Path-planning research in radioactive environment based on particle swarm algorithm

Liu, Yong-kuo; Li, Mengkun; Xie, Changsheng; Peng, Minjun; Xie, Fei

doi:10.1016/j.pnucene.2014.03.002

Cited by 54 publications

(12 citation statements)

References 7 publications

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“…(a) Case One: Ten Targets Need to Be Rescued. Consider the situation that ten targets need to be rescued, and their positions are (70, 0), (60, 25), (80, 40), (80, 80), (20,45), (15,65), (36,87), (41,22), (95,20), and (2,11). From formula (4), the initial strengths (after the disaster) of all these targets are 14.03, 9.05, 6.71, 3.61, 10.30, 10.05, 6.73, 10.73, 11.66, and 17.26, respectively.…”

Section: Simulation Results Of Two Casesmentioning

confidence: 99%

“…The positions of the 100 targets in case two are listed as follows: (70, 0), (60, 25), (82, 40), (80, 80), (20,45), (15,65), (36,87), (41,22), (95, 50), (2,11), (73, 10), (60, 35), (8,50), (20,90), (20,55), (15,76), (36,70), (41, 12), (95, 52), (12,17), (76,8), (60, 15), (29,30), (80, 70), (20, 35, (15, 55), (36,47), (41, 12), (95, 40), (2,31), (71,20), (60, 70), (11,90), (89,20), (20,2), (15,40), (36,65), (41, 6), (95, 40), (2,15), (70, 27), (60, 10), (88, 40), (26,60), (20,30), (15,3...…”

Section: Appendixmentioning

confidence: 99%

“…PSO was originally proposed by Kennedy and Eberhard [12]. Since it has a simple structure, fast convergence, and few parameters to be set, it has been widely applied in the field of robot path planning [13][14][15]. However, there have been no studies on robot rescue path planning with time-constraint.…”

Section: Introductionmentioning

confidence: 99%

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PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Geng

Gong

Zhang

2014

Mathematical Problems in Engineering

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Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning for multisurvivor rescue in limited survival time using a representative heuristic, particle swarm optimization (PSO). First, the robot path planning problem including multiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of our method in generating optimal paths with high quality.

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Section: Simulation Results Of Two Casesmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

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PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Geng

Gong

Zhang

2014

Mathematical Problems in Engineering

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“…A fitness-scaling adaptive Chaotic PSO approach was presented to solve the path planning of UCAVs [46]. Based on PSO, Liu et al [47] introduced some key technologies for path planning in radiation environment. The probability and effectiveness of the method is verified by the experiment.…”

Section: Psomentioning

confidence: 99%

Path Planning for the Mobile Robot: A Review

2018

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Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.

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“…The idea of GA is also used in the path planning using BEA which more adaptive than GA [9][10] [11]. The other evolutionary algorithms that were used to produce path planning were PSO [11] [12], [14], [15]. Each particle represents a potential solution that is evaluated by three factors: position, velocity, and adaptability.…”

Section: Related Workmentioning

confidence: 99%

An Integrated Artificial Potential Field Path Planning with Kinematic Control for Nonholonomic Mobile Robot

Triharminto¹,

Wahyunggoro²,

Adji³

et al. 2016

International Journal on Advanced Science, Engineering and Information Technology

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In this paper, path planning which is based on Artificial Potential Field (APF) and the kinematic based control is integrated in order to solve an issue in the APF. Usually, the APF assumes the robot is modeled as a point mass. It means that the robot can move in any direction and neglect the nonholonomic constraint. In order to solve such a problem, the APF should be considered as part of the control system. This research proposed an approach integrating APF and control system under nonholonomic constraint. Naturally, the force of the APF can be used as linear velocity in the control system. Then, waypoint of APF is used as equilibrium point of kinematic control. In order to validate the proposed method, the experimental setup conducted on loop simulation. The scenario is that the robot moves along the certain trajectory to reach the goal point. The obstacle was set in between the robot and the goal point. The initial, goal, and the obstacles are set randomly. The experiments show that the integration of the proposed method can be implemented successfully. The real obstacle avoidance method and fulfilling the nonholonomic constraint are the proof that the method is running well. The results show that the integrated proposed method meets convergent and stable.

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Path-planning research in radioactive environment based on particle swarm algorithm

Cited by 54 publications

References 7 publications

PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

PSO-Based Robot Path Planning for Multisurvivor Rescue in Limited Survival Time

Path Planning for the Mobile Robot: A Review

An Integrated Artificial Potential Field Path Planning with Kinematic Control for Nonholonomic Mobile Robot

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