Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity

Das, Pradipta Kumar; Behera, Himansu Sekhar; Panigrahi, B. K.

doi:10.1016/j.jestch.2015.09.009

Cited by 57 publications

(30 citation statements)

References 10 publications

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“…Thus, without loss of generality and for simplicity, the subscript m in each variable in (9)-(10) can be omitted in terms of the convergence investigation of the proposed SAEGBPSO. Also, please note that since every dimension in the velocity and position vectors of each particle in SAEGBPSO is updated independently from the others in the moving rules defined in (9)-(10), the moving rule defined by (9)- (10) in SAEGBPSO can be simplified and rewritten into a one-dimensional matrix form as follows…”

Section: Convergence Investigation Of Saegbpsomentioning

confidence: 99%

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Multi-robot path planning using an improved self-adaptive particle swarm optimization

Tang

Xiang

Pang

et al. 2020

International Journal of Advanced Robotic Systems

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Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.

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Section: Convergence Investigation Of Saegbpsomentioning

confidence: 99%

“…Update the velocity of each particle based on (9) 13. Update the position of each particle based on (10) 14. Modify the position vector of each particle based on the saturation strategy given by (48) 15.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Multi-robot path planning using an improved self-adaptive particle swarm optimization

Tang

Xiang

Pang

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

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“…The strategy iteration algorithm repeats each iteration process until the optimal strategy * is converged. According to Equation (4), the optimal strategy * can be expressed as Equation (6)…”

Section: A Algorithm Principlementioning

confidence: 99%

Robot Path Planning based on an Improved Q-learning Method

2019

Electrical Engineering and Computer Science (EECS)

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Path planning is one of the most basic and pivotal aspects in research of robots, which is to solve the walking problem of robots. As a widely used reinforcement learning method, Q-learning is employed when the robot has no prior knowledge of how its actions affect its environment. For Q-learning method, there is a problem of explorationutilization in robot path planning. Therefore, robot path planning based on an improved Q-learning method is proposed. According to the environment in which the robot is located, a Markov decision model is established to design the rewardpunishment mechanism and action strategy of the robot in the path planning. During the robot training process, a heuristic search function is defined and added to the value iteration algorithm in order to reduce the invalid path exploration in the environment. The experimental results show that the proposed method not only reduces the length of path and improves the efficiency of path planning, but also accelerates the speed of robot learning. This indicates the effectiveness of the proposed method.

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“…In this regard, various improved Q-learning models were introduced, in which improving the performance of Q-learning by applying the optimization algorithm as modifier [14,[18][19][20][21][22] was one of the proposed solutions. Das applied the improved particle swarm optimization (IPSO) with perturbed velocity in Q-learning algorithm in order to improve its global search ability and convergence rate [19]. The results showed that the robots were able to perform well even several obstacles were considered in the working environment.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, the tendency of scores of the implemented algorithm is rising as opposed to opponent team which is dropping. While in path planning, Euclidean distance between robots' current position and target position and Euclidean distance among robot with other robots had been used to form the overall fitness function [19]. In this case study, dynamic obstacles are considered, as robots act as dynamic obstacles among each other.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning using Q-Learning with Guided Distance

Low

Ong

Low

2018

IJET

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In path planning for mobile robot, classical Q-learning algorithm requires high iteration counts and longer time taken to achieve convergence. This is due to the beginning stage of classical Q-learning for path planning consists of mostly exploration, involving random direction decision making. This paper proposed the addition of distance aspect into direction decision making in Q-learning. This feature is used to reduce the time taken for the Q-learning to fully converge. In the meanwhile, random direction decision making is added and activated when mobile robot gets trapped in local optima. This strategy enables the mobile robot to escape from local optimal trap. The results show that the time taken for the improved Q-learning with distance guiding to converge is longer than the classical Q-learning. However, the total number of steps used is lower than the classical Q-learning.

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Intelligent-based multi-robot path planning inspired by improved classical Q-learning and improved particle swarm optimization with perturbed velocity

Cited by 57 publications

References 10 publications

Multi-robot path planning using an improved self-adaptive particle swarm optimization

Multi-robot path planning using an improved self-adaptive particle swarm optimization

Robot Path Planning based on an Improved Q-learning Method

Mobile Robot Path Planning using Q-Learning with Guided Distance

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