An Efficient and Robust Improved A* Algorithm for Path Planning

Path planning plays an essential role in mobile robot navigation, and the A* algorithm is one of the best-known path planning algorithms. However, the conventional A* algorithm and the subsequent improved algorithms still have some limitations in terms of robustness and efficiency. These limitations include slow algorithm efficiency, weak robustness, and collisions when robots are traversing. In this paper, we propose an improved A*-based algorithm called EBHSA* algorithm. The EBHSA* algorithm introduces the expansion distance, bidirectional search, heuristic function optimization and smoothing into path planning. The expansion distance extends a certain distance from obstacles to improve path robustness by avoiding collisions. Bidirectional search is a strategy that searches for a path from the start node and from the goal node at the same time. Heuristic function optimization designs a new heuristic function to replace the traditional heuristic function. Smoothing improves path robustness by reducing the number of right-angle turns. Moreover, we carry out simulation tests with the EBHSA* algorithm, and the test results show that the EBHSA* algorithm has excellent performance in terms of robustness and efficiency. In addition, we transplant the EBHSA* algorithm to a robot to verify its effectiveness in the real world.

Rapid Planning of an Assembly Path by Reusing the Prior Path

Assembly path planning of complex products in virtual assembly is a necessary and complicated step, which will become long and inefficient if the assembly path of each part is completely planned in the assembly space. The coincidence or partial coincidence of the assembly paths of some parts provides an opportunity to solve this problem. A path planning algorithm based on prior path reuse (PPR algorithm) is proposed in this paper, which realizes rapid planning of an assembly path by reusing the planned paths. The core of the PPR algorithm is a dual-tree fusion strategy for path reuse, which is implemented by improving the rapidly exploring random tree star (RRT *) algorithm. The dual-tree fusion strategy is used to find the nearest prior node, the prior connection node, the nearest exploring node, and the exploring connection node and to connect the exploring tree to the prior tree after the exploring tree is extended to the prior space. Then, the optimal path selection strategy is used to calculate the costs of all planned paths and select the one with the minimum cost as the optimal path. The PPR algorithm is compared with the RRT * algorithm in path planning for one start node and multiple start nodes. The results show that the total time and the number of sampling points for assembly path planning of batch parts using the PPR algorithm are far less than those using the RRT * algorithm.

A Generic Approach on How to Formally Specify and Model Check Path Finding Algorithms: Dijkstra, A* and LPA*

The paper describes how to formally specify three path finding algorithms in Maude, a rewriting logic-based programming/specification language, and how to model check if they enjoy desired properties with the Maude LTL model checker. The three algorithms are Dijkstra Shortest Path Finding Algorithm (DA), A* Algorithm and LPA* Algorithm. One desired property is that the algorithms always find the shortest path. To this end, we use a path finding algorithm (BFS) based on breadth-first search. BFS finds all paths from a start node to a goal node and the set of all shortest paths is extracted. We check if the path found by each algorithm is included in the set of all shortest paths for the property. A* is an extension of DA in that for each node [Formula: see text] an estimation [Formula: see text] of the distance to the goal node from [Formula: see text] is used and LPA* is an incremental version of A*. It is known that if [Formula: see text] is admissible, A* always finds the shortest path. We have found a possible relaxed sufficient condition. The relaxed condition is that there exists the shortest path such that for each node [Formula: see text] except for the start node on the path [Formula: see text] plus the cost to [Formula: see text] from the start node is less than the cost of any non-shortest path to the goal from the start. We informally justify the relaxed condition. For LPA*, if the relaxed condition holds in each updated version of a graph concerned including the initial graph, the shortest path is constructed. Based on the three case studies for DA, A* and LPA*, we summarize the formal specification and model checking techniques used as a generic approach to formal specification and model checking of path finding algorithms.

Proactive Fault Tolerant Routing Protocol for Mobile WSN

In Wireless Sensor Network when the sensory nodes are mobile, it is called Mobile WSN. Since the sensors are on the move, the topology of a MWSN is perpetually dynamical. So, finding an optimal routing path from the start node (where the event takes place) to the base station in Mobile WSN is highly complicated. Recent research works have resulted in the design of many innovative protocols for MWSN, however there are many unresolved issues like fault tolerance, connectivity, reducing the energy consumption, enhancing coverage, improving security etc. This work aims at proposing a proactive FT routing scheme for Mobile WSN using a dynamic routing table PFTP and then simulating in MATLAB for evaluating the various performance metrics.

Comparative Analysis of Pathfinding Algorithms A *, Dijkstra, and BFS on Maze Runner Game

Maze Runner game is a game that requires pathfinding algorithm to get to the destination with the shortest path. This algorithm is used in an NPC that will move from start node to destination node. However, the use of incorrect algorithms can affect the length of the computing process to find the shortest path. The longer the computing process, the longer the players have to wait. This study compared pathfinding algorithms A *, Dijkstra, and Breadth First Search (BFS) in the Maze Runner game. Comparison process of these algorithms was conducted by replacing the algorithm in the game by measuring the process time, the length of the path, and the numbers of block played in the existing computing process. The results of this study recommend which algorithm is suitable to be applied in Maze Runner Game.

An optical solution for the set splitting problem

We describe here an optical device, based on time-delays, for solving the set splitting problem which is well-known NP-complete problem. The device has a graph-like structure and the light is traversing it from a start node to a destination node. All possible (potential) paths in the graph are generated and at the destination we will check which one satisfies completely the problem's constrains.

Path-Cost Bounds for Parameterized Centralized Variants of A* for Static and Certain Environments

A* search and its variants have been used in various fields for solving problems with large search spaces where state transitions occur because of application of operators. The key values in A* search are g(n) and h(n), where g(n) is the cost of the path from root (or start) node to node n, and h(n) is the estimated cost of cheapest path from n to goal. In this paper, we report on a space of variants of A* based on the following ideas: (i) using weighting functions for g(n) and h(n), (ii) evaluating different nodes with different heuristics, (iii) evaluating nodes with computationally cheap heuristics and re-evaluating some nodes with computationally expensive heuristics, and (iv) changing the size of the set of nodes from which the node to be expanded next is selected. We report on the bounds on the costs of solutions found by these variants of A*. We also report on the bounds for meta-variants of A* that invoke these variants sequentially. We show how the results can be used to obtain a more flexible search control without increasing the bound on the cost of the solution found by a variant or a meta-variant.

High Performance Start Node of Edge Ordering for Infrastructure Networks

During the reliability analysis of infrastructure networks based on Binary Decision Diagram (BDD), we studied the high performance start node for edge ordering. We use node’s betweenness to divide the network into several partitions, and show the relation between high performance start nodes and network partitions. We summarized the distribution patterns of the high performance start nodes. The experiment results on selected aviation network shows that we can select high performance ordering start nodes for large-scale networks by using these patterns and network partitions. Thus, we can enhance the performance of network reliability analysis algorithm.

Node Information Collection Routing Algorithm Using Mobile Agent in WSN Initialization Stage

We propose a mobile agent fixed-direction one step routing algorithm based on modeling WSN node data collection task. This algorithm divides the data collection task into three stages to finish and they are searching start node for MA data collection, data collection and returning the sink node. MA determines the next hop node according to the surrounding node information sensed and current move direction information. Simulation result shows that the proposed algorithm can ensure that all nodes information in WSN is collected. Then, it can effectively reduce the total number of hops for information transferring and the node total energy consumption compared to LGF algorithm in C\S mode