Path Planning Algorithm Using the Hybridization of the Rapidly-Exploring Random Tree and Ant Colony Systems

Pohan, Muhammad Aria Rajasa; Trilaksono, Bambang Riyanto; Santosa, Sigit Puji; Rohman, Arief Syaichu

doi:10.1109/access.2021.3127635

Cited by 18 publications

(24 citation statements)

References 50 publications

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“…Based on the datareported in Table 4, it can be seen that the average time for the RRT-ACS+RT algorithm to carry out the re-planning process is 65.5% faster than the RRT* algorithm. These results are consistent with the RRT-ACS algorithm performance measurement results reported by Pohan et al [16]. They have also reported that the speed of the RRT-ACS algorithm was 59.4% faster than the RRT* algorithm in reaching the optimal path.…”

Section: Resultssupporting

confidence: 91%

“…Algorithms 2 -4 depict the RRT-ACS+RT-based dynamic path planning algorithm. In order to generate a feasible initial path with fast convergence speed, the RRT-ACS algorithm, as described by Pohan et al [16], is used (line 2 in algorithm 2). After that, the robot will set its velocity (line 4 in algorithm 2).…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…As a result, Karaman developed the RRT* [12]. Since the introduction of the RRT* algorithm, research has expanded to discover new ways to improve the RRT* algorithm in the path planning application [13][14][15][16]. However, the operational environment of path planning is dynamic in many scenarios [17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, informed RRT*-connect can find the optimal solution faster than informed RRT* in a narrow passage environment. Pohan et al [16] created the RRT-ACS algorithm, a hybrid of the RRT algorithm and the Ant Colony System (ACS). In Friedman's nonparametric test, Pohan et al [16] reported that the RRT-ACS algorithm outperformed the informed RRT*, informed RRT*-connect, RRT*-connect, and RRT* algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Pohan et al [16] created the RRT-ACS algorithm, a hybrid of the RRT algorithm and the Ant Colony System (ACS). In Friedman's nonparametric test, Pohan et al [16] reported that the RRT-ACS algorithm outperformed the informed RRT*, informed RRT*-connect, RRT*-connect, and RRT* algorithms. To the authors' best of knowledge, no research has used the RRT-ACS algorithm in a path planning mobile robot in a dynamic environment.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Pohan

Utama

2023

IJE

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This study presents an efficient path planning method for mobile robots in a dynamic environment. The method is based on the rapidly-exploring random tree (RRT) algorithm. The two primary processes in mobile robot path planning in a dynamic environment are initial path planning and path re-planning. In order to generate a feasible initial path with fast convergence speed, we used a hybridization of rapidlyexploring random tree star and ant colony systems (RRT-ACS). When an obstacle obstructs the initial path, the path re-planner must be executed. In addition to the RRT-ACS algorithm, we proposed using a rule-template set based on the mobile robot in dynamic environment scenes during the path re-planner process. This novel algorithm is called RRT-ACS with Rule-Template Sets (RRT-ACS+RT). We conducted many benchmark simulations to validate the proposed method in a real dynamic environment. The performance of the proposed method is compared to the state-of-the-art path planning algorithms: RRT*FND and MOD-RRT*. Numerous experimental results demonstrate that the proposed method outperforms other comparison algorithms. The results show that the proposed method is suitable for the use on robots that need to navigate in a dynamic environment, such as self-driving cars.

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Section: Resultssupporting

confidence: 91%

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Pohan

Utama

2023

IJE

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Urban Ecotourism Evaluation System Based on Ant Colony Algorithm

Cheng

Teekaraman²

2023

Lecture Notes on Data Engineering and Communications Technologies

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Reinforcement Learning-Based Approaches in Manufacturing Environments

Fernández Martínez,

González-Val,

Martín

et al. 2024

Artificial Intelligence in Manufacturing

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The application of reinforcement learning often faces limitations due to the exploration phase, which can be costly and risky in various contexts. This is particularly evident in manufacturing industries, where the training phase of a reinforcement learning agent is constrained, resulting in suboptimal performance of developed strategies. To address this challenge, digital environments are typically created, allowing agents to freely explore the consequences of their actions in a controlled setting. Strategies developed in these digital environments can then be tested in real scenarios, and secondary training can be conducted using hybrid data that combines digital and real-world experiences.In this chapter, we provide an introduction to reinforcement learning and showcase its application in two different manufacturing scenarios. Specifically, we focus on the woodworking and textile sectors, which are part of ongoing research activities within two distinct European Research Projects. We demonstrate how reinforcement learning is implemented in a digital context, with the ultimate goal of deploying these strategies in real systems.

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Path Planning Algorithm Using the Hybridization of the Rapidly-Exploring Random Tree and Ant Colony Systems

Cited by 18 publications

References 50 publications

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Efficient Sampling-based for Mobile Robot Path Planning in a Dynamic Environment Based on the Rapidly-exploring Random Tree and a Rule-template Sets

Urban Ecotourism Evaluation System Based on Ant Colony Algorithm

Reinforcement Learning-Based Approaches in Manufacturing Environments

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