Low Complexicity Graph Based Navigation and Path Finding of Mobile Robot Using BFS
26 Feb 2015-pp 189-195
TL;DR: An RFID (Radio Frequency Identification) based localization technique using a set of RFID(IC) tags arranged in a grid structure in an equidistant manner for tracing the current co-ordinate/location of the robot.
Abstract: Path-finding is a fundamental problem, in mobile robotics which involves finding an optimal collision-free path from the source node to the destination node. Before the robot traces out the required path, the first step to be carried out is, exploring the environment. Localization plays a major role in this case. This paper adopts an RFID (Radio Frequency Identification) based localization technique. A set of RFID(IC) tags arranged in a grid structure in an equidistant manner are used for the purpose of tracing the current co-ordinate/location of the robot. After exploring the environment one virtual map is generated which contains the location of source, destination, obstacles and landmarks. From the map one graph is generated which is composed of a set of vertices that indicates the cells of the grid and a set of edges which indicates the free path in the environment reachable from the source. After analyzing different searching algorithms we found Breadth First Search (BFS) algorithm to be more effective, because it finds the shortest path from the source node to each node in the graph. We improved the BFS algorithm so that the optimal collision free path from source to destination node is generated. We have implemented our techniques in a simulated environment. We have used MATLAB and JAVA for the simulation purpose. Finally we have demonstrated the result of the implementation through examples.
Citations
More filters
TL;DR: In this article, a collision-free low-complexity mobile robot navigation scheme called Collision Aware Mobile Robot navigation in Grid-Environment is designed, which uses the Radio Frequency based Identification method for Mobile Robot localization, the hybrid approach for the path planning, and a predefined decision table for the navigation.
49 citations
TL;DR: This paper presents a comprehensive review on localization system, problems, principle and approaches for mobile robots, and classify the localization problems in to three categories based on the information of initial position of the robot.
43 citations
01 Nov 2019
TL;DR: To elevate capabilities of global optimal and real-time obstacle avoidance of mobile robots, a novel method based on fusion of A-star algorithm and dynamic window approach is proposed.
Abstract: To elevate capabilities of global optimal and real-time obstacle avoidance of mobile robots, a novel method based on fusion of $A^{\star}$ algorithm and dynamic window approach is proposed. Firstly, the principle and work process of $A^{\star}$ algorithm is introduced and simulation experiments in MATLAB are used to compare A$^{\star}$ algorithm and Dijkstra algorithm, then the dynamic window approach based on the evaluation function is applied to implement dynamic path planning to guarantee the ability of obstacle avoidance as holding the global optimality of path. Finally, the experimental results in ROS system verified the effectiveness of global path planning algorithm and the fusion algorithm in a simulation environment.
20 citations
Cites methods from "Low Complexicity Graph Based Naviga..."
...Intelligent optimization algorithms, such as genetic algorithm and particle swarm optimization algorithm, also can be utilized to calculate the optimal path[7-11]....
[...]
01 Jan 2022
TL;DR: In this paper , an efficient low-complexity path-finding algorithm for autonomous mobile robot in a grid-based environment is presented by using a hybrid approach which combines the greedy strategy with proposed improving BFS (I-BFS) algorithm.
Abstract: This paper brings an efficient low complexity path-finding algorithm for autonomous mobile robot in a grid-based environment. Research is carried out by using a hybrid approach which combines the greedy strategy with proposed improving BFS (I-BFS) algorithm. A robot adopts localization strategy based on the environment in which it is employed. The radio frequency identification (RFID)-based localization is quite challenging and is adopted by various authors for localizing robots in indoor environment. The proposed navigation algorithm utilizes RFID technique for positioning the mobile robot which is a substitute for existing vision-based approaches. The added features like both obstacle detection and avoidance are the most promising factor of the proposed methodology. The methodology generated an optimal shortest path in an unknown grid-structured environment through repeated exploration. In this paper, both computationally effective and cost-efficient solution is made for an alternative to several mobile robot navigation algorithms. The navigation algorithm presented here can be used for autonomous vehicular robot employed in different environments like buildings, hospitals, shopping malls, etc., for effective path planning. The simulation result establishes the efficiency of the proposed navigation algorithm.
1 citations
TL;DR: MaPHeA is proposed, a lightweight Memory hierarchy-aware Profile-guided Heap Allocation framework applicable to both HPC and embedded systems that guides and applies the optimized allocation of dynamically allocated heap objects with very low profiling overhead and without additional user intervention to improve application performance.
Abstract: Hardware performance monitoring units (PMUs) are a standard feature in modern microprocessors, providing a rich set of microarchitectural event samplers. Recently, numerous profile-guided optimization (PGO) frameworks have exploited them to feature much lower profiling overhead compared to conventional instrumentation-based frameworks. However, existing PGO frameworks mainly focus on optimizing the layout of binaries; they overlook rich information provided by the PMU about data access behaviors over the memory hierarchy. Thus, we propose MaPHeA, a lightweight Memory hierarchy-aware Profile-guided Heap Allocation framework applicable to both HPC and embedded systems. MaPHeA guides and applies the optimized allocation of dynamically allocated heap objects with very low profiling overhead and without additional user intervention to improve application performance. To demonstrate the effectiveness of MaPHeA, we apply it to optimizing heap object allocation in an emerging DRAM-NVM heterogeneous memory system (HMS), selective huge-page utilization, and controlling the cacheability of the objects with the low temporal locality. In an HMS, by identifying and placing frequently accessed heap objects to the fast DRAM region, MaPHeA improves the performance of memory-intensive graph-processing and Redis workloads by 56.0% on average over the default configuration that uses DRAM as a hardware-managed cache of slow NVM. By identifying large heap objects that cause frequent TLB misses and allocating them to huge pages, MaPHeA increases the performance of the read and update operations of Redis by 10.6% over the transparent huge-page implementation of Linux. Also, by distinguishing the objects that cause cache pollution due to their low temporal locality and applying write-combining to them, MaPHeA improves the performance of STREAM and RADIX workloads by 20.0% on average over the system without cacheability control.
References
More filters
Book•
31 Jul 2009TL;DR: Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book a great resource on the basic tools used to analyze the performance of algorithms.
Abstract: If you had to buy just one text on algorithms, Introduction to Algorithms is a magnificent choice. The book begins by considering the mathematical foundations of the analysis of algorithms and maintains this mathematical rigor throughout the work. The tools developed in these opening sections are then applied to sorting, data structures, graphs, and a variety of selected algorithms including computational geometry, string algorithms, parallel models of computation, fast Fourier transforms (FFTs), and more. This book's strength lies in its encyclopedic range, clear exposition, and powerful analysis. Pseudo-code explanation of the algorithms coupled with proof of their accuracy makes this book is a great resource on the basic tools used to analyze the performance of algorithms.
2,972 citations
06 Jul 2004
TL;DR: A probabilistic measurement model for RFID readers that allow us to accurately localize RFID tags in the environment and demonstrates how such maps can be used to localize a robot and persons in their environment.
Abstract: We analyze whether radio frequency identification (RFID) technology can be used to improve the localization of mobile robots and persons in their environment. In particular we study the problem of localizing RFID tags with a mobile platform that is equipped with a pair of RFID antennas. We present a probabilistic measurement model for RFID readers that allow us to accurately localize RFID tags in the environment. We also demonstrate how such maps can be used to localize a robot and persons in their environment. Finally, we present experiments illustrating that the computational requirements for global robot localization can be reduced strongly by fusing RFID information with laser data.
770 citations
TL;DR: A novel algorithm is proposed that improves the localization by fusing an RFID system with an ultrasonic sensor system to estimate the position of the mobile robot using both GPE and LEC.
Abstract: This paper addresses a radio-frequency identification (RFID)-based mobile robot localization which adopts RFID tags distributed in a space. Existing stand-alone RFID systems for mobile robot localization are hampered by many uncertainties. Therefore, we propose a novel algorithm that improves the localization by fusing an RFID system with an ultrasonic sensor system. The proposed system partially removes the uncertainties of RFID systems by using distance data obtained from ultrasonic sensors. We define a global position estimation (GPE) process using an RFID system and a local environment cognition (LEC) process using ultrasonic sensors. Then, a hierarchical localization algorithm is proposed to estimate the position of the mobile robot using both GPE and LEC. Finally, the utility of the proposed algorithm is demonstrated through experiments.
171 citations
TL;DR: This paper discusses how RFID tags are placed in the 3-D space so that the lines linking their projections on the ground define the ldquofree waysrdquo along which the robot can (or is desired to) move.
Abstract: This paper presents an innovative mobile robot navigation technique using radio frequency identification (RFID) technology. Navigation based on processing some analog features of an RFID signal is a promising alternative to different types of navigation methods in the state of the art. The main idea is to exploit the ability of a mobile robot to navigate a priori unknown environments without a vision system and without building an approximate map of the robot workspace, as is the case in most other navigation algorithms. This paper discusses how this is achieved by placing RFID tags in the 3-D space so that the lines linking their projections on the ground define the ldquofree waysrdquo along which the robot can (or is desired to) move. The suggested algorithm is capable of reaching a target point in its a priori unknown workspace, as well as tracking a desired trajectory with a high precision. The proposed solution offers a modular, computationally efficient, and cost-effective alternative to other navigation techniques for a large number of mobile robot applications, particularly for service robots, such as, for instance, in large offices and assembly lines. The effectiveness of the proposed approach is illustrated through a number of computer simulations considering testbeds of various complexities.
147 citations
TL;DR: A robust indoor positioning system that provides 2-D positioning and orientation information for mobile objects and outperforms similar existing systems in minimizing the average positioning error is proposed.
Abstract: Ambient intelligence (AmI) considers responsive environments in which applications and services adapt their behavior according to the user's needs and changing context. One of the most challenging aspects for many applications in AmI environments is location and orientation of the surrounding objects. This is especially important for effective cooperation among mobile physical objects in such smart environments. In this paper, we propose a robust indoor positioning system that provides 2-D positioning and orientation information for mobile objects. The system utilizes low-range passive radio frequency identification (RFID) technology. The proposed system, which consists of RFID carpets and several peripherals for sensor data interpretation, is implemented and tested through extensive experiments. Our results show that the proposed system outperforms similar existing systems in minimizing the average positioning error.
116 citations