A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment

Das, Pradipta Kumar; Behera, Himansu Sekhar; Das, Swagatam; Tripathy, Hrudaya Kumar; Panigrahi, B. K.; Pradhan, Saroj Kumar

doi:10.1016/j.neucom.2016.05.057

Cited by 109 publications

(46 citation statements)

References 27 publications

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“…The number of pigeons decreases by half after each iteration in a conventional QPIO algorithm, according to Equation (14). However, if the pigeon number decreases too fast, only one pigeon can survive after a small amount of iterations eventually, which prevents globally searching for the algorithm.…”

Section: Adaptive Compression Factor Strategymentioning

confidence: 99%

“…In recent decades, inspired by the organized behavior of natural biological groups, numerous swarm-intelligence optimization algorithms have been proposed to be applied to UAV path planning problem [11,12]. Notable examples include ant colony optimization algorithm (ACO) [13], particle swarm optimization algorithm (PSO) [14], fruit fly optimization algorithm (FOA) [15], and pigeon-inspired optimization algorithm (PIO) [16]. Various merits, including simple structure, general problem adaptability, and rapid search rate, make swam-intelligence optimization algorithms a promising tool for solving UAV path planning problems, especially under varied and complex environments.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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Section: Adaptive Compression Factor Strategymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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“…Some examples of its application can be found for guiding robots for targets searching in complex and noisy environment as presented in [13]. Modified versions of the PSO are proposed to balance searching and selecting in a collective clean-up task [16] for path planning in a clutter environment [11] and for mimicking natural selection emulated using the principles of social exclusion and inclusion [9].…”

Section: Related Work 21 Multi-robot Allocation and Coordinationmentioning

confidence: 99%

“…For the ease of presentation, the robots start the search simultaneously at the same time. These assumptions, that can easily be removed in the future, but for the moment this is to (4,5) and (11,9) have detected a target. They start a recruitment process by sending packets that will be received by the robots within their wireless range R t .…”

Section: Assumptionsmentioning

confidence: 99%

“…The third is in the direction of the detected targets (to perform an disarming task cooperatively). The first two are based on interactions accumulated Figure 5: (a) The robot in the cell (6,11) that is recruited by the robot in cell (6,8) moves into a cell that is too far from the target, thus it changes its state by becoming an explorer. (b) The robot in cell (9,9) that is recruited by two robots in both cells (6,8) and (10,6), respectively.…”

Section: Moving Actions In the Cells Which Imply Rotation To Choomentioning

confidence: 99%

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Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

et al. 2018

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This work addresses the coordination problem of multiple robots with the goal of finding specific hazardous targets in an unknown area and dealing with them cooperatively. The desired behaviour for the robotic system entails multiple requirements, which may also be conflicting. The paper presents the problem as a constrained bi-objective optimization problem in which mobile robots must perform two specific tasks of exploration and at same time cooperation and coordination for disarming the hazardous targets. These objectives are opposed goals, in which one may be favored, but only at the expense of the other. Therefore, a good trade-off must be found. For this purpose, a nature-inspired approach and an analytical mathematical model to solve this problem considering a single equivalent weighted objective function are presented. The results of proposed coordination model, simulated in a two dimensional terrain, are showed in order to assess the behaviour of the proposed solution to tackle this problem. We have analyzed the performance of the approach and the influence of the weights of the objective function under different conditions: static and dynamic. In this latter situation, the robots may fail under the stringent limited budget of energy or for hazardous events. The paper concludes with a critical discussion of the experimental results.

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A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems

Baskaran

et al. 2021

Concurrency and Computation

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One of the most frequent issues in multiple robot implementation is task allocation with the lowest path cost. Our study addresses the multi‐robot task allocation challenge with path costs, the lowest computing time, and task distribution. Furthermore, it is usual for a robot's processing capabilities to be restricted to operate in various target environments. As a consequence, adequate processing power consumption would demonstrate the system's efficiency. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. Task allocation and path planning issues must be addressed regularly to ensure multi‐robot system operation. The above‐mentioned serious challenge gets more complicated when system factors such as robots and tasks multiply. As previously stated, this article solves the issue using a fuzzy‐based optimum path and reverse auction‐based methods. The detailed simulation results indicate that the suggested methods can solve task allocation with the lowest path cost. A comparative study is conducted between the suggested algorithm and two existing commonly used techniques, the auction‐based and the Hungarian algorithms. Finally, the suggested method was run in real‐time on a TurtleBot2 robot. The findings show the suggested algorithm's efficiency and simplicity of implementation.

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A hybrid improved PSO-DV algorithm for multi-robot path planning in a clutter environment

Cited by 109 publications

References 27 publications

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Self-adaptive decision-making mechanisms to balance the execution of multiple tasks for a multi-robots team

A novel fuzzy and reverse auction‐based algorithm for task allocation with optimal path cost in multi‐robot systems

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