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.
The effective enhancement of braking energy recovery and safety in electric vehicles can be achieved by accurately identifying the driver's braking intention and developing a corresponding regenerative braking control strategy based on different braking intentions. This paper presents a categorization of braking conditions into mild, moderate, and emergency levels, followed by the construction of a test system for recognizing braking intentions. Multiple sets of braking conditions are tested under various initial speeds to obtain parameters for recognizing the driver's intended action during braking. Through feature selection using random forest, acceleration, brake pedal displacement, and brake pedal force are identified as key parameters for recognizing the driver's intended action during braking. Subsequently, an AdaBoost-based model is established for recognizing the driver's intended action during braking. Experimental data is used to validate this model offline and compare it with various other models for recognition of driving intentions during brakes. The results show that the braking intention recognition model based on AdaBoost has a high recognition accuracy.
To solve the problems of low segmentation accuracy, mis-segmentation, and exponential increase of computation time with the rise of the number of thresholds when multi-threshold image segmentation is performed on images of multiple laser stripes in the structured lights windshield assembly detection environment, an improved black widow optimization algorithm (IBWOA) for multi-threshold laser stripe image segmentation is proposed. In the population initialization stage, Logistic chaotic mapping is used to enhance the initial population randomness. In the position updating stage, an adaptive step size strategy is introduced to improve the search speed and accuracy in the later stage, and individual variation based on the reverse learning strategy is introduced to avoid falling into local optimal solutions. Compared with existing swarm intelligence optimization algorithms, the results show that IBWOA has better search speed, accuracy, and escape ability to jump out of locally optimal solutions in multi-threshold laser stripe image segmentation.
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