Mobile robots have been widely used in various sectors in the last decade. A mobile robot could autonomously navigate in any environment, both static and dynamic. As a result, researchers in the robotics field have offered a variety of techniques. This paper reviews the mobile robot navigation approaches and obstacle avoidance used so far in various environmental conditions to recognize the improvement of path planning strategists. Taking into consideration commonly used classical approaches such as Dijkstra algorithm (DA), artificial potential field (APF), probabilistic road map (PRM), cell decomposition (CD), and meta-heuristic techniques such as fuzzy logic (FL), neutral network (NN), particle swarm optimization (PSO), genetic algorithm (GA), cuckoo search algorithm (CSO), and artificial bee colony (ABC). Classical approaches have limitations of trapping in local minima, failure to handle uncertainty, and many more. On the other hand, it is observed that heuristic approaches can solve most real-world problems and perform well after some modification and hybridization with classical techniques. As a result, many methods have been established worldwide for the path planning strategy for mobile robots. The most often utilized approaches, on the other hand, are offered below for further study.
The emerging trend of modern industry automation requires intelligence to be embedded into mobile robot for ensuring optimal or near-optimal solutions to execute certain task. This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. The purpose of this paper is to review the mobile robots path planning problem, optimization criteria and various methodologies reported in the literature for global and local mobile robot path planning. In this paper, commonly use classical approaches such as cell decomposition (CD), roadmap approach (RA), artificial potential field (AFP), and heuristics approaches such as genetic algorithm (GA), particle swarm optimization (PSO) approach and ant colony optimization (ACO) method are considered. It is observed that when it comes to dynamic environment where most of the information are unknown to the mobile robots before starting, heuristics approaches are more popular and widely used compared to classical approaches since it can handle uncertainty, interact with objects and making quick decision. Finally, few suggestions for future research work in this field are addressed at the end of this paper.
Problem statement:Organizations critically needed to supply recent data to users who may be geographically remote, while at the same time handle a volume request of distributed data around multiple sites. The storage, availability and consistency are important issues to be addressed in order to allow distributed users efficiently and safely access data from many different sites. Approach: Data replication is a way to deal with this problem since it provides user with fast, local access to shared data and protects availability of applications because alternate data access options exist. Handling fragmented database replication becomes challenging issue to administrator since the distributed database was scattered into split replica partitions or fragments. Results: This study presented a new mechanism on how to handle the fragmented database replication through the Binary Vote Assignment on Grid Quorum (BVAGQ). We address how to build reliable system by using the proposed BVAGQ for distributed database fragmentation. Conclusion: The result shows that managing fragmented database replication and transaction through proposed BVAGQ is able to preserve the data consistency.
A supercomputer can have thousands of processor-memory pairs which often referred as processing pins. Each of these pins is connected to each other through networks and passes message using a standard message passing mechanism such as Message Passing Interface. In this research, we consider the routing problem in rectangular mesh network. Each terminal pin in the network needs to be connected with its destination pin for it to function properly. Thus, maximizing the number of connection for each pair of pins and keeping the total energy throughout the network minimum becomes our main objective. In order to achieve this objective, each net need to be routed as shortest as possible. Therefore, developing a shortest path based routing algorithm is in need. In this research, Dijkstra’s algorithm is used to establish the shortest connection for each net. While this method guarantees to provide the shortest connection for each single net (if exists), however each routed net will become the obstacles and block later connections. This will add complexities to route later nets and make its routing longer than optimal or sometimes impossible to complete. Therefore, the routing sequence need to be rip-up and all nets need to be re-routed. This paper presents a complete routing algorithm which can further refine the solution by using Dijkstra’s based greedy method. The outcomes from this research is expected to benefit engineers from electric & electronic industry.
Mobile robots are growing more significant from time to time and have been applied to many fields such as agriculture, space, and even human life. It could improve mobile robot navigation efficiency, ensure path planning safety and smoothness, minimize time execution, etc. The main focus of mobile robots is to have the most optimal functions. An intelligent mobile robot is required to travel autonomously in various environments, static and dynamic. This paper article presents the optimization criteria for mobile robot path planning to figure out the most optimal mobile robot criteria to fulfill, including modeling analysis, path planning and implementation. Path length and path smoothness are the most parameters used in optimization in mobile robot path planning. Based on path planning, the mobile robot navigation is divided into three categories: global navigation, local navigation and personal navigation. Then, we review each category and finally summarize the categories in a map and discuss the future research strategies.
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