Mobile robot path planning based on adaptive bacterial foraging algorithm

Liang, Xiaodan; Li, Liangyu; Wu, Jigang; Chen, Hanning

doi:10.1007/s11771-013-1864-5

Cited by 51 publications

(21 citation statements)

References 18 publications

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“…In Figure 10(a), it can be seen that the green quadrotor will collide with the yellow one at the position of (5, 5), while Figure 10(b) shows that the green quadrotor will avoid the moving obstacle of yellow quadrotor at the position of (4.5, 4.5). In this experiment the robot is placed at the initial position of (10,10) moving to the goal position of (90, 100) in the environment having four circular obstacles located at the position of (3,3), (3,6 ), (6, 2.5), and (6, 7) as shown in Figure 11. The figure is simulated by two methods namely fuzzy cell decomposition method indicated by the green line and fuzzy cell decomposition that has been modified with the potential field indicated by the blue line.…”

Section: Results and Analysismentioning

confidence: 99%

“…Foragin bacteria algorithm used by Liang et al, [6] is one of the heuristic algorithms having characteristics like bacteria in which this algorithm can create a contour map of the environment that is used to avoid obstacles and move to goal position in a static environment. Honey Bee Mating Optimization algorithm used by Rashmi et al, [7] for multi-robot path planning has a characteristic like insect colony in which this algorithm can make maps of the safest path and avoid obstacles to move to goal position.…”

Section: Introductionmentioning

confidence: 99%

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Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

2016

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Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

2016

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“…Hossain & Ferdousand [149] have applied Bacterial Foraging Optimization (BFO) method for mobile robot navigation to find out shortest possible path within the minimum time from the start position to the goal position between moving obstacles. Liang et al [150] have developed a bacterial foraging algorithm for making a bio-inspired path planning strategy for a mobile robot. In the proposed model, the behavior of bacteria is applied to search an optimal collision-free path between the start nodes to the target node in an environment with obstacles.…”

Section: Ant Colony Optimization Algorithm and Other Nondeterministicmentioning

confidence: 99%

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Pandey¹

2017

IRATJ

184

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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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“…9,10 Recently, researchers have been employing algorithms, inspired from natural biological processes to generate an optimal path for a mobile robot. Metaheuristic optimization techniques like Genetic Algorithm, 11,12 Ant Colony Optimization, [13][14][15] Cuckoo Search technique, 16 Artificial Immune System, [17][18][19] PSO, 20,21 and Bacteria Foraging Optimization 22 have been proved to be efficient in the motion planning of a mobile robot. Although these algorithms have gained acceptance in efficiency to get the optimal solution in the search space, however, they involve large computations and complicated coding.…”

Section: State Of Artsmentioning

confidence: 99%

Optimal path planning for mobile robots using oppositional invasive weed optimization

Panda

Das

Dutta

et al. 2018

Computational Intelligence

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A mobile robot is an autonomous agent, capable of planning the path from source to destination in both, a known, or an unknown environment. In this paper, we presented a novel approach to find the optimal trajectory. We have hybridized oppositional-based learning (OBL) with the evolutionary invasive weed optimization (IWO) technique to generate the optimal path for different robot(s). The navigation algorithm considered in this work is intelligent enough to fulfill the objective of minimizing the path length and the time to reach its specified goal. Oppositional IWO (OIWO) algorithm mimics the colonizing behavior of weed and uses the concept of quasi-opposite number, the concept of OBL is to find the shortest path between source and destination of mobile robot(s). An objective function has been formulated that takes care of the optimal target-seeking behavior as well as the obstacle avoidance of the mobile robot. In this technique, OBL considers the current population generated by IWO algorithm and its opposite population simultaneously to get better and faster convergence. The simulation and experimental results prove and validate the effectiveness of developed OIWO path planning algorithm.

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Mobile robot path planning based on adaptive bacterial foraging algorithm

Cited by 51 publications

References 18 publications

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

Quadrotor Path Planning Based on Modified Fuzzy Cell Decomposition Algorithm

Mobile Robot Navigation and Obstacle Avoidance Techniques: A Review

Optimal path planning for mobile robots using oppositional invasive weed optimization

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