42] uses modified free-space attraction concept. As shown in Fig.…”

Section: Agoraphilic Algorithmmentioning

confidence: 99%

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Hewawasam

Ibrahim

Kahandawa

2022

IEEE Open J. Ind. Electron. Soc.

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Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, pathplanning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot pathplanning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field.

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Section: Agoraphilic Algorithmmentioning

confidence: 99%

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Hewawasam

Ibrahim

Kahandawa

2022

IEEE Open J. Ind. Electron. Soc.

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“…The future global map at iteration "N" is created by combining the positions of moving obstacles in the N th iteration with the positions of static obstacles, both with respect to the robot's future location, Equation (7). These future global maps are used for future FSH creation [30]. This module takes ∼5% of the overall processing time.…”

Section: Dynamic Obstacle Position Prediction Modulementioning

confidence: 99%

A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment

2022

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This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed algorithm takes the optimistic approach of the navigation problem. It does not look for problems to avoid, but rather for solutions to follow. This human-like decision-making behaviour distinguishes the new algorithm from all the other navigation algorithms. Furthermore, the new algorithm utilises newly developed tracking and prediction algorithms, to safely navigate mobile robots. This is further supported by a fuzzy logic controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. This paper also includes physical experimental results combined with bench-marking against other recent methods. The reported results verify the algorithm’s successful advantages in navigating robots in both static and dynamic environments.

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“…Mobile robot navigation plays a vital role in the field of robotics. The Agoraphilic navigation algorithm has been developed to overcome many challenges in mobile robot navigation [1][2][3]. The work presented in this paper is an advanced development of Agoraphilic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

2023

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This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.

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Agoraphilic navigation algorithm in dynamic environment with obstacles motion tracking and prediction

Cited by 5 publications

References 38 publications

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

Past, Present and Future of Path-Planning Algorithms for Mobile Robot Navigation in Dynamic Environments

A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment

Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

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