scholarly journals Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Zengliang Han ◽  
Mou Chen ◽  
Tongle Zhou ◽  
Zhiqiang Nie ◽  
Qingxian Wu
Keyword(s):  
Path Planning ◽  
Search Time ◽  
Optimization Method ◽  
Flight Path ◽  
Planning System ◽  
Selection Strategy ◽  
Planning Problem ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Autonomous Helicopter

Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

scholarly journals An Improved Ant Colony Algorithm for Solving the Path Planning Problem of the Omnidirectional Mobile Vehicle

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jiang Zhao ◽  
Dingding Cheng ◽  
Chongqing Hao
Keyword(s):  
Path Planning ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Selection Strategy ◽  
Planning Problem ◽  
Evaporation Coefficient ◽  
Mobile Vehicle ◽  
Starting Point ◽  
Path Planning Problem ◽  
Improved Ant Colony Algorithm

This paper presents an improved ant colony algorithm for the path planning of the omnidirectional mobile vehicle. The purpose of the improved ant colony algorithm is to design an appropriate route to connect the starting point and ending point of the environment with obstacles. Ant colony algorithm, which is used to solve the path planning problem, is improved according to the characteristics of the omnidirectional mobile vehicle. And in the improved algorithm, the nonuniform distribution of the initial pheromone and the selection strategy with direction play a very positive role in the path search. The coverage and updating strategy of pheromone is introduced to avoid repeated search reducing the effect of the number of ants on the performance of the algorithm. In addition, the pheromone evaporation coefficient is segmented and adjusted, which can effectively balance the convergence speed and search ability. Finally, this paper provides a theoretical basis for the improved ant colony algorithm by strict mathematical derivation, and some numerical simulations are also given to illustrate the effectiveness of the theoretical results.

scholarly journals Investigation on the Inversion of the Atmospheric Duct Using the Artificial Bee Colony Algorithm Based on Opposition-Based Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Chao Yang ◽  
Jian-Ke Zhang ◽  
Li-Xin Guo
Keyword(s):  
Convergence Rate ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Research Field ◽  
Invasive Weed ◽  
Local Optima ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Opposition Based Learning ◽  
Atmospheric Duct

The artificial bee colony (ABC) algorithm is a recently introduced optimization method in the research field of swarm intelligence. This paper presents an improved ABC algorithm named as OGABC based on opposition-based learning (OBL) and global best search equation to overcome the shortcomings of the slow convergence rate and sinking into local optima in the process of inversion of atmospheric duct. Taking the inversion of the surface duct using refractivity from clutter (RFC) technique as an example to validate the performance of the proposed OGABC, the inversion results are compared with those of the modified invasive weed optimization (MIWO) and ABC. The radar sea clutter power calculated by parabolic equation method using the simulated and measured refractivity profile is utilized to carry out the inversion of the surface duct, respectively. The comparative investigation results indicate that the performance of OGABC is superior to that of MIWO and ABC in terms of stability, accuracy, and convergence rate during the process of inversion.

Flight Path Planning Based on PSO and Minimum Threat Surface

2012 ◽  
Vol 461 ◽  
pp. 482-486
Author(s):  
Xun Qiang Hu ◽  
Xiao Fang Xie ◽  
Long Jie Zhang ◽  
Jian Cao
Keyword(s):  
Global Optimization ◽  
Path Planning ◽  
Polynomial Function ◽  
Cost Model ◽  
Flight Path ◽  
Planning Problem ◽  
Optimal Coefficients ◽  
Path Cost ◽  
Simulation Results ◽  
Path Planning Problem

Optimal flight path is restricted within Minimum Threat Surface (MTS). With Taylor’s expansion, the horizon projection of 3D flight path within MTS is fitted with a polynomial function with finite items. So the original path planning problem is simplified to search the optimals values in the coefficient space of the polynomial function. Flight path cost model of flight path is built and PSO is applied to search optimal coefficients. Flow of optimization is introduced and validated with three simulation cases. Simulation results show that MTS and PSO can effectively solve global optimization of 3D flight path.

scholarly journals A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Mohammed Hasan Abdulameer ◽  
Siti Norul Huda Sheikh Abdullah ◽  
Zulaiha Ali Othman
Keyword(s):  
Face Recognition ◽  
Model Fitting ◽  
Optimization Method ◽  
Appearance Model ◽  
Active Appearance Model ◽  
Abc Algorithm ◽  
Model Based ◽  
Bee Colony ◽  
Active Appearance ◽  
Recognition Technique

Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition.

scholarly journals Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

2018 ◽  
Vol 46 (2) ◽  
pp. 85-97 ◽  
Author(s):  
Hongxing Zhao ◽  
Ruichun He ◽  
Jiangsheng Su
Keyword(s):  
Optimization Model ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Signal Timing ◽  
Multi Objective Optimization ◽  
Abc Algorithm ◽  
Multi Objective ◽  
Bee Colony ◽  
Vehicle Delay ◽  
Single Objective

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

scholarly journals A Two-Stage Method for Target Searching in the Path Planning for Mobile Robots

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6919
Author(s):  
Tao Song ◽  
Xiang Huo ◽  
Xinkai Wu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Search Time ◽  
Optimal Solution ◽  
Corner Detection ◽  
Planning Problem ◽  
Complete Coverage ◽  
Two Stage ◽  
Set Up ◽  
Target Searching

The path planning for target searching in mobile robots is critical for many applications, such as warehouse inspection and caring and surveillance for elderly people in the family scene. To ensure visual complete coverage from the camera equipped in robots is one of the most challenging tasks. To tackle this issue, we propose a two-stage optimization model to efficiently obtain an approximate optimal solution. In this model, we first develop a method to determine the key locations for visual complete coverage of a two-dimensional grid map, which is constructed by drawing lessons from the method of corner detection in the image processing. Then, we design a planning problem for searching the shortest path that passes all key locations considering the frequency of target occurrence. The testing results show that the proposed algorithm can achieve the significantly shorter search path length and the shorter target search time than the current Rule-based Algorithm and Genetic Algorithm (GA) in various simulation cases. Furthermore, the results show that the improved optimization algorithm with the priori known frequency of occurrence of the target can further improve the searching with shorter searching time. We also set up a test in a real environment to verify the feasibility of our algorithm.

scholarly journals Artificial Bee Colony Algorithm for Solving Optimal Power Flow Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Luong Le Dinh ◽  
Dieu Vo Ngoc ◽  
Pandian Vasant
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Artificial Bee Colony ◽  
Optimization Method ◽  
Capacity Limits ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Optimal Power ◽  
Line Flow ◽  
Power Flow Problem

This paper proposes an artificial bee colony (ABC) algorithm for solving optimal power flow (OPF) problem. The objective of the OPF problem is to minimize total cost of thermal units while satisfying the unit and system constraints such as generator capacity limits, power balance, line flow limits, bus voltages limits, and transformer tap settings limits. The ABC algorithm is an optimization method inspired from the foraging behavior of honey bees. The proposed algorithm has been tested on the IEEE 30-bus, 57-bus, and 118-bus systems. The numerical results have indicated that the proposed algorithm can find high quality solution for the problem in a fast manner via the result comparisons with other methods in the literature. Therefore, the proposed ABC algorithm can be a favorable method for solving the OPF problem.

scholarly journals Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bai Li
Keyword(s):  
Neural Network ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Gold Price ◽  
Selection Strategy ◽  
Price Forecasting ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Previous Cycle ◽  
Subsequent Cycle

Gold price forecasting has been a hot issue in economics recently. In this work, wavelet neural network (WNN) combined with a novel artificial bee colony (ABC) algorithm is proposed for this gold price forecasting issue. In this improved algorithm, the conventional roulette selection strategy is discarded. Besides, the convergence statuses in a previous cycle of iteration are fully utilized as feedback messages to manipulate the searching intensity in a subsequent cycle. Experimental results confirm that this new algorithm converges faster than the conventional ABC when tested on some classical benchmark functions and is effective to improve modeling capacity of WNN regarding the gold price forecasting scheme.

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