Topological Frontier-Based Exploration and Map-Building Using Semantic Information

Gómez, Clara; Hernandez, Alejandra C.; Barber, Ramón

doi:10.3390/s19204595

Cited by 36 publications

(33 citation statements)

References 35 publications

(40 reference statements)

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“…Francisco et al [30] proposed a frontier exploration algorithm for simultaneous localization and mapping, with an objective to eliminate frontier points from the map. Clara Gomez et al [31] proposed integration of frontier based approach with a behavioral strategy to build a topological map. Similarly, Gomez et al [32] developed a frontier-based exploration technique compatible with the ROS system for analyzing path length and execution period.…”

Section: ) Deterministic Cmementioning

confidence: 99%

Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

et al. 2021

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Multi-robot space exploration involves building a finite map utilizing a cluster of robots in an obstacle cluttered environment. The uncertainties are minimized by assigning tasks among robots and computing the optimum action. Such optimal trajectories are traditionally obtained utilizing deterministic or metaheuristic techniques, with each having peculiar limitations. Recently, limited work with the sub-optimal result has been done utilizing frameworks that utilize a blend of both techniques. This paper proposes a novel framework which involves the integration of deterministic Coordinated Multi-Robot Exploration (CME) and metaheuristic frequency modified Whale Optimization Algorithm (WOA) techniques, to perform search exploration that imitates the predatory behavior of whales. The frequency is dynamically adjusted utilizing a statistical objective function to tune exploitation and exploration operators. The proposed framework involves a) determination of the cost and utility functional values around individual group members utilizing deterministic CME technique, b) search space exploration to optimize and improve the overall solution utilizing frequency modified whale metaheuristic approach. The effectiveness of the proposed Frequency Modified Hybrid Whale Optimization Algorithm (FMH-WOA) is ascertained by training the multi-robotic framework in different complexity environmental conditions. The results efficacy is then demonstrated by comparing the results of the proposed methodology with those achieved from three other contemporary optimization techniques namely CME-WOA, CME-GWO, and CME-SineCosine.

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Section: ) Deterministic Cmementioning

confidence: 99%

Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

et al. 2021

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“…Local search approaches can also be optimized by performing the segmentation of detected frontiers. As reported in [8], an additional pre-processing procedure is added to semantically classified a frontier as either a free area or a transit area based on the geometric map information. The algorithm considers a transit area, an edge point between two semantically different area, as having a higher priority to be chosen compared to common free areas.…”

Section: Literature Reviewmentioning

confidence: 99%

An enhanced frontier strategy with global search target-assignment approach for autonomous robotic area exploration

Ibrahim¹,

Huddin²,

Zaman³

et al. 2021

IJATEE

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Frontier strategy is an effective robotic area exploration mechanism that exploits the boundaries information between known area and unknown area to determine the next best target location for robots to explore autonomously. A typical frontier strategy employs a greedy-based local search approach to select a target location, also known as goal-assignment task, thus may slow down the exploration process. This paper presents a modified frontier strategy with a global search target-assignment paradigm. The proposed method optimises the target-assignment task by using genetic algorithm to provide a global search mechanism by carefully examining path distances between frontiers. A set of possible routes to visit all frontiers is generated heuristically by the genetic algorithm. After several generations, the first frontier of the shortest route is chosen as the next target location. The proposed enhanced frontier strategy outperforms the canonical frontier strategy in terms of the performance of area exploration by 31% to 50%.

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“…C. Gomez et al. introduced a topological frontier-based exploration using semantic information [ 25 ]. They combined frontier-based concepts with behavior-based strategies in order to build a topological representation of the environment.…”

Section: Related Workmentioning

confidence: 99%

Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

Lü

Redondo

Campoy

2020

Sensors

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Aerial robots are widely used in search and rescue applications because of their small size and high maneuvering. However, designing an autonomous exploration algorithm is still a challenging and open task, because of the limited payload and computing resources on board UAVs. This paper presents an autonomous exploration algorithm for the aerial robots that shows several improvements for being used in the search and rescue tasks. First of all, an RGB-D sensor is used to receive information from the environment and the OctoMap divides the environment into obstacles, free and unknown spaces. Then, a clustering algorithm is used to filter the frontiers extracted from the OctoMap, and an information gain based cost function is applied to choose the optimal frontier. At last, the feasible path is given by A* path planner and a safe corridor generation algorithm. The proposed algorithm has been tested and compared with baseline algorithms in three different environments with the map resolutions of 0.2 m, and 0.3 m. The experimental results show that the proposed algorithm has a shorter exploration path and can save more exploration time when compared with the state of the art. The algorithm has also been validated in the real flight experiments.

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Topological Frontier-Based Exploration and Map-Building Using Semantic Information

Cited by 36 publications

References 35 publications

Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

Novel Implementation of Multi-Robot Space Exploration Utilizing Coordinated Multi-Robot Exploration and Frequency Modified Whale Optimization Algorithm

An enhanced frontier strategy with global search target-assignment approach for autonomous robotic area exploration

Optimal Frontier-Based Autonomous Exploration in Unconstructed Environment Using RGB-D Sensor

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