Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

Taghavifar, Hamid; Mardani, Aref

doi:10.1016/j.inpa.2014.06.001

Cited by 8 publications

(7 citation statements)

References 16 publications

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“…The contour plot for the integrated obstacle collision risk function and that of the start to target positions is also presented in Figure 6, where the robot can realize the approaching obstacles (with a total number of 7), and accordingly find the optimal path based on the proposed algorithm. It is noteworthy that the optimal values related to the robot heading angle, yaw rate, and position described in (26)- (27), are obtained based on the average of infinite discounted rewards assigned an agent to reach the optimality subject to (28), (30) and (31) by employing the chaotic-metaheuristics based Q-Learning (41)-(44). The constrained control inputs for the terrain robot are plotted in Figure 7, subject to the deformable terrain induced skid/slip magnitudes formulated in (6) and based on the single-time velocity estimation described in Section 3.3.…”

Section: Resultsmentioning

confidence: 99%

“…Kennedy and Eberhart [29] first proposed the particle swarm optimization (PSO) algorithm, which has exhibited an extensive applicability within various optimization problems [30]- [32]. However, the standard method in [29] comprises various setbacks related to the premature convergence for multimodal problems [33].…”

Section: B Metaheuristics-based Target Explanationmentioning

confidence: 99%

“…This approach yields greater convergence performance because updating of the position is implemented independent of the velocity components, as seen in (36). This optimization problem based on the chaotic transition of the particles is employed for the problem (28) and (29) subject to constraints (30) and (31).…”

Section: B Metaheuristics-based Target Explanationmentioning

confidence: 99%

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Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

Taghavifar

Taghavifar³

et al. 2019

IEEE Access

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A single-time velocity estimator-based reinforcement learning (RL) algorithm, integrated with a chaotic metaheuristic optimization technique is proposed in this article for the optimal path-planning of the nonholonomic robots considering a moving/stationary obstacle avoidance strategy. The additional contribution of the present study is by employing the Terramechanics principles to incorporate the effects of wheel sinkage into the deformable terrain on the dynamics of the robot aiming to find the optimal compensating force/torque magnitude to sustain a robust and smooth motion. The designed systematic control-oriented system incorporates a cost function of weighted components associated with the targettracking and the obstacle avoidance. The designed velocity estimator contributes to the finite-state Markov decision process (MDP) in order to train the transition probabilities of the problem objectives. Based on the obtained results, the optimal solution for the Q-learning in terms of the adjusting factor for the minimized tracking error and obstacle collision risk propagation profiles is found at 0.22. The results further confirm the promising capacity of the proposed optimization-based RL algorithm for the collision avoidance control of the nonholonomic robots on deformable terrains. INDEX TERMS Mechatronics, terramechanics, path-planning, artificial intelligence.

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Section: Resultsmentioning

confidence: 99%

Section: B Metaheuristics-based Target Explanationmentioning

confidence: 99%

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Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

Taghavifar

Taghavifar³

et al. 2019

IEEE Access

Self Cite

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“…However, PSO has not been used in the existing literature on machine-soil interactions. A relatively unknown population based stochastic algorithm has been used in [48,49].…”

Section: Optimization Metaheuristicsmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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“…Also ICA's accuracy is more than PSO. 17 Therefore, in this paper the ICA method is used as an adequate optimization technique.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

Baseri

Belali-Owsia

2016

Proceedings of the Institution of Mechanical Engineers, Part E:

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In order to reduce the manufacturing process variability and improve the production yield, it is important to predict the performance of manufacturing process. The adaptive neuro-fuzzy inference system (ANFIS) is a powerful network which can predict the output parameters of manufacturing process. However, design of ANFIS needs trial and errors to select the best structure. In this study, an imperialistic competitive algorithm has been used to determine the ANFIS architecture to reach minimum values of the prediction error. In order to evaluate the performance of this combined method, two illustrative examples of manufacturing processes have been used. Results indicated that the combined method has superiority in prediction of output, rather than previous developed ANFIS models and so it can be applied for modeling of the other manufacturing processes.

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Energy loss optimization of run-off-road wheels applying imperialist competitive algorithm

Cited by 8 publications

References 16 publications

Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

Optimal Path-Planning of Nonholonomic Terrain Robots for Dynamic Obstacle Avoidance Using Single-Time Velocity Estimator and Reinforcement Learning Approach

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

A novel hybrid ICA-ANFIS model for prediction of manufacturing processes performance

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