Stereo Vision-Based Optimal Path Planning with Stochastic Maps for Mobile Robot Navigation

Sheikh, Taimoor Shakeel; Afanasyev, Ilya

doi:10.1007/978-3-030-01370-7_4

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Therefore, the efficiency of the A* algorithm is better than Dijkstra's. Many works of literature show that the A* algorithm has been widely used in many fields, such as transportation industry [14,15], and with the development of artificial intelligence, the A* algorithm has since been improved, including automated guided vehicle (AGV) [16,17] or unmanned surface vehicle (USV) path planning [18,19] and robot path planning [20,21]. The A* algorithm is simpler and uses less search node, which means less computation than some other path planning algorithms, so it lends itself to more constrained scenarios [22,23].…”

Section: Related Workmentioning

confidence: 99%

“…The A* algorithm needs to search for the shortest path in a map and through a heuristic function for guidance. The heuristic function mainly includes Euclidean distance [24], Manhattan distance [21], diagonal distance [25], and Chebyshev distance. An algorithm is performed and governed by a heuristic function for computation time or run time for the performance of the task.…”

Section: Related Workmentioning

confidence: 99%

“…A constrained A* approach is proposed for USV in a maritime environment [21], determining the approach path with a USV enclosed by a circular boundary as a safety distance constraint on the generation of optimal waypoints to avoid collision. This means the USV is surrounded by a circuit and reflects the performance of a collision-free approach.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient and Robust Improved A* Algorithm for Path Planning

Wang¹,

Qi²,

Lou³

et al. 2021

Symmetry

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Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient and Robust Improved A* Algorithm for Path Planning

Wang¹,

Qi²,

Lou³

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…The path planning is used to reach the points that the gas search algorithm computed in an optimal way, without colliding with the obstacles. Binary Heap A* is utilized for the path planning, based on [16]. First, the user elaborates an inspection trajectory for the robot by selecting way-points in the developed control application.…”

Section: Path Planning and Gas Search Algorithmmentioning

confidence: 99%

Indoor Environment Monitoring in Search of Gas Leakage by Mobile Robot

Braun

Piardi

Brito³

et al. 2019

Advances in Intelligent Systems and Computing

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Inspection based on mobile autonomous robots can assume an important role in many industries. Instead of having fixed sensors, the concept of assembling the sensors on a mobile robot that performs the scanning and inspection through a defined path is cheaper, configurable and adaptable. This paper describes a mobile robot, equipped with several gas sensors and a LIDAR device, that scans an established area by following a trajectory based on way-points searching for gas leakage and simultaneously avoid obstacles in the map. In other words, the robot follows the trajectory while the gas concentration is under a defined value and surrounding the obstacles. Otherwise, the autonomous robot starts the leakage search based on a search algorithm that allows to find the leakage position. The proposed methodology is verified in simulation based on a model of the real robot. The search test performed in a simulation environment allows to validate the proposed methodology.

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