Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

Jaradat, Mohammad A.; Garibeh, Mohammad H.; Feilat, Eyad A.

doi:10.1007/s00500-011-0742-z

Cited by 97 publications

(36 citation statements)

References 21 publications

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“…Baxter et al [28,29] used the approach to plan the movement of multiple robots that could share potential values to correct each other's environment perceptions. The method has been applied in conjunction with fuzzy logic using a variety of techniques, creating hybrid potential-fuzzy planners [30,31].…”

Section: Related Researchmentioning

confidence: 99%

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Kala

Warwick

2015

Electronics

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Autonomous vehicles operate in real time traffic scenarios and aim to reach their destination from their source in the most efficient manner possible. Research in mobile robotics provides a variety of sophisticated means with which to plan the path of these vehicles. Conversely professional human drivers usually drive using instinctive means, which enables them to reach their goal almost optimally whilst still obeying all traffic laws. In this paper we propose the use of fuzzy logic for novel motion planning. The planner is generated using an evolutionary algorithm which resembles the learning stage of professional drivers. Whether to overtake or not, is a decision which affects one's driving and the decision is made using some deliberation. We further extend the approach to perform decision making regarding overtaking for all vehicles. Further we coordinate the motion of the vehicles at a traffic crossing to avoid any potential jam or collision. Experimental results prove that by using this approach we have been able to make the vehicles move in an optimal manner in a variety of scenarios.

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Section: Related Researchmentioning

confidence: 99%

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Kala

Warwick

2015

Electronics

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“…Path planning is one of the hot spots in the field of mobile robots, which means finding an optimal or near optimal collision-free path from the starting position to the target position in an environment with obstacles [1][2][3][4]. There have been various methods used for robot path planning, and the classical methods include artificial potential field methods [5], grid map representation methods [6] visibility graph methods [7].…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Ni¹,

Wang²,

Cao³

et al. 2017

International Journal of Robotics and Automation

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There are some shortcomings of the traditional methods for robot path planning, such as the local minimum problem and the low convergence speed in the genetic algorithm (GA)-based methods.In this paper, an improved memetic algorithm is proposed for robot path planning. In the global search process of the proposed memetic algorithm, a GA with variable length chromosome based on improved two-point crossover and bacterial mutation is used to avoid the local optimum problem. And a search method which combines a neighbouring local search and a disorder strategy is used in the local search process of the proposed approach to speed up the convergence procedure. Furthermore, a dynamic module is added in the proposed approach to deal with the path planning in dynamic environments. Finally, some simulation and real robot experiments are carried out and the experimental results show the efficiency and the effectiveness of the proposed algorithm in the path planning of mobile robots.

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“…Fuzzy logic controller (FLC) [4][5][6][7][8] is widely used in MR navigation because it is able to emulate human reasoning capabilities of dealing with uncertainties [9]. However, the design of a fuzzy controller is difficult and highly time consuming task, as there are lots of parameters values to define [10].…”

Section: Introductionmentioning

confidence: 99%

“…Artificial potential field (APF) [6,14,[19][20][21][22][23][24] is one of the most commonly used schemes in the field of MR navigation [18]. In the work of Ge and Cui [20], the authors apply APF in a dynamic environment containing a moving target.…”

Section: Introductionmentioning

confidence: 99%

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Findi¹,

Marhaban²,

Kamil³

et al. 2017

Journal of Electrical Engineering and Technology

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-The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

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Autonomous mobile robot dynamic motion planning using hybrid fuzzy potential field

Cited by 97 publications

References 21 publications

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

Reactive Planning of Autonomous Vehicles for Traffic Scenarios

A Memetic Algorithm With Variable Length Chromosome for Robot Path Planning Under Dynamic Environments

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

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