Robot path planning in dynamic environments using a simulated annealing based approach

Miao, Hui; Tian, Yu-Chu

doi:10.1109/icarcv.2008.4795701

Cited by 49 publications

(51 citation statements)

References 11 publications

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“…The unit meter (m) used in the simulations can be multiply by any scale (*10 for example). Starting point is (0,0) and target point is (10,10). MATLABR2011b programming language used to create the simulation code for path planning using 2.13GHz processor and 2 GB RAM.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…2) Local Path Planning (LPP): is usually constructed online when the robot avoids the obstacles in a real time environment [10]. In this paper, global path planning is adopted where the environment is static and totally known.…”

Section: Path Planning and Problem Formulationmentioning

confidence: 99%

“…The proposed fitness is formulated as follows, (10) where and are coefficients effect the importance of the two objectives. Since the main goal is to have a short path, the value of would be larger than .…”

Section: (8)mentioning

confidence: 99%

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Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Abbas¹,

Ali²

2014

IJCA

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This paper describes the problem of offline autonomous mobile robot path planning, which is consist of generating optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An improved algorithm for solving the problem of path planning using Artificial Bee Colony algorithm is presented. This natureinspired metaheuristic algorithm, which imitates the foraging behavior of bees around their hive, is used to find the optimal path from a starting point to a target point. The proposed algorithm is demonstrated by simulations in three different environments. A comparative study is evaluated between the developed algorithm, the original ABC and other two state-ofthe-art algorithms. This study shows that the proposed method is effective and gets trajectories with satisfactory results. General TermsNature-inspired algorithm, Heuristic algorithms

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Path Planning and Problem Formulationmentioning

confidence: 99%

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Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Abbas¹,

Ali²

2014

IJCA

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“…Hussein et al [114] have designed three metaheuristic optimization algorithms: Tabu Search, Simulated Annealing and Genetic Algorithm; and implemented these algorithms to improve the navigation performance of mobile robot from the start point to goal point in an environment. Miao and Tian [115] have presented a simulated annealing algorithm based intelligent navigational controller, which helps the robot to search an optimal or near-optimal path in the static and dynamic environments. Zhang et al [116] have combined the simulated annealing algorithm and Ant Colony Optimization (ACO) algorithm to increase the navigation speed of the mobile robot.…”

Section: Simulated Annealing Algorithm For Mobile Robot Navigationmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

186

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Mobile robot is an autonomous agent capable of navigating intelligently anywhere using sensor-actuator control techniques. The applications of the autonomous mobile robot in many fields such as industry, space, defence and transportation, and other social sectors are growing day by day. The mobile robot performs many tasks such as rescue operation, patrolling, disaster relief, planetary exploration, and material handling, etc. Therefore, an intelligent mobile robot is required that could travel autonomously in various static and dynamic environments. Several techniques have been applied by the various researchers for mobile robot navigation and obstacle avoidance. The present article focuses on the study of the intelligent navigation techniques, which are capable of navigating a mobile robot autonomously in static as well as dynamic environments. Keywords: IntroductionThis article introduces the literature survey of the various techniques used for mobile robot navigation. Navigation and obstacle avoidance are one of the fundamental problems in mobile robotics, which are being solved by the various researchers in the past two decades. The aim of navigation is to search an optimal or suboptimal path from the start point to the goal point with obstacle avoidance competence. Basically, the mobile robot navigation has been done by the Deterministic algorithm and Nondeterministic (Stochastic) algorithm. Nowadays, the hybridization of both the algorithms called as an Evolutionary algorithm is being used to solve the mobile robot navigation problem. Figure 1 shows the general classification of the Deterministic algorithm, Nondeterministic (Stochastic) algorithm, and Evolutionary algorithm, which are implemented for mobile robot navigation by various authors.

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“…In the artificial potential field method, we can imagine that all obstacles can generate repulsive force to the robot that is inversely proportional to the distance from the robot to obstacles and is pointing away from obstacles, while the destination or goal has attractive force that attracts robot to the goal. The combination of these two forces will generate a total force with magnitude and direction, the mobile robot should follow that direction to avoid obstacles and reach to the target in a safe path [2]. Actually the artificial potential field method uses a scalar function called the potential function [3].…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Mobile Robot by using Modified Optimized Potential Field Method

Ahmed¹,

Abdalla²,

Abed³

2015

IJCA

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This paper deals with the navigation of a mobile robot in an unknown environment by using artificial potential field (APF) method. The aim is to develop a method for path planning of mobile robot from start point to the goal point while avoiding obstacles on robot's path. Artificial potential field method will be modified and optimized by using particle swarm optimization (PSO) algorithm to solve the drawbacks such as local minima and improve the quality of the trajectory of mobile robot.

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Robot path planning in dynamic environments using a simulated annealing based approach

Cited by 49 publications

References 11 publications

Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Path Planning of an Autonomous Mobile Robot using Directed Artificial Bee Colony Algorithm

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Path Planning of Mobile Robot by using Modified Optimized Potential Field Method

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