A new path planning and obstacle avoidance algorithm in dynamic environment

Mohammadi, Afshin; Rahimi, Meysam; Suratgar, Amir Abolfazl

doi:10.1109/iraniancee.2014.6999735

Cited by 13 publications

(10 citation statements)

References 8 publications

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“…In Fig. 1, the starting point S of the robot is (7,7), the target point G is (9,18), the radius of VD is 4, and the VD indicated by the size of the dotted line is 9 × 9. At any time, the robot can only walk directly to one of its adjacent free grids.…”

Section: Environment Modelingmentioning

confidence: 99%

“…At any time, the robot can only walk directly to one of its adjacent free grids. For example, the accessible grids for the robot are limited to the set {(6,6), (7,6), (8,6), (6,7), (6,8), (7,8), (8,8)} when the current position is S.…”

Section: Environment Modelingmentioning

confidence: 99%

“…Case2: If G is included in VD (gR) and the Statek value of the antk is 1, the length is calculated according to (7).…”

Section: Robot Path Rolling Planning Based On Ant Colony Optimizationmentioning

confidence: 99%

“…Heuristic approaches are particularly useful when the environment is more complex, and have shown good results in overcoming the limitations of traditional methods. Representative methods include the A* algorithm [7], simulated annealing [8], rapidly-exploring random tree [9], genetic algorithm (GA) [10], fuzzy logic [11], artificial immune algorithm [12,13], ant colony optimization (ACO), etc. In particular, first developed for solving the travelling salesman problem, ant colony optimization has been heavily used in robot path planning because of its superiority in path planning [14].…”

mentioning

confidence: 99%

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A Hybrid Metaheuristic Navigation Algorithm for Robot Path Rolling Planning in an Unknown Environment

Xu¹,

Ho²,

Shum³

2019

Mechatronic Systems and Control

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In this paper, a new method for robot path rolling planning in a static and unknown environment based on grid modelling is proposed. In an unknown scene, a local navigation optimization path for the robot is generated intelligently by ant colony optimization (ACO) combined with the environment information of robot's local view and target information. The robot plans a new navigation path dynamically after certain steps along the previous local navigation path, and always moves along the optimized navigation path which is dynamically modified. The robot will move forward to the target point directly along the local optimization path when the target is within the current view of the robot. This method presents a more intelligent sub-goal mapping method comparing to the traditional rolling window approach. Besides, the path that is part of the generated local path based on the ACO between the current position and the next position of the robot is further optimized using particle swarm optimization (PSO), which resulted in a hybrid metaheuristic algorithm that incorporates ACO and PSO. Simulation results show that the robot can reach the target grid along a global optimization path without collision.

show abstract

Section: Environment Modelingmentioning

confidence: 99%

Section: Environment Modelingmentioning

confidence: 99%

“…Case2: If G is included in VD (gR) and the Statek value of the antk is 1, the length is calculated according to (7).…”

Section: Robot Path Rolling Planning Based On Ant Colony Optimizationmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Metaheuristic Navigation Algorithm for Robot Path Rolling Planning in an Unknown Environment

Xu¹,

Ho²,

Shum³

2019

Mechatronic Systems and Control

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“…The safe, optimal, and feasible paths produced by the path planning technique have been implemented successfully (Campbell and Naeem, 2012). When a USV operates in an obstacle field, the distance from the USV to the obstacles, the computational time, and the smoothness of the final path are essential factors that are employed to assess the ability of the USV (Mohammadi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Yoon

Ryu

et al. 2019

J. Ocean Eng. Technol.

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Efficient path planning is essential for unmanned surface vehicle (USV) navigation. The A* algorithm is an effective algorithm for identifying a safe path with optimal distance cost. In this study, a modified version of the A* algorithm is applied for planning the path of a USV in a static and dynamic obstacle environment. The current study adopts the A* approach while maintaining a safe distance between the USV and obstacles. Two important parameters-path length and computational time-are considered at various start times. The results demonstrate that the modified approach is effective for obstacle avoidance by a USV that is compliant with the International Regulations for Preventing Collision at Sea (COLREGs).This is an open access article distributed under the terms of the creative commons attribution non-commercial license (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

Tanveer¹,

Recchiuto²,

Sgorbissa³

2018

Robotica

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SUMMARYThe article presents the experimental evaluation of an integrated approach for path following and obstacle avoidance, implemented on wheeled robots. Wheeled robots are widely used in many different contexts, and they are usually required to operate in partial or total autonomy: in a wide range of situations, having the capability to follow a predetermined path and avoiding unexpected obstacles is extremely relevant. The basic requirement for an appropriate collision avoidance strategy is to sense or detect obstacles and make proper decisions when the obstacles are nearby. According to this rationale, the approach is based on the definition of the path to be followed as a curve on the plane expressed in its implicit formf(x, y) = 0, which is fed to a feedback controller for path following. Obstacles are modeled through Gaussian functions that modify the original function, generating a resulting safe path which – once again – is a curve on the plane expressed asf′(x, y) = 0: the deformed path can be fed to the same feedback controller, thus guaranteeing convergence to the path while avoiding all obstacles. The features and performance of the proposed algorithm are confirmed by experiments in a crowded area with multiple unicycle-like robots and moving persons.

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A new path planning and obstacle avoidance algorithm in dynamic environment

Cited by 13 publications

References 8 publications

A Hybrid Metaheuristic Navigation Algorithm for Robot Path Rolling Planning in an Unknown Environment

A Hybrid Metaheuristic Navigation Algorithm for Robot Path Rolling Planning in an Unknown Environment

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Analysis of path following and obstacle avoidance for multiple wheeled robots in a shared workspace

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