Warehouse automation has become an essential aspect of modern logistics management. A critical component of warehouse automation is the path finding algorithm, which is responsible for calculating the route for automated guided vehicles to navigate the warehouse. However, conventional single-agent path planning suffers from deadlocks, resulting in a significant reduction in throughput. On the other hand, optimal and complete multi-agent path finding algorithms are slow and computationally expensive, making them unsuitable for warehouses with multiple uncertainties that require constant replanning. In this paper, we propose a novel scheduling technique to avoid deadlocks by introducing nonstop areas based on the dynamics of vehicle intersections. We define critical areas in the warehouse as nonstop areas and prohibit robots from stopping and waiting within these areas. The robot’s reservation length is dynamically extended to ensure that it reserves the entire path through the nonstop area, preventing it from blocking other robots moving through the intersection in other directions. The optimal positions of the nonstop areas were determined in two steps: the placement step to find where the deadlock occurred, and the trimming step to remove the excess nonstop area tiles. Simulation results showed 100% deadlock prevention and significantly improved throughput with the inclusion of the proposed nonstop area. Moreover, the addition of the trimming step further increases the throughput up to 12.27%.

Recently scaling damages of concrete structures have become apparent and it is now investigated to develop a method for quantitatively assessing the damages. In this paper, we propose a beneficial method for this purpose based on long-distance 3D laser scanning. In our proposed method, original shapes of the concrete structures before to be damaged are estimated on their laser scanned data by region growing, the distances to the estimated shapes are computed at each scanned point, and then their surface concavity and convexity are quantitatively evaluated based on the computed distance. We demonstrate the effectiveness of our proposed method from various experiments on real scanned data of concrete structures with scaling damages.

Hash Distributed A* (HDA*) is a parallel A* algorithm that is proven to be effective in optimal sequential planning with unit edge costs. HDA* leverages the Zobrist function to almost uniformly distribute and schedule work among processors. This paper evaluates the performance of HDA * in optimal sequence alignment. We observe that with a large number of CPU cores HDA* suffers from an increase of search overhead caused by reexpansions of states in the closed list due to nonuniform edge costs in this domain. We therefore present a new work distribution strategy limiting processors to distribute work, thus increasing the possibility of detecting such duplicate search effort. We evaluate the performance of this approach on a cluster of multi-core machines and show that the approach scales well up to 384 CPU cores.