Path Planning for Autonomous Mobile Robots: A Review

Sánchez‐Ibáñez, José Ricardo; Pérez-del-Pulgar, Carlos Jesús; García-Cerezo, Alfonso

doi:10.3390/s21237898

Cited by 174 publications

(98 citation statements)

References 203 publications

(218 reference statements)

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“…According to the different characteristics of AMR algorithms, the algorithm can be divided into global path planning and local path planning 23 . In the global planning algorithm, A* algorithm is a heuristic global path planning algorithm and one of the most efficient direct search methods for finding the shortest path in a static environment.…”

Section: Introductionmentioning

confidence: 99%

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Xiang

Lin

Ouyang

et al. 2022

Sci Rep

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With the development of artificial intelligence, path planning of Autonomous Mobile Robot (AMR) has been a research hotspot in recent years. This paper proposes the improved A* algorithm combined with the greedy algorithm for a multi-objective path planning strategy. Firstly, the evaluation function is improved to make the convergence of A* algorithm faster. Secondly, the unnecessary nodes of the A* algorithm are removed, meanwhile only the necessary inflection points are retained for path planning. Thirdly, the improved A* algorithm combined with the greedy algorithm is applied to multi-objective point planning. Finally, path planning is performed for five target nodes in a warehouse environment to compare path lengths, turn angles and other parameters. The simulation results show that the proposed algorithm is smoother and the path length is reduced by about 5%. The results show that the proposed method can reduce a certain path length.

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Section: Introductionmentioning

confidence: 99%

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Xiang

Lin

Ouyang

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Most works that perform path search in this type of workspace are also referred to as C-space search. Depending on the way of discretizing the C-space search, path-searching methods can be subdivided into two predominant groups [ 28 ]: the sampling-based method and graph search method. Rapidly Random Tree (RRT) [ 29 ] is the most famous sampling-based path search method, which stimulates the growth of a tree, starting from a starting point and dynamically creating branches [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

Object-Based Reliable Visual Navigation for Mobile Robot

Wang

Zhang

et al. 2022

Sensors

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Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.

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“…Some early studies on mobile robot collision avoidance are limited to structured environments with static obstacles (Dahl et al , 2019; Mac et al , 2016; Sánchez-Ibáñez et al , 2021). In particular, simultaneous localization and mapping (SLAM) algorithms based on LiDAR data have achieved excellent accuracy and stability, and are widely used in structured industrial environments (Oğuz-Ekim, 2020).…”

Section: Introductionmentioning

confidence: 99%

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

Zhou

Feng²,

Chou³

2022

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Purpose Wheeled mobile robots (WMR) are the most widely used robots. Avoiding obstacles in unstructured environments, especially dynamic obstacles such as pedestrians, is a serious challenge for WMR. This paper aims to present a hybrid obstacle avoidance method that combines an informed-rapidly exploring random tree* algorithm with a three-dimensional (3D)-object detection approach and model prediction controller (MPC) to conduct obstacle perception, collision-free path planning and obstacle avoidance for WMR in unstructured environments. Design/methodology/approach Given a reference orientation and speed, the hybrid method uses parametric ellipses to represent obstacle expansion boundaries based on the 3D target detection results, and a collision-free reference path is planned. Then, the authors build on a model predictive control for tracking the collision-free reference path by incorporating the distance between the robot and obstacles. The proposed framework is a mapless method for WMR. Findings The authors present experimental results with a mobile robot for obstacle avoidance in indoor environments crowded with obstacles, such as chairs and pedestrians. The results show that the proposed hybrid obstacle avoidance method can satisfy the application requirements of mobile robots in unstructured environments. Originality/value In this study, the parameter ellipse is used to represent the area occupied by the obstacle, which takes the velocity as the parameter. Therefore, the motion direction and position of dynamic obstacles can be considered in the planning stage, which enhances the success rate of obstacle avoidance. In addition, the distance between the obstacle and robot is increased in the MPC optimization function to ensure a safe distance between the robot and the obstacle.

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Path Planning for Autonomous Mobile Robots: A Review

Cited by 174 publications

References 203 publications

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

Object-Based Reliable Visual Navigation for Mobile Robot

A hybrid obstacle avoidance method for mobile robot navigation in unstructured environment

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