Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Rahmaniar, Wahyu; Rakhmania, Amalia Eka

doi:10.18196/jrc.v3i1.11024

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…The method in [34] combined deep learning and GA to design a followable path for multi-robots. Moreover, there are many different improvements to GA [35][36][37] to achieve better results. In the genetic algorithm, the choice of parameters such as crossover rate and mutation rate has a significant impact on the quality of the solution, but these values are typically chosen based on experience.…”

Section: Related Workmentioning

confidence: 99%

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

Xie

Zhao

et al. 2022

Remote Sensing

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The majority of planning algorithms used are based on the occupancy grid maps, but in complicated situations, the occupancy grid maps have a significant search overhead. This paper proposed a path planner based on the visibility graph (v-graph) for the mobile robot that uses sparse methods to speed up and simplify the construction of the v-graph. Firstly, the complementary grid framework is designed to reduce graph updating iteration costs during the data collection process in each data frame. Secondly, a filter approach based on the edge length and the number of vertices of the obstacle contour is proposed to reduce redundant nodes and edges in the v-graph. Thirdly, a bidirectional breadth-first search is combined into the path searching process in the proposed fast path planner algorithm in order to reduce the waste of exploring space. Finally, the simulation results indicate that the proposed sparse v-graph planner can significantly improve the efficiency of building the v-graph and reduce the time of path search. In highly convoluted unknown or partially known environments, our method is 40% faster than the FAR Planner and produces paths 25% shorter than it. Moreover, the physical experiment shows that the proposed path planner is faster than the FAR Planner in both the v-graph update process and laser process. The method proposed in this paper performs faster when seeking paths than the conventional method based on the occupancy grid.

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Section: Related Workmentioning

confidence: 99%

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

Xie

Zhao

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Each type of this robot moving technique has several advantages and limitations. The main feature of the wheeled mobile robots is the high velocity [21]- [23], while both snake and worm robots consider more suitable for narrow paths [24]. A soft floor is the primary work area for sphere-shaped machine [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Abed,

Al-Ibadi,

Abed

2023

Journal of Robotics and Control

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Designing an inexpensive, low-noise, safe for individual, mobile robot with an efficient vision system represents a challenge. This paper proposes a soft mobile robot inspired by the silkworm body structure and moving behavior. Two identical pneumatic artificial muscles (PAM) have been used to design the body of the robot by sewing the PAMs longitudinally. The proposed robot moves forward, left, and right in steps depending on the relative contraction ratio of the actuators. The connection between the two artificial muscles gives the steering performance at different air pressures of each PAM. A camera (eye) integrated into the proposed soft robot helps it to control its motion and direction. The silkworm soft robot detects a specific object and tracks it continuously. The proposed vision system is used to help with automatic tracking based on deep learning platforms with real-time live IR camera. The object detection platform, named, YOLOv3 is used effectively to solve the challenge of detecting high-speed tiny objects like Tennis balls. The model is trained with a dataset consisting of images of Tennis balls. The work is simulated with Google Colab and then tested in real-time on an embedded device mated with a fast GPU called Jetson Nano development kit. The presented object follower robot is cheap, fast-tracking, and friendly to the environment. The system reaches a 99% accuracy rate during training and testing. Validation results are obtained and recorded to prove the effectiveness of this novel silkworm soft robot. The research contribution is designing and implementing a soft mobile robot with an effective vision system.

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“…Many intelligent optimization algorithms have been offered to help robots optimize their paths. These algorithms draw inspiration from natural phenomena or biological groups, such as the ant colony algorithm (ACO) [19]- [28], genetic algorithm (GA) [29]- [32], [33], [34], and particle swarm optimization (PSO) [35]- [39] [40]. Other algorithms such as fuzzy algorithm [41]- [46], A* algorithm [47]- [50], cuckoo algorithm [51]- [53], improved artificial fish swarm algorithm [54], modified probabilistic roadmap algorithm [55], [56], artificial potential field algorithm [57]- [59] [69]- [71], and hybrid algorithms [60]- [64] [57], [72] have also been implemented in robot path planning.…”

Section: Introductionmentioning

confidence: 99%

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

Hidayat,

Buono,

Priandana

et al. 2023

Journal of Robotics and Control

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Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning.

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Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Cited by 17 publications

References 28 publications

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

FPS: Fast Path Planner Algorithm Based on Sparse Visibility Graph and Bidirectional Breadth-First Search

Vision-Based Soft Mobile Robot Inspired by Silkworm Body and Movement Behavior

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

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