In response to the issues of low search efficiency, long path length, and high energy consumption associated with the traditional ant colony algorithm in Automated Guided Vehicle (AGV) path planning, this paper proposes an improved ant colony path planning algorithm that integrates the sparrow algorithm. Firstly, an improved sparrow algorithm is utilized to plan a relatively optimal path, and based on this path, the initial pheromone distribution of the ant colony is improved. Secondly, a heuristic function is introduced to enhance the search efficiency of the ant colony. Finally, a new pheromone updating method is designed, which incorporates an adaptive pheromone evaporation factor to improve the global search capability of the ant colony. Simulations conducted on the MATLAB platform demonstrate that the proposed improved algorithm significantly enhances search efficiency, reduces path turning points, smoothens paths, and effectively lowers energy consumption.
To address the challenges faced by traditional Ant Colony Optimization (ACO) in manipulator path planning, such as slow convergence rate, vulnerability to local optima, and excessively lengthy planned paths, this study introduces a novel algorithm for path planning that combines Rapidly-Exploring Random Tree (RRT) with Ant Colony Optimization (ACO). Firstly, the kinematic model of the AUBO-i5 robotic arm is established using the Modified Denavit-Hartenberg (M-DH) method. the application of the Rapidly-exploring Random Tree (RRT) algorithm is employed to conduct a preliminary search for paths. This approach aims to enhance the initial distribution of pheromones in ant colony algorithms and improve heuristic functions. Additionally, an adaptive pheromone volatilization coefficient is introduced to address local optimality issues. Finally, simulation results conducted in MATLAB demonstrate that compared with traditional algorithms, the improved approach exhibits significant enhancements in terms of search time reduction, iteration count decrease, and path length improvement.
Semi-global stereo matching (SGM) algorithm is widely adopted in stereo matching due to its optimal trade-off between accuracy and efficiency. However, SGM exhibits limitations in accurately matching weak texture regions and entails high computational complexity. The present paper proposes a novel enhanced SGM algorithm by integrating the CT cost and BT cost, aiming to address this issue. Initially, the anti-interference capability of Census transform is improved by setting a standard deviation threshold. The fused weights of the Census cost and window-based BT cost compensate for insufficient image information. Subsequently, an 8-channel dynamic programming algorithm is utilized to aggregate costs followed by a winner-take-all approach to compute disparity values. Furthermore, a weighted least squares filter optimizes the disparity map. Finally, the proposed algorithm's anti-occlusion performance and matching accuracy are evaluated using the Middlebury dataset. Experimental results demonstrate that our proposed cost calculation method outperforms CT cost and BT cost in terms of anti-interference performance significantly when compared with both SGM algorithm and AD-Census algorithm.
In the motor vehicle inspection scenario, accurate identification and positioning of vehicle nameplates is an important prerequisite for automated robotic inspection operations. In view of the current problems that vehicle nameplates are mainly checked manually and the level of intelligence is low, a target recognition and localisation method based on binocular vision combined with improved SURF algorithm is proposed. Firstly, feature points are extracted based on the SURF algorithm, and target recognition is achieved by combining the nearest neighbour ratio method with the RANSAC algorithm based on the gradient constraint of the parallax; then the edge contour of the target in the template image is obtained based on the Canny edge detection method, and the target shape centre on the template image is obtained based on the contour, and the left and right scene map target shape centres are obtained using the single-response matrix mapping; finally, target localisation is achieved by combining the principle of binocular vision 3D reconstruction. The experimental results show that the method has good recognition efficiency and accuracy, and can successfully identify and locate the target, which has certain application value.
Stereo matching is a critical step in three-dimensional reconstruction. The conventional Census transform is excessively dependent on the central pixel and is susceptible to noise interference. Therefore, this study proposes a cost calculation method for multi-feature fusion based on the HSV color space and an improved Census transform. To address the problem of poor matching in regions with weak textures and depth discontinuities. In this study, a new window construction method is developed based on the length and variable color threshold to obtain the crossover region. After dividing the weights of the constructed windows, the cost aggregation is calculated. Finally, a dense parallax map is obtained via parallax calculation and optimization. Experiments were conducted based on the data provided by the Middlebury dataset. The experimental results showed that the improved cost calculation algorithm was significantly resistant to noise, and the average mis match rate of the algorithm was reduced to 8.03%.
An improved ORB feature point extraction method is proposed for the disadvantages of concentrated extraction, uneven distribution of feature points, slow matching speed and high matching error rate due to the fixed threshold value set by the traditional ORB algorithm in feature point extraction. Firstly, the adaptive threshold is set by automatically calculating the greyscale value around the image block, and the feature points are judged and extracted using this threshold; the traditional ORB algorithm uses the greyscale center of mass method to determine the principal direction, but its accuracy has certain deviations, and the accuracy of calculating the principal direction is improved by Gaussian weighting the pixel greyscale values and then calculating the principal direction by the greyscale center of mass method. The experimental results show that the improved ORB algorithm has a larger number of feature points extracted, a more even distribution and a higher correct matching rate.
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