Path Planning of Mobile Robot in Unknown Environment

In this paper, we study the online path planning for khepera II mobile robot in an unknown environment. The well known heuristic A* algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance and time metric as the cost function.

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D* lite algorithm based path planning of mobile robot in static Environment

International Journal of Computer and Communication Technology ◽

10.47893/ijcct.2012.1158 ◽

2012 ◽

pp. 286-290

Author(s):

Pradipta kumar Das ◽

S .N. Patro ◽

C. N. Panda ◽

Bunil Balabantaray

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Cost Function ◽

Shortest Path ◽

Target Position ◽

Initial Position ◽

Distance Metric ◽

Unknown Environment ◽

Feasible Path ◽

The Cost

In this paper, we study the path planning for khepera II mobile robot in an unknown environment. The well known heuristic D* lite algorithm is implemented to make the mobile robot navigate through static obstacles and find the shortest path from an initial position to a target position by avoiding the obstacles. and to perform efficient re-planning during exploration. The proposed path finding strategy is designed in a grid-map form of an unknown environment with static unknown obstacles. The robot moves within the unknown environment by sensing and avoiding the obstacles coming across its way towards the target. When the mission is executed, it is necessary to plan an optimal or feasible path for itself avoiding obstructions in its way and minimizing a cost such as time, energy, and distance. In our study we have considered the distance metric as the cost function.

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World representation and path planning for a mobile robot

Robotica ◽

10.1017/s026357470000357x ◽

1988 ◽

Vol 6 (1) ◽

pp. 35-40 ◽

Cited By ~ 18

Author(s):

E. Palma-Villalon ◽

P. Dauchez

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Cost Function ◽

Experimental Results ◽

Spatial Relationships ◽

Simple Representation ◽

Matrix Description ◽

Path Planner ◽

The Cost

SUMMARYThis paper is related to the problem of navigation of a mobile robot amidst obstacles. In order to easily take into account any modification of the environment, we propose a very simple representation of the obstacles, based on the use of rectangles, as well as a matrix description of the spatial relationships between the obstacles. We also present a path planner based on a A* algorithm, the features of which are specifically designed for our world of rectangles. The cost function takes into account both the length of the path and the number of turns. Some experimental results and implementation details are also given in this paper.

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A dynamic path planning method for wheeled mobile robots (Dyna-bug).

Global Journal of Computer Sciences Theory and Research ◽

10.18844/gjcs.v7i3.2791 ◽

2017 ◽

Vol 7 (3) ◽

pp. 123-128

Author(s):

Suat Karakaya ◽

Gurkan Kucukyildiz ◽

Hasan Ocak

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Cost Function ◽

Sensor Data ◽

Stationary Condition ◽

Wheeled Mobile Robots ◽

Dynamic Path Planning ◽

Dynamic Path ◽

The Cost ◽

Local Map

Abstract In this study, a hybrid path-planning scheme is presented. The main contribution of this paper is merging the static grid costs of the global map and the immediate environmental structure of the local map. The stationary condition of the map and the instant local goal is weighted by certain coefficients in order to determine the next move of the wheeled mobile robot (WMR). Thus, the cost function is defined in terms of the grid costs and the dynamic parameters. The main assumption is that the WMR on which this scheme is executed must be equipped with a field scanning sensor. The sensor readings in each processing cycle are pre-processed before plugging in the cost function. The passages in the local map are extracted from the sensor data, then the optimal collision-free point lying on the passages is obtained via the cost function. Keywords: Path planning, collision avoidance, mobile robot.

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Research on path planning of three-neighbor search A* algorithm combined with artificial potential field

International Journal of Advanced Robotic Systems ◽

10.1177/17298814211026449 ◽

2021 ◽

Vol 18 (3) ◽

pp. 172988142110264

Author(s):

Jiqing Chen ◽

Chenzhi Tan ◽

Rongxian Mo ◽

Hongdu Zhang ◽

Ganwei Cai ◽

...

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Obstacle Avoidance ◽

Potential Field ◽

Path Length ◽

Search Time ◽

Experimental Results ◽

Artificial Potential Field ◽

A Algorithm ◽

Neighbor Search

Among the shortcomings of the A* algorithm, for example, there are many search nodes in path planning, and the calculation time is long. This article proposes a three-neighbor search A* algorithm combined with artificial potential fields to optimize the path planning problem of mobile robots. The algorithm integrates and improves the partial artificial potential field and the A* algorithm to address irregular obstacles in the forward direction. The artificial potential field guides the mobile robot to move forward quickly. The A* algorithm of the three-neighbor search method performs accurate obstacle avoidance. The current pose vector of the mobile robot is constructed during obstacle avoidance, the search range is narrowed to less than three neighbors, and repeated searches are avoided. In the matrix laboratory environment, grid maps with different obstacle ratios are compared with the A* algorithm. The experimental results show that the proposed improved algorithm avoids concave obstacle traps and shortens the path length, thus reducing the search time and the number of search nodes. The average path length is shortened by 5.58%, the path search time is shortened by 77.05%, and the number of path nodes is reduced by 88.85%. The experimental results fully show that the improved A* algorithm is effective and feasible and can provide optimal results.

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