A review of metaheuristics in robotics

Fong, Simon; Deb, Suash; Chaudhary, Ankit

doi:10.1016/j.compeleceng.2015.01.009

Cited by 49 publications

(11 citation statements)

References 53 publications

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“…For instance, the simulation runs were aborted after the 98 th iteration during our experiment when one of the robots got stuck next to the wall obstacle. For the purpose of completing the exploration, the initial position of robot 2 or r 2 (from Table 1) was changed from (7,9) to (6,5). From these results, we can conclude that the exploration by the deterministic CME algorithm requires fine-tuning the map parameters for successful map coverage.…”

Section: Comparisonmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.

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Section: Comparisonmentioning

confidence: 99%

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

et al. 2019

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“…The algorithm converges much faster than any of the previous methods. The fifth paper focuses on applications of meta-heuristics in robotics [5]. Meta-heuristics are high-level nature-inspired strategies for collaborative robots to achieve common goals, which are not easily achievable with independent robots.…”

Section: Scanning the Issuementioning

confidence: 99%

Introduction to the Special issue on “Future trends in robotics and autonomous techniques”

Chaudhary

Escolano

Bhojan

2015

Computers & Electrical Engineering

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“…The study [9] stated the drawback of this technique that refers to a lot of memory consumption, which results in a slow search of frontiers in unfavorable environments for forming a tree. To improve the process, an optimization mechanism [14], named a Random Frontier Points Optimization algorithm, was proposed to evaluate all frontier points iteratively according to exploration information, navigation cost, and accuracy of robot position.…”

Section: Related Workmentioning

confidence: 99%

“…The hunting begins with the searching for prey operator ( | ⃗ | > 1 ), in which a whale swarm follows a randomly selected whale ⃗ at iteration . In (14), the vector ⃗ ⃗⃗ computes the distance of the current whale ⃗ to the random whale position. Here, the stochastic parameter ⃗ gives some additional deviations for ⃗ ⃗⃗ that allows finding the ⃗ ( + 1) position around the ⃗ solution (15).…”

Section: ) Mathematical Modelmentioning

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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A review of metaheuristics in robotics

Cited by 49 publications

References 53 publications

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer

Introduction to the Special issue on “Future trends in robotics and autonomous techniques”

Waypoint Mobile Robot Exploration Based on Biologically Inspired Algorithms

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