World representation and path planning for a mobile robot

Robotica ◽  
1988 ◽  
Vol 6 (1) ◽  
pp. 35-40 ◽  
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.

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

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.

scholarly journals Path Planning of Mobile Robot in Unknown Environment

2010 ◽  
pp. 122-127
Author(s):  
Pradipta kumar Das ◽  
Romesh Laishram ◽  
Amit Konar
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Cost Function ◽  
Shortest Path ◽  
Target Position ◽  
Initial Position ◽  
A Algorithm ◽  
Unknown Environment ◽  
Feasible Path ◽  
The Cost

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.

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

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.  

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

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.

scholarly journals Design and performance analysis of global path planning techniques for autonomous mobile robots in grid environments

2017 ◽  
Vol 14 (2) ◽  
pp. 172988141666366 ◽  
Author(s):  
Imen Chaari ◽  
Anis Koubaa ◽  
Hachemi Bennaceur ◽  
Adel Ammar ◽  
Maram Alajlan ◽  
...  
Keyword(s):  
Path Planning ◽  
Execution Time ◽  
Large Scale ◽  
Optimal Path ◽  
Heuristic Methods ◽  
Solution Quality ◽  
Good Trade ◽  
And Performance ◽  
Path Planner ◽  
The Cost

This article presents the results of the 2-year iroboapp research project that aims at devising path planning algorithms for large grid maps with much faster execution times while tolerating very small slacks with respect to the optimal path. We investigated both exact and heuristic methods. We contributed with the design, analysis, evaluation, implementation and experimentation of several algorithms for grid map path planning for both exact and heuristic methods. We also designed an innovative algorithm called relaxed A-star that has linear complexity with relaxed constraints, which provides near-optimal solutions with an extremely reduced execution time as compared to A-star. We evaluated the performance of the different algorithms and concluded that relaxed A-star is the best path planner as it provides a good trade-off among all the metrics, but we noticed that heuristic methods have good features that can be exploited to improve the solution of the relaxed exact method. This led us to design new hybrid algorithms that combine our relaxed A-star with heuristic methods which improve the solution quality of relaxed A-star at the cost of slightly higher execution time, while remaining much faster than A* for large-scale problems. Finally, we demonstrate how to integrate the relaxed A-star algorithm in the robot operating system as a global path planner and show that it outperforms its default path planner with an execution time 38% faster on average.

Mobile Robot Path Planning using Voronoi Diagram and Fast Marching

2015 ◽  
pp. 92-108 ◽  
Author(s):  
S. Garrido ◽  
L. Moreno
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Voronoi Diagram ◽  
High Speed ◽  
Fast Marching Method ◽  
Robot Path Planning ◽  
Fast Marching ◽  
Path Planner ◽  
Marching Method ◽  
Robot Path

This chapter presents a new sensor-based path planner, which gives a fast local or global motion plan capable to incorporate new obstacles data. Within the first step, the safest areas in the environment are extracted by means of a Voronoi Diagram. Within the second step, the fast marching method is applied to the Voronoi extracted areas so as to get the trail. This strategy combines map-based and sensor-based designing operations to supply a reliable motion plan, whereas it operates at the frequency of the sensor. The most interesting characteristics are high speed and reliability, as the map dimensions are reduced to a virtually one-dimensional map and this map represents the safest areas within the environment.

Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

Robotica ◽  
2014 ◽  
Vol 34 (4) ◽  
pp. 823-836 ◽  
Author(s):  
Hamed Shorakaei ◽  
Mojtaba Vahdani ◽  
Babak Imani ◽  
Ali. Gholami
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Cost Function ◽  
Simple Formula ◽  
Parallel Genetic Algorithm ◽  
Probability Map ◽  
Genetic Encoding ◽  
Cell Position ◽  
The Cost ◽  
Unique Vector

SUMMARYThe current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

scholarly journals Hexagonal Grid-Based Framework for Mobile Robot Navigation

2021 ◽  
Vol 13 (21) ◽  
pp. 4216
Author(s):  
Piotr Duszak ◽  
Barbara Siemiątkowska ◽  
Rafał Więckowski
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Robot Navigation ◽  
Hexagonal Lattice ◽  
Planning Method ◽  
Experimental Results ◽  
Mobile Robot Navigation ◽  
Rectangular Grid ◽  
Grid Based

The paper addresses the problem of mobile robots’ navigation using a hexagonal lattice. We carried out experiments in which we used a vehicle equipped with a set of sensors. Based on the data, a traversable map was created. The experimental results proved that hexagonal maps of an environment can be easily built based on sensor readings. The path planning method has many advantages: the situation in which obstacles surround the position of the robot or the target is easily detected, and we can influence the properties of the path, e.g., the distance from obstacles or the type of surface can be taken into account. A path can be smoothed more easily than with a rectangular grid.

A New Real Time Path Planning for Mobile Robot Navigation Using Invasive Weed Optimization Algorithm

2014 ◽  
Author(s):  
Prases K. Mohanty ◽  
Dayal R. Parhi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Fitness Function ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Invasive Weed Optimization ◽  
Invasive Weed ◽  
Planning Algorithm ◽  
Path Planner

In this article a new optimal path planner for mobile robot navigation based on invasive weed optimization (IWO) algorithm has been addressed. This ecologically inspired algorithm is based on the colonizing property of weeds and distribution. A new fitness function has been formed between robot to goal and obstacles, which satisfied the conditions of both obstacle avoidance and target seeking behavior in robot present in the environment. Depending on the fitness function value of each weed in the colony the robot that avoids obstacles and navigating towards goal. The optimal path is generated with this developed algorithm when the robot reaches its destination. The effectiveness, feasibility, and robustness of the proposed navigational algorithm has been performed through a series of simulation and experimental results. The results obtained from the proposed algorithm has been also compared with other intelligent algorithms (Bacteria foraging algorithm and Genetic algorithm) to show the adaptability of the developed navigational method. Finally, it has been concluded that the proposed path planning algorithm can be effectively implemented in any kind of complex environments.

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