Ant Colony Optimization (ACO) is a classic swarm intelligence optimization algorithm that has been widely applied in various task scheduling scenarios. However, traditional ACO may easily get trapped in local optimal solutions. Inspired by Hybrid Breeding Optimization (HBO) algorithm and coevolution, this paper proposes a Heterogeneous Coevolution Ant Colony Optimization (HCEACO) algorithm based on hybrid breeding mechanisms to overcome the shortcomings of a single population in terms of solution diversity. Moreover, a strategy based on population similarity is proposed to determine whether communication is necessary after a fixed number of iterations, and to maintain a dynamic balance between population diversity and convergence speed in selecting communication partners. To fully validate the effectiveness of the proposed algorithm, multiple path planning algorithms are simulated and applied to multi-load Automatic Guided Vehicle (AGV) path planning. The experimental results show that the improved algorithm performs well in the multi-load AGV path planning problem, and has broad application prospects in this field.
The visual SLAM algorithm based on the assumption of static environment will be affected by the motion feature points in dynamic environment leading to the degradation of system robustness and accuracy. To address this problem, a visual SLAM algorithm based on instance segmentation to remove dynamic feature points is proposed. The Yolact is used to detect potential motion objects, and the semantic information obtained is used to accurately reject dynamic feature points. In addition, considering the under-segmentation problem of the instance segmentation network, this algorithm combines the semantic information in historical key frames and depth maps to segment dynamic objects that are not detected by Yolact using a region growing algorithm, which further improves the robustness of the system. To ensure the real-time performance of the system, the instance segmentation network segments only the key frames, and improves the key frame selection strategy. The experimental results of the system based on TUM dataset show that compared with ORB-SLAM3, the ab-solute trajectory error and relative trajectory error of this algorithm in dynamic environment are reduced by 95.3% and 96.5%, respectively, the real-time performance of this algorithm is better compared with DynaSLAM and DS-SLAM.
In view of the lack of real-time performance in most of the current object-level semantic map construction systems. Combined with high real-time instance segmentation network and visual synchronous location and map construction (VSLAM) algorithm, a object-oriented semantic map real-time construction system is proposed. firstly, the system uses the instance segmentation network to segment each color image, obtains the object and combines the feature and spatial information provided by VSLAM to form the instance description information, and then matches the instance through feature consistency and spatial consistency. At the same time, Bayes filter is used to calculate the existence state of instances and spatial constraints are used to filter the error detection of instances. finally, a real-time instance-level semantic map construction system based on VSLAM is realized, and the system is tested with TUM data sets. The results show that the system can build object-level semantic maps in real time and reduce the error detection rate of instance segmentation.
The visual SLAM algorithm based on the assumption of static environment will be affected by the motion feature points in dynamic environment, resulting in the system robustness and accuracy degradation. To solve this problem, a visual SLAM algorithm for dynamic feature removal based on instance segmentation is proposed. Firstly, Yolact network is used to detect potential moving objects, and the obtained semantic information is combined with epipolar geometric constraints to accurately eliminate dynamic feature points. Considering the failure of example segmentation network, this algorithm combines the semantic information in the historical key frame with the depth map and uses the region growth algorithm to segment the dynamic objects that are not detected by Yolact, which further improves the robustness of the system. The experimental results show that compared with ORB-SLAM3, the absolute trajectory error and relative trajectory error of the algorithm in the dynamic environment are significantly reduced.
With the geometric increase in the number of surveillance cameras and the sharp increase in video data, the traditional monitoring method that only relies on manual real-time viewing of surveillance information needs to be changed. The unattended system is an intelligent monitoring system that does not require a lot of manual intervention. It can automatically detect specific targets in the video and alert the staff through early warning. This paper designs a set of the simplified neural network combined with an unattended platform on the edge computing end. Through the optimization, compression, and conversion of the neural network model, the NPU is used to accelerate the realization of fast and accurate target detection, early warning and data return at the mobile edge.
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