Path planning is a fundamental problem, especially for various AEC applications, such as architectural design, indoor and outdoor navigation, and emergency evacuation. However, the conventional approaches mainly operate path planning on 2D drawings or building layouts by simply considering geometric information, while losing abundant semantic information of building components. To address this ...

Search using subgoal graphs is a recent preprocessing-based path-planning algorithm that can find shortest paths on 8neighbor grids several orders of magnitude faster than A*, while requiring little preprocessing time and memory overhead. In this paper, we first generalize the ideas behind subgoal graphs to a framework that can be specialized to different types of environments (represented as w...

Path planning is one of the most important issues in mobile robots since it determines the intelligence of robots. It is about finding the shortest, collision free and smooth path form predefined start point to fixed target point with the presence of obstacles either static or dynamic. In this paper, we present a new approach of artificial intelligence for mobile robot. The proposed approach fo...

In this lecture, we will further examine shortest path algorithms. We will first revisit Dijkstra’s algorithm and prove its correctness. Next, we will look at another shortest path algorithm known as the Bellman-Ford algorithm, that has a slower running time than Dijkstra’s but allows us to compute shortest paths on graphs with negative edge weights. Lastly, we look at an O(n3)-time algorithm f...

We propose a dynamic program to find the shortest path in a network having gamma probability distributions as arc lengths. Two operators of sum and comparison need to be adapted for the proposed dynamic program. Convolution approach is used to sum two gamma probability distributions being employed in the dynamic program.

Recently the area of motion planning research has been experiencing a significant resurgence of interest based on hybrid working environments that combine point and CAD models. Companies are able to work with point clouds and perform certain operations, such as path-planning, but they lack the support for fast shortest-distance computations for point clouds with more than tens of millions of po...

a shortest path problem is a practical issue in networks for real-world situations. this paper addresses the fuzzy shortest path (fsp) problem to obtain the best fuzzy path among fuzzy paths sets. for this purpose, a new efficient algorithm is introduced based on a new definition of ideal fuzzy sets (ifss) in order to determine the fuzzy shortest path. moreover, this algorithm is developed for ...

Minimum cost flow problems in infinite networks arise, for example, in infinite-horizon sequential decision problems such as production planning. Strong duality for these problems was recently established for the special case of linear costs using an infinite-dimensional simplex algorithm. Here, we use a different approach to derive duality results when the costs are convex. We formulate the pr...

Computing rectilinear shortest paths in two dimensions has been solved optimally using a number of different techniques. A variety of related problems have been solved, including minimizing the number of bends in the path, the total rectilinear distance, or some combination of both. However, solutions to the 3D versions of these problems are less common. We propose a solution to the 3D minimum-...

We present a sampling-based, asymptotically optimal feedback planning method for the shortest path problem among obstacles in R. Our method combines an incremental sampling-based Delaunay triangulation with the newly introduced Repairing Fast Marching Method for computing a converging sequence of control policies. The convergence rate and asymptotic computational complexity of the algorithm are...