Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.

Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.

Authentic remote-sensing images suffer non-uniform complex distortions during acquisition, transmission, and storage. Clouds, light, and exposure also affect local quality. This paper constructs a usability-based subjective remote-sensing image dataset and gives a definition of usability for images with non-uniform distortion, where the image usability is determined by the weighted quality of image's blocks. It is difficult to extract the handcraft features from remote-sensing images with complex mixture distortion. Recently, convolutional neural network (CNN) has been introduced into blind quality assessment for images with uniform distortion, which includes feature learning and regression in one processing. In this paper, we first describe and systematically analyze the usability of remote-sensing images in detail. Then, we propose a remote-sensing image usability assessment (RSIUA) method based on a residual network by combining edge and texture maps. The score of remote-sensing image usability was obtained with the weighted averaging of the quality scores of all image blocks, and the weight of each image block was determined by its quality score. We compared the proposed method with three traditional image quality assessment methods, one CNN-based method for images with simulated distortion, and one scale-invariant feature transform-based RSIUA method. The linear correlation coefficient, Spearman's rank ordered correlation coefficient, and root-mean-squared error of experiments demonstrate that our method outperforms all five competitors. The experiments also reveal that the edge and texture maps can improve the performance.

Irregular boundaries in image stitching naturally occur due to freely moving cameras. To deal with this problem, existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit solution. However, previous methods always depend on hand-crafted features (e.g., keypoints and line segments). Thus, failures often happen in overlapping regions without distinctive features. In this paper, we address this problem by proposing RecStitchNet, a reasonable and effective network for image stitching with rectangular boundaries. Considering that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework, we propose a three-step progressive learning based strategy, which not only simplifies this task, but gradually achieves a good balance between stitching and imposing rectangularity. In the first step, we perform initial stitching by a pre-trained state-of-the-art image stitching model, to produce initially warped stitching results without considering the boundary constraint. Then, we use a regression network with a comprehensive objective regarding mesh, perception, and shape to further encourage the stitched meshes to have rectangular boundaries with high content fidelity. Finally, we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively, which can effectively improve the stitching results in terms of feature alignment, as well as boundary and structure preservation. Due to the lack of stitching datasets and the difficulty of label generation, we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels, and the performance upper bound induced from the it can be broken by our unsupervised refinement. Qualitative and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.