Path planning in uncertain environment by using firefly algorithm

Patle, B.K.; Pandey, Anish; Jagadeesh, A.; Parhi, Dayal R.

doi:10.1016/j.dt.2018.06.004

Cited by 151 publications

(57 citation statements)

References 12 publications

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“…Traditional material handling equipment makes the production system rigid to change in layout and process routing. The availability of technologies using Artificial Intelligence for positioning and navigation (Fuentes-Pacheco et al 2015;Patle et al 2018) can support the improvements in transportation in production systems making use of intelligent vehicles, such as the AMR in order to obtain feasible solutions in increasing the flexibility and productivity of the production systems.…”

Section: Introductionmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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Manufacturing flexibility improves a firm's ability to react in timely manner to customer demands and to increase production system productivity without incurring excessive costs and expending an excessive amount of resources. The emerging technologies in the Industry 4.0 era, such as cloud operations or industrial Artificial Intelligence, allow for new flexible production systems. We develop and test an analytical model for a throughput analysis and use it to reveal the conditions under which the autonomous mobile robots (AMR)-based flexible production networks are more advantageous as compared to the traditional production lines. Using a circular loop among workstations and inter-operational buffers, our model allows congestion to be avoided by utilizing multiple crosses and analyzing both the flow and the load/unload phases. The sensitivity analysis shows that the cost of the AMRs and the number of shifts are the key factors in improving flexibility and productivity. The outcomes of this research promote a deeper understanding of the role of AMRs in Industry 4.0-based production networks and can be utilized by production planners to determine optimal configurations and the associated performance impact of the AMR-based production networks in as compared to the traditionally balanced lines. This study supports the decision-makers in how the AMR in production systems in process industry can improve manufacturing performance in terms of productivity, flexibility, and costs.

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Section: Introductionmentioning

confidence: 99%

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

et al. 2020

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“…In addition to PSO and BA, the Brain Storm optimization algorithm was used for the exploration in another paper [19]. Other than the exploration, the nature-inspired optimization algorithms are widely used for the path planning problem in the mobile robotics [20] - [22].…”

Section: Related Workmentioning

confidence: 99%

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

2020

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This paper proposes stochastic exploration algorithms for mobile robot exploration problems. Navigation with uncertain conditions in the absence of initial parameters is a situation wherein precomputation and prediction are impossible for a robot. Therefore, stochastic optimization techniques were applied to find the optimal solution for the robot exploration problem. Driving to the unknown areas, the robot updates the frontier line of sensor visibility during the exploration mission. The points of the frontier line are assumed as the swarm population with their own positions and costs, which allows the computation of the next global waypoint. The calculation of global waypoints is carried out by a nature-inspired optimization algorithm that can place a waypoint in uncertainties. This study offers to apply three metaheuristic algorithms individually, such as Whale Optimization, Grey Wolf Optimizer, and Particle Swarm Optimization algorithms, for comparison and testing their performances in the mobile robotics. At first, the simulations based on the proposed exploration algorithms were implemented and evaluated in a created environment. The results were compared in a single and average cases. Then, the real-world experiments using Grey Wolf Optimizer exploration algorithm were conducted in the different types of environments using MATLAB-ROS integration tool. These results proved the effectiveness and applicability of the bio-inspired optimization algorithm in the mobile robotics.

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“…Nowadays, the number of methods for path planning in the dynamic enviroment considerably increases ( [2,40,41,55,59]). But, they generally refer to the problem of path planning in the dynamic environment in the context of autonomous mobile robots navigation or unmanned aerial vehicles.…”

Section: Transit-based Routingmentioning

confidence: 99%

Intelligent route planning system based on interval computing

Chmiel

Skalna

Jędrusik

2018

Multimed Tools Appl

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We investigate the problem of vehicle route planning in a dynamic environment. In order to better reflect real-life situations, we assume that travel times are not known exactly, but bounded from below and from above, i.e., they are given as interval quantities. Accordingly, we develop algorithms for effective route replanning in a highly dynamic road network environment that combines traffic image processing with interval data for dynamic path optimisation. The developed algorithms are integrated into a larger system for traffic management. The efficiency of the proposed algorithms and their ability to support the dynamics of road traffic is verified using real data. The experimental research was also conducted using the microscopic, time-discrete, space-continuous traffic simulator.

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Path planning in uncertain environment by using firefly algorithm

Cited by 151 publications

References 12 publications

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Increasing flexibility and productivity in Industry 4.0 production networks with autonomous mobile robots and smart intralogistics

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

Intelligent route planning system based on interval computing

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