A controllability perspective of dynamic soaring

Mir, Imran; Taha, Haitham E.; Eisa, Sameh A.; Maqsood, Adnan

doi:10.1007/s11071-018-4493-6

Cited by 47 publications

(43 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Advancement in research and commercial technology made it conceivable for UAVs, robots, and their related application to appear in our daily life activities at an unprecedented rate [2,3]. For UAVs, Dull Dirty and Dangerous (DDD) missions [4][5][6][7][8][9][10][11] are the most prominent applications, whereas for robotics, iRobot applications [12] and Self-Driven cars [13] have gained much importance in research area as well as in commercial technology. Such vehicles require autonomous path planning algorithms to be energy/time efficient for their implementation.…”

Section: Introductionmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

Self Cite

View full text Add to dashboard Cite

In this paper, a review on the three most important communication techniques (ground, aerial, and underwater vehicles) has been presented that throws light on trajectory planning, its optimization, and various issues in a summarized way. This kind of extensive research is not often seen in the literature, so an effort has been made for readers interested in path planning to fill the gap. Moreover, optimization techniques suitable for implementing ground, aerial, and underwater vehicles are also a part of this review. This paper covers the numerical, bio-inspired techniques and their hybridization with each other for each of the dimensions mentioned. The paper provides a consolidated platform, where plenty of available research on-ground autonomous vehicle and their trajectory optimization with the extension for aerial and underwater vehicles are documented.

show abstract

Section: Introductionmentioning

confidence: 99%

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mobile-robot space exploration utilizing cluster of robots have a wide spectrum utilization ranging from transportation [1], healthcare [2], industry [3], rescue [4][5][6], to all sort of Dull Dirty and Dangerous (DDD) missions [7][8][9]. Similar to Unmanned Aerial Vehicles (UAV) [10][11][12][13][14], their applications on ground is increasing at unprecedented rate. An important aspect of mobile robotics is the exploration in an environment where multiple robots build up the finite map, without any prior knowledge of map layout or position of obstacles.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The research in the area of mobile robotics covers a wide variety of subjects [1][2][3][4][5][6][7], with their broaden genre being Unmanned Aerial Vehicles (UAV) [8][9][10][11][12][13][14][15][16][17][18]. Path planning, routing, localization, networking, and sensing are some of the well-known technologies that deal with preconditioned maps of the ecosystems [19].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, machine learning has recognized bioinspired optimization algorithms to address the optimal solution to complex scientific and technical problems involving time and space complexities. These problems are inherently nonlinear in nature, and usually subjected to various path/terminal constraints [12]. The recent trend is to use bio-inspired optimizing algorithms, which offer a promising approach to solve problems which could not be handled efficiently through conventional optimization algorithms [27].…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Space Exploration: An Augmented Arithmetic Approach

et al. 2021

Self Cite

View full text Add to dashboard Cite

Space exploration refers to constructing a map with the aid of sensor data. This exploration is achieved utilizing a group of robots in an obstacle cluttered environment and distributing tasks amongst these robot(s). The robotic configuration is equipped with sensors to acquire data from the surroundings and to ensure collision-free motion. This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration. The proposed algorithm augments deterministic Coordinated Multi-Robot Exploration (CME) and stochastic Arithmetic Optimization (AO) techniques for maximizing the utility. The framework initially utilizes deterministic CME to ascertain the cost and utility values of adjacent cells around robot(s). The overall solution accuracy is then improved utilizing the Arithmetic Optimization algorithm. The proposed utilization of hybrid is interpreted that the algorithm starts with deterministic technique and continues off with stochastic method until the required improved solution with the desired accuracy is achieved. The effectiveness of the proposed Hybrid Stochastic Optimizer is ascertained by training the multi-robotic framework in various complexity maps. The results efficacy is then demonstrated by comparing the results of the HSO algorithm with those achieved from two contemporary techniques namely conventional CME and hybrid CME with whale optimizer. Results demonstrate that the proposed HSO algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.

show abstract

A controllability perspective of dynamic soaring

Cited by 47 publications

References 34 publications

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

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

Multi-Robot Space Exploration: An Augmented Arithmetic Approach

Contact Info

Product

Resources

About