Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search

Khan, Ameer Tamoor; Cao, Xinwei; Li, Zhan; Li, Shuai

doi:10.4108/airo.v1i.6

Cited by 16 publications

(6 citation statements)

References 41 publications

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“…The data here is not line pixels but image features with an advanced representation. The quality of the extraction of the characteristics is very important for the results [21][22][23]. Wu et al, (2015) [24] applied DL to handwritten character recognition and explored two common DL algorithms: the CNN and the deep belief network (DBN).…”

Section: In Image Recognitionmentioning

confidence: 99%

Deep Learning Application Pros And Cons Over Algorithm

Moshayedi

Roy

Kolahdooz

et al. 2022

EAI Endorsed Trans AI Robotics

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Deep learning is a new area of machine learning research. Deep learning technology applies the nonlinear and advanced transformation of model abstraction into a large database. The latest development shows that deep learning in various fields and greatly contributed to artificial intelligence so far. This article reviews the contributions and new applications of deep learning. The main target of this review is to give the summarize points for scholars to have the analysis about applications and algorithms. Then review tries to investigate the main applications and uses algorithms. In addition, the advantages of using the method of deep learning and its hierarchical and nonlinear functioning are introduced and compared to traditional algorithms in common applications. The following three criteria should be taken into consideration when choosing the area of application. (1) expertise or knowledge of the author; (2) the successful application of deep learning technology has changed the field of application, such as voice recognition, chat robots, search technology and vision; and (3) deep learning can have a significant impact on the application domain and benefit from recent research with natural language and text processing, information recovery and multimodal information processing resulting from multitasking deep learning. This review provides a general overview of a new concept and the growing benefits and popularity of deep learning, which can help researchers and students interested in deep learning methods.

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Section: In Image Recognitionmentioning

confidence: 99%

Deep Learning Application Pros And Cons Over Algorithm

Moshayedi

Roy

Kolahdooz

et al. 2022

EAI Endorsed Trans AI Robotics

View full text Add to dashboard Cite

show abstract

“…BAS is a single particle searching algorithm, where the particle optimizes an objective function by searching the search space iteratively. The utility of BAS has expanded to several real-world problems [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ], including the portfolio optimization. Ref.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

et al. 2022

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The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.

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“…The main problem with this method is that the robot can get trapped in local optima. In addition to these obstacle avoidance algorithms some other algorithms can be found in [17][18][19][20][21]. Mobile robot path planning approaches can be classified in two broad categories based on the availability of prior information.…”

Section: Introductionmentioning

confidence: 99%

Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

Ijaz

Khan

2022

EAI Endorsed Trans AI Robotics

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Trajectory tracking and obstacle avoidance lies at the heart of autonomous navigation for mobile robots. In this paper, a control architecture for trajectory tracking while avoiding obstacles and controller tuning is proposed for a differential drive mobile robot (DMR). The framework of optimization algorithm is inspired by the food search behavior of beetles using their antennae. Path planning and controller tuning remain computationally demanding tasks despite of the proposed algorithms existing today. Our bio inspired approach unifies these two problems by minimizing the respective cost functions and solving the optimization problem efficiently. Trajectory tracking problem is based on the difference of the current and next pose of the robot while obstacle avoidance is achieved on the principle of maximizing the minimum distance between the robot and obstacle in the path of the robot. The proposed architecture is simulated in V-REP environment using MATLAB. Simulation results have verified that beetle antennae search can successfully plan and track the reference path by tuning the PID controller efficiently.

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Evolutionary Computation Based Real-time Robot Arm Path-planning Using Beetle Antennae Search

Cited by 16 publications

References 41 publications

Deep Learning Application Pros And Cons Over Algorithm

Deep Learning Application Pros And Cons Over Algorithm

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

Bio-inspired BAS: Run-time Path-planning And The Control of Differential Mobile Robot

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