The multi-agent cooperative search has garnered increasing attention in recent decades. Within the decentralized framework of multi-UAV cooperation, precise task allocation and assignment are paramount to balancing the workload of each UAV. To achieve reasonable task allocation, we employ spatial segmentation within the search domain, dividing it into distinct partitions. The aim is to allocate equivalent search workloads to individual UAVs across these partitions. To enhance algorithm efficiency, we utilize the Voronoi Diagram as the spatial segmentation generator and integrate deep reinforcement learning to refine the topological structure of these partitions. Finally, to assess the robustness of our proposed algorithm, we conducted experiments under various search scenarios. The results demonstrate significant improvements in the overall search efficiency of the swarm.
The issue of blind spots in the field of vision during automobile driving has always been a significant concern. In response to the limitations of the stereo-matching algorithm based on census transforms, such as inadequate accuracy, insensitivity to textures, and excessive reliance on central grayscale values, an improved local stereo-matching algorithm based on census transform is proposed. The algorithm utilizes the maximum cross-domain variance method to calculate the matching window size, reducing mismatches in discontinuous disparity regions. The minimum cross-domain mean method is employed to determine the grayscale value of the central pixel, minimizing external influences on the central pixel. Additionally, a guided filter with edge-preserving characteristics is used for cost aggregation, further reducing mismatches in non-continuous domains. The experimental results illustrate that the introduced algorithm attains an average mismatch rate of 5.96% on the Middlebury benchmark dataset, marking a substantial enhancement when contrasted with the original census algorithm, which exhibited a mismatch rate of 16.2%.
This paper presents a low-resource binocular stereo matching algorithm. This algorithm begins with an optimized design of semi-global stereo matching (SGM) by employing a sparser version of the census transform and introducing filtering operations in the sparse transformation factors. This optimization enhances the reliability of the sparse template and incorporates color information for fusion. Furthermore, it effectively preserves aggregation direction while curtailing other directions to alleviate the hardware cache resource pressure. The complete hardware system is implemented on an Artix7 AC7A035 platform. The overall matching accuracy is approximately 2.7% higher than that of the classic SGM algorithm. Under the condition that the input image is 1024*768 resolution, it consumes 42,456 lookup tables (LUTs), 40,175 flipflops (FF), and 4.5MB block RAM (BRAM). Resource utilization for both lookup tables and block RAM is reduced. Experimental validation confirms the effectiveness of the proposed algorithm in this paper.
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