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Agoraphilic navigation algorithm in dynamic environment with and without prediction of moving objects location

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2019
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 45th Annual Conference of the IEEE Industrial Electronics Society, IECON 2019 Vol. 2019-October, p. 5179-5185
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  • Description: This paper presents a summary of research conducted in performance improvement of Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE). The ANADE is an optimistic navigation algorithm which is capable of navigating robots in static as well as in unknown dynamic environments. ANADE has been successfully extended the capacity of original Agoraphilic algorithm for static environment. However, it could identify that ANADE takes costly decisions when it is used in complex dynamic environments. The proposed algorithm in this paper has been successfully enhanced the performance of ANADE in terms of safe travel, speed variation, path length and travel time. The proposed algorithm uses a prediction methodology to estimate future growing free space passages which can be used for safe navigation of the robot. With motion prediction of moving objects, new set of future driving forces were developed. These forces has been combined with present driving force for safe and efficient navigation. Furthermore, the performances of proposed algorithm (Agoraphilic algorithm with prediction) was compared and benched-marked with ANADE (Without predication) under similar environment conditions. From the investigation results, it was observed that the proposed algorithm extends the effective decision making ability in a complex navigation environment. Moreover, the proposed algorithm navigated the robot in a shorter and quicker path with smooth speed variations. © 2019 IEEE.
  • Description: E1

Comparative study on object tracking algorithms for mobile robot navigation in GPS-denied environment

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2019
  • Type: Text , Conference proceedings
  • Relation: 2019 IEEE International Conference on Industrial Technology, ICIT 2019; Melbourne, Australia; 13th-15th February 2019 Vol. 2019-February, p. 19-26
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  • Description: This paper presents a comparative study conducted on the performance of the commonly used object tracking and location prediction algorithms for mobile robot navigation in a dynamically cluttered and GPS-denied mining environment. The study was done to test the different algorithms for the same set criteria (such as accuracy and computational time) under the same conditions.The identified commonly used algorithms for object tracking and location prediction of moving objects used in this investigation are Kalman filter (KF), extended Kalman filter (EKF) and particle filter (PF). The study results of those algorithms are analyzed and discussed in this paper. A trade-off was apparent. However, in overall performance KF has shown its competitiveness.The result from the study has found that the KF based algorithm provides better performance in terms of accuracy in tracking dynamic objects under commonly used benchmarks. This finding can be used in development of an efficient robot navigation algorithm.
  • Description: Proceedings of the IEEE International Conference on Industrial Technology

Development and bench-marking of agoraphilic navigation algorithm in dynamic environment

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer , IEEE
  • Date: 2019
  • Type: Text , Book chapter
  • Relation: 2019 IEEE 28th International Symposium on Industrial Electronics p. 1156-1161
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  • Description: This paper presents a summary of research which was conducted in developing a new human-like navigation methodology based on the Agoraphilic algorithm. This new methodology is capable of maneuvering robots in both static and dynamically clutter unknown environments. The Agoraphilic algorithm is an "optimistic" navigation algorithm. The algorithm is based on free space attraction rather than repulsion of obstacles for navigation. Therefore, this algorithm directs robots to follow the free space leading to the goal instead of avoiding obstacles. This approach has eliminated many draw backs of the traditional APF algorithm. However, the major limitation of the previously developed Agoraphilic algorithm could only deal with static environment. The new proposed algorithm has successfully extended the capacity of Agoraphilic algorithm to deal with environment cluttered with dynamic obstacles. The new Agoraphilic algorithm uses a tracking and prediction methodology to estimate the path of unknown moving objects. The estimated locations of the moving objects are combined with static object locations in the robot's visible region to generate time-varying free space attractive forces. These time varying forces maneuver the robot to the goal in dynamically cluttered unknown environment without collusions. To demonstrate the algorithm's ability, several simulations were performed. Furthermore, the new algorithm was tested and bench-marked against other APF published work under similar environment and conditions. The comparative results are discussed and showed the effectiveness of the new Algoraphilic navigation algorithm.

Evaluating the Performances of the Agoraphilic Navigation Algorithm under Dead-Lock Situations

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2020
  • Type: Text , Conference proceedings , Conference paper
  • Relation: 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 Vol. 2020-June, p. 536-542
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  • Description: This paper presents a summary of the research which was conducted in developing a new free-space based (Agoraphilic) navigation algorithm. This new methodology is capable of maneuvering robots in static as well as dynamically cluttered unknown environments. The new algorithm uses only one force to drive the robot. This force is always an attractive force created by the freespace. This force is focused towards the goal by a force shaping module. Consequently, the robot is motivated to follow free-space directing towards the goal. As this method only based on the attractive forces, the robot always moves towards the goal as long as there is free-space . This method has eradicated many drawbacks of the traditional APF method. Several experimental tests were conducted using Turtlebot3 research platform. These tests were focused on testing the behavior of the new algorithm under dead-lock (local minima) situations for APF method. The test results proved that the proposed algorithm has successfully eliminated the local minima problem of APF method. © 2020 IEEE.

Agoraphilic navigation algorithm in dynamic environment

  • Authors: Hewawasam, Hasitha
  • Date: 2021
  • Type: Text , Thesis , PhD
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  • Description: This thesis presents a novel Agoraphilic (free space attraction [FSA])-based navigation algorithm. This new algorithm is capable of undertaking local path planning for robot navigation in static and dynamic environments with the presence of a moving goal. The proposed algorithm eliminates the common weaknesses of the existing navigation approaches when operating in unknown dynamic environments while using the modified Agoraphilic concept. The Agoraphilic Navigation Algorithm in Dynamic Environment (ANADE) presented in this thesis does not look for obstacles (problems) to avoid; rather, it looks for free space (solutions) to follow. Therefore, this algorithm is also a human-like optimistic navigation algorithm. The proposed algorithm creates a set of Free Space Forces (FSFs) based on the current and future growing free space around the robot. These Free Space Forces are focused towards the current and future locations of a moving goal and finally generate a single attractive force. This attractive force pulls the robot through current free space towards the future growing free space leading to the goal. The new free space concept allows the ANADE to overcome many common problems of navigation algorithms. Several versions of the ANADE have been developed throughout this research to overcome the main limitation of the original Agoraphilic algorithm and address the common weaknesses of the existing navigation approaches. The ANADE I uses an object tracking method to identify the states (locations) of moving objects accurately. The ANADE II uses a dynamic obstacle prediction methodology to identify the robot’s future environments. In the ANADE III, a novel controller based on fuzzy logic was developed and combined with the new FSA concept to provide optimal navigational solutions at a low computational cost. In the ANADE III, the effectiveness of the ANADE II was further improved by incorporating the velocity vectors of the moving objects into decision-making. In the ANADE IV, a self-tuning system was successfully applied to the ANADE III to take advantage of the performances of free space attraction-based navigation algorithms. The proposed final version of the algorithm (ANADE V) comprises nine main modules. These modules are repeatedly used to create the robot’s driving force, which pulls the robot towards the goal (moving or static). An obstacle tracking module is used to identify the time-varying free spaces by tracking the moving objects. Further, a tracking system is also used to track the moving goal. The capacity of the ANADE was strengthened further by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decisions by considering future environments around the robot. This is further supported by a self-tuning, machine learning–based controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. Experimental and simulation-based tests were conducted under dynamic environments to validate the algorithm. Further, the ANADE was benchmarked against other recently developed navigation algorithms. Those tests were focused on the behaviour of the algorithm under challenging environments with moving and static obstacles and goals. Further, the test results demonstrate that the ANADE is successful in navigating robots under unknown, dynamically cluttered environments.
  • Description: Doctor of Philosophy

The agoraphilic navigation algorithm under dynamic environment with a moving goal

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 30th IEEE International Symposium on Industrial Electronics, ISIE 2021 Vol. 2021-June
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  • Description: This paper presents a research conducted in developing a new navigation algorithm to navigate robots under dynamically cluttered environments with a moving goal. There are only a few navigation algorithms capable of navigating robots under dynamic environments compared to static environments. The inability to track and reach a moving goal/target is a one of the common weakness of existing navigating algorithms operating in dynamic environments. The existing free space attraction (Agoraphilic) based navigation algorithms also suffer from this common problem. The proposed algorithm, in this paper was developed to overcome this issue. Agoraphilic Navigation Algorithm under Dynamic Environment, 'ANADE' consists of eight main modules. These modules are iteratively used to create the robot's driving force which pulls the robot towards the moving goal. An obstacle tracking module is used to identify the time varying free spaces by tracking moving obstacles. Furthermore, a tracking system is also used to track the moving goal. The capacity of the ANADE was further strengthen by obstacle and goal path prediction modules. Future location prediction allowed the algorithm to make decision considering future environments around the robot. The proposed algorithm was tested under dynamic environment. These tests were focused on testing the behavior of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that ANADE is successful in reaching a moving goal under an unknown dynamically cluttered environment. © 2021 IEEE.

A novel optimistic local path planner : agoraphilic navigation algorithm in dynamic environment

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Machines Vol. 10, no. 11 (2022), p.
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  • Description: 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. © 2022 by the authors.

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

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan , Choudhury, Tanveer
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Robotica Vol. 40, no. 2 (2022), p. 329-347
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  • Description: This paper presents a new algorithm to navigate robots in dynamically cluttered environments. The proposed algorithm uses basic concepts of space attraction (hence the term Agoraphilic) to navigate robots through dynamic obstacles. The new algorithm in this paper is an advanced development of the original Agoraphilic navigation algorithm that was only able to navigate robots in static environments. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for a free space (solutions) to follow. Therefore, it is also described as an optimistic navigation algorithm. This algorithm uses only one attractive force created by the available free space. The free-space concept allows the Agoraphilic algorithm to overcome inherited challenges of general navigation algorithms. However, the original Agoraphilic algorithm has the limitation in navigating robots only in static, not in dynamic environments. The presented algorithm was developed to address this limitation of the original Agoraphilic algorithm. The new algorithm uses a developed object tracking module to identify the time-varying free spaces by tracking moving obstacles. The capacity of the algorithm was further strengthened by the new prediction module. Future space prediction allowed the algorithm to make decisions considering future growing/diminishing free spaces. This paper also includes a bench-marking study of the new algorithm compared with a recently published APF-based algorithm under a similar operating environment. Furthermore, the algorithm was validated based on experimental tests and simulation tests. © 2022 Cambridge University Press. All rights reserved.

Agoraphilic navigation algorithm under dynamic environment

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Appuhamillage, Gayan , Choudhury, Tanveer
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE/ASME Transactions on Mechatronics Vol. 27, no. 3 (2022), p. 1727-1737
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  • Description: This article presents a summary of the work done on the development of a new algorithm for mobile robot navigation in unknown dynamic environment. The developed humanlike algorithm uses a free-space attraction (Agoraphilic) concept for robot navigation. The algorithm presented in this article is an advanced development of the Agoraphilic navigation algorithm. The Agoraphilic algorithm does not look for obstacles (problems) to avoid but rather for free spaces toward the goal (solutions) to follow. The original Agoraphilic while it was able to overcome the limitations of the traditional algorithms had its own limitations in navigating robots in environments cluttered with moving obstacles. The new Agoraphilic Navigation Algorithm under Dynamic Environment (ANADE) was developed to overcome those limitations. ANADE consists of seven main modules reported in this article. The objects tracking and objects prediction methodologies developed for the algorithm estimate future growing free-space passages toward the goal. The algorithm generates a time-varying single attractive force to pull the robot through the free space toward the predicted (future) growing free-space passages leading to the goal. The new algorithm was tested, not only through simulation, but also through experimental work. Summary of the experimental results is presented and discussed in this article. © 1996-2012 IEEE.

Machine learning-based agoraphilic navigation algorithm

  • Authors: Hewawasam, Hasitha , Ibrahim, Yousef , Kahandawa, Gayan
  • Date: 2022
  • Type: Text , Conference paper
  • Relation: 48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022, Brussels, 17-20 October 2022, Proceedings IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Vol. 2022-October
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  • Description: This paper presents a novel machine learning-based Agoraphilic (free space attraction) navigation algorithm. The proposed algorithm is capable of undertaking local path planning for mobile robots in unknown dynamic environments with a moving goal. The inability to track and reach a moving goal is one of the common weaknesses of most existing navigation algorithms operating in dynamic environments. High uncertainty involved in dynamic environments is also another major challenge. The novel machine learning-based approach helps the proposed algorithm to successfully overcome these challenges. This paper also introduces the integrated modular-based architecture for free-space attraction-based algorithms. This allows the algorithm to incorporate ten different modules with miscellaneous algorithms to perform sub-tasks such as tracking, prediction, map generation, machine learning-based free space attraction force generation and robot motion command generation. The new modular-based architecture integrates those sub-modules to create the robot's driving force. This driving force is the single attractive force to pull the robot towards the moving goal via current free space leading to future free space passages. The proposed algorithm was experimentally tested under a dynamic environment. The experiment was focused on testing the behaviour of the algorithm under the challenge of reaching a moving goal. Furthermore, the test results demonstrate that the Agoraphilic algorithm is successful in reaching a moving goal in an unknown dynamically cluttered environment. © 2022 IEEE.

Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments

  • Authors: Hewawasam, Hasitha , Ibrahim, Muhammad , Appuhamillage, Gayan
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Open Journal of the Industrial Electronics Society Vol. 3, no. (2022), p. 353-365
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  • Description: 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, path-planning 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 path-planning 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. © 2020 IEEE.

Artificial intelligence enabled digital twin for predictive maintenance in industrial automation system : a novel framework and case study

  • Authors: Siddiqui, Mustafa , Appuhamillage, Gayan , Hewawasam, Hasitha
  • Date: 2023
  • Type: Text , Conference paper
  • Relation: 2023 IEEE International Conference on Mechatronics, ICM 2023, Leicestershire UK, 15-17 March 2023, Proceedings - 2023 IEEE International Conference on Mechatronics, ICM 2023
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  • Description: Industrial automation systems are excessively used in advanced manufacturing environments. These systems are always prone to failure which not only disturbs smooth manufacturing operations but can also cause injuries to operators. Therefore, in this research, a novel predictive maintenance algorithm is proposed that can be used to detect anomalies in the automation system to avoid asset failure. Artificial Intelligence enabled Digital Twin model was used to detect early anomalies to avoid catastrophic effects of equipment failure. Real-time sensor data were used to validate the proposed novel algorithm. The data were recorded via sensors mounted on the physical system. This paper presents the effectiveness of the proposed algorithm to detect anomalies in industrial automation systems under faulty conditions. © 2023 IEEE.

Machine learning driven digital twin for industrial control black box system : a novel framework and case study

Machine learning-based agoraphilic navigation algorithm for use in dynamic environments with a moving goal

  • Authors: Hewawasam, Hasitha , Kahandawa, Gayan , Ibrahim, Yousef
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Machines Vol. 11, no. 5 (2023), p. 513
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  • Description: 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.

Enhanced agoraphilic algorithm for uneven terrain-adaptive navigation

Free space identification for agoraphilic algorithm in uneven terrain environment

Navigating the new normal: student perspectives on transitioning from online to face-to-face learning after COVID-19 lockdowns

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