China
Africa
Explore chapters and articles related to this topic
Artificial Intelligence
Published in Naveen Chilamkurti, T. Poongodi, Balamurugan Balusamy, Blockchain, Internet of Things, and Artificial Intelligence, 2021
Ishita Singh, Joy Gupta, K. P. Arjun
An issue can be analyzed by the following elements:The primary node where agents start the work.An illustration of all possible actions available to the agents.An account of each and every operation, which is known as the transition models.The goal node, which differs from the goal node from other nodes.A path cost operation that allocates a numeric cost to each path.
Architecture
Published in Hanky Sjafrie, Introduction to Self-Driving Vehicle Technology, 2019
Two of the popular path planning algorithms used for SDVs are the Probabilistic Roadmap (PRM) [13] and the Rapidly-exploring Random Tree (RRT) [15]. Both algorithms are examples of the so-called sampling-based planning approach. The sampling-based approach uses random node probing for the path exploration and a fast collision avoidance function to validate the new candidate nodes. As opposed to the combinatorial planning approach, the sampling-based approach does not require the full construction of all possible collision-free paths between the start and the goal node. Thus, the sampling-based approach might give a suboptimal solution, but it is more practical, especially when the search space is highly dimensional. The PRM algorithm, as illustrated in Figure 4.4, starts with knowledge of the start and end points, and the constraints. The algorithm randomly distributes a defined number of nodes in the space and connects each node to its nearest neighbor. All nodes and edges that connect the nodes must be collision-free according to a collision avoidance function. After all the nodes are connected, the resulting path between the start and the goal nodes can be easily determined using a shortest path algorithm such as Dijkstra. As shown in Figure 4.5, the RRT algorithm works by choosing a random node and trying to connect it to the current tree. The collision avoidance function validates whether the new node is collision-free. It continues until the goal node is reached. The resulting path is the set of edges that connect the start and the goal nodes in the tree.
Flight Planning
Published in Yasmina Bestaoui Sebbane, Multi-UAV Planning and Task Allocation, 2020
The A* algorithm is based on Dijkstra’s algorithm. It focuses the search in the graph toward the goal. A heuristic value is added to the objective function. It must be a lower-bound estimate of the distance from the current vertex to the goal vertex. A* heuristic algorithm computes the shortest path from one given start node to one given goal node. Distance graph with relative distance information between all nodes plus lower bounds of distance to goal from each node are required. In every step, it expands only the currently shortest path by adding adjacent node with shortest distance including estimate of remaining distance to goal. The algorithm stops when the goal vertex has been treated in the priority queue.
An Integrated Co-Design of Flow-Based Biochips Considering Flow-Control Design Issues and Objectives
Published in IETE Journal of Research, 2023
Piyali Datta, Arpan Chakraborty, Rajat Kumar Pal
Let us denote tri as the RST for clique i. In each clique, the end points are the valves that are assumed to be the target nodes, whereas only a single valve is designated as the start node. Now for each clique a multi-target A* [31] can be applied. As the working area is assumed to be a grid, for each grid-node x, A*-search follows a cost function f(x) = g(x) + h(x). Here, g(x) denotes the cost so far to reach x from the start node. The heuristic h(x) provides an estimated cost by observing Manhattan distance between the current x and the goal node. When A* is completed, it results in an RST for the clique in consideration. In addition, the A* must consider obstacle avoidance procedure. The already consumed nodes in a route for the previous application are assumed as obstacles for the next. Thus A* provides a set of disjoint RSTs for all the cliques. The important fact is that since multi-target A* is to be applied sequentially on each clique, different orders of the cliques generally result into different RSTs, which helps in search space exploration and provides an opportunity to obtain better channels. After obtaining a set of disjoint RSTs, we proceed to the valve-pin connection step. Here a single target A* with obstacle avoidance is applied by considering a point on the RST and a pin taking the similar cost function as above. The completion of this procedure results in suitable control channels.