Indoor positioning systems (IPSs) locate objects in closed structures such as office buildings, hospitals, stores, factories, and warehouses, where Global Positioning System devices generally do not work. Most available systems apply wireless concepts, optical tracking, and/or ultrasound. This paper presents a standalone IPS using radio frequency identification (RFID) technology. The concept is based on an object carrying an RFID reader module, which reads low-cost passive tags installed next to the object path. A positioning system using a Kalman filter is proposed. The inputs of the proposed algorithm are the measurements of the backscattered signal power propagated from nearby RFID tags and a tag-path position database. The proposed algorithm first estimates the location of the reader, neglecting tag-reader angle-path loss. Based on the location estimate, an iterative procedure is implemented, targeting the estimation of the tag-reader angle-path loss, where the latter is iteratively compensated from the received signal strength information measurement. Experimental results are presented, illustrating the high performance of the proposed positioning system.

A Standalone RFID Indoor Positioning System Using Passive Tags

Mobile robots are increasingly used in automated industrial environments. There are also other applications like planet exploration, surveillance, landmine detection, etc. In all these applications, in order that the mobile robots perform their tasks, collision-free path planning is a prerequisite. This article provides an overview of the research progress in path planning of a mobile robot for off-line as well as on-line environments. Commonly used classic and evolutionary approaches of path planning of mobile robots have been addressed. Review shows that evolutionary optimization algorithms are computationally efficient and hence are increasingly being used in tandem with classic approaches while handling Non-deterministic Polynomial time hard (NP-hard) problems. Also, challenges involved in developing a computationally efficient path planning algorithm are addressed.

	 

	Key words: Path planning, mobile robot, off-line environment, on-line environment, classic, evolutionary algorithms.

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Optimal path planning of mobile robots: A review

Seeking multiple optima simultaneously, which multimodal optimization aims at, has attracted increasing attention but remains challenging. Taking advantage of ant colony optimization (ACO) algorithms in preserving high diversity, this paper intends to extend ACO algorithms to deal with multimodal optimization. First, combined with current niching methods, an adaptive multimodal continuous ACO algorithm is introduced. In this algorithm, an adaptive parameter adjustment is developed, which takes the difference among niches into consideration. Second, to accelerate convergence, a differential evolution mutation operator is alternatively utilized to build base vectors for ants to construct new solutions. Then, to enhance the exploitation, a local search scheme based on Gaussian distribution is self-adaptively performed around the seeds of niches. Together, the proposed algorithm affords a good balance between exploration and exploitation. Extensive experiments on 20 widely used benchmark multimodal functions are conducted to investigate the influence of each algorithmic component and results are compared with several state-of-the-art multimodal algorithms and winners of competitions on multimodal optimization. These comparisons demonstrate the competitive efficiency and effectiveness of the proposed algorithm, especially in dealing with complex problems with high numbers of local optima.

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Adaptive Multimodal Continuous Ant Colony Optimization

This paper presents a global localization system for an indoor autonomous vehicle equipped with odometry sensors and a radio-frequency identification (RFID) reader to interrogate tags located on the ceiling of the environment. The RFID reader can measure the phase of the signals coming from responding tags. This phase has non-univocal dependence on the distance robot tag, but in the considered frequency, it is really sensitive to a change in the position of the robot. For this reason, a multihypothesis Kalman filtering approach provides a really satisfactory performance even in the case that a very small density of tags is used: In the experimental tests, an average position estimation error of about 4 cm is achieved using only two tags for an area of about 5 m2.

Mobile Robot Localization Using the Phase of Passive UHF RFID Signals

To automate assembly planning for complex products such as aircraft components, an assembly planning and simulation system called AutoAssem has been developed. In this paper, its system architecture is presented; the main components and the key technologies in each component are discussed. The core functions of the system that have been focused include Digital Assembly Modeling, Assembly Sequence Planning (ASP), Path Planning, Visualization, and Simulation. In contrast to existing assembly planning systems, one of the novelties of the system is it allows the assembly plans be automatically generated from a CAD assembly model with minimal manual interventions. Within the system, new methodologies have been developed to: (i) create Assembly Relationship Matrices; (ii) plan assembly sequences; (iii) generate assembly paths; and (iv) visualize and simulate assembly plans. To illustrate the application of the system, the assembly of a worm gear reducer is used as an example throughout this paper for demonstration purpose. AutoAssem has been successfully applied to virtual assembly design for various complex products so far.

AutoAssem: An Automated Assembly Planning System for Complex Products

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.

A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion

The Efficient-Energy Coverage (EEC) problem is an important issue when implementing Wireless Sensor Networks (WSNs) because of the need to limit energy use. In this paper, we propose a new approach to solving the EEC problem using a novel Ant Colony Optimization (ACO) algorithm. The proposed ACO algorithm has a unique characteristic that conventional ACO algorithms do not have. The proposed ACO algorithm (Three Pheromones ACO, TPACO) uses three types of pheromones to find the solution efficiently, whereas conventional ACO algorithms use only one type of pheromone. One of the three pheromones is the local pheromone, which helps an ant organize its coverage set with fewer sensors. The other two pheromones are global pheromones, one of which is used to optimize the number of required active sensors per Point of Interest (PoI), and the other is used to form a sensor set that has as many sensors as an ant has selected the number of active sensors by using the former pheromone. The TPACO algorithm has another advantage in that the two user parameters of ACO algorithms are not used. We also introduce some techniques that lead to a more realistic approach to solving the EEC problem. The first technique is to utilize the probabilistic sensor detection model. The second method is to use different kinds of sensors, i.e., heterogeneous sensors in continuous space, not a grid-based discrete space. Simulation results show the effectiveness of our algorithm over other algorithms, in terms of the whole network lifetime.

Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

Sensors in most wireless sensor networks (WSNs) work with batteries as their energy source, it is usually infeasible to recharge or replace batteries when they discharge. Thus, solving the efficient-energy coverage (EEC) problem is an important issue for a WSN. Therefore, it is necessary to schedule the activities of the devices in a WSN to save the network's limited energy and prolong its lifetime. In this paper, we propose an ant-colony-based acheduling algorithm (ACB-SA) to solve the EEC problem. Our algorithm is a simplified version of the conventional ant colony optimization algorithm, optimized for solving the EEC problem. We also use the probability sensor detection model and apply our proposed algorithm to a heterogeneous sensor set, which represents a more realistic approach to solving the EEC problem. Simulation results are performed to verify the effectiveness of the ACB-SA for solving the EEC problem in comparison with other algorithms.

Ant-Colony-Based Scheduling Algorithm for Energy-Efficient Coverage of WSN

Mobile robots can be applied to a wide range of problems, and the demand for these applications has risen in recent years, increasing interest in the study of mobile robotics. Many studies have examined the path planning problem, one of the most important issues in mobile robotics. However, the grid paths found by traditional planners are often not the true shortest paths or are not smooth because their potential headings are artificially constrained to multiples of 45 degrees. These paths are unfit for application to mobile robots because the high number of heading changes increases the energy required to move the mobile robot. Some studies have proposed a post-processing step to smooth the grid path. However, in this case, the post-smoothed path may not necessarily find the true shortest path because the post-smoothed path is still constrained to headings of multiples of 45 degrees. This study attempts to develop a global path planner that can directly find an optimal and smoother path without post-processing to smooth the path. We propose a heterogeneous-ants-based path planner (HAB-PP) as a global path planner to overcome the shortcomings mentioned above. The HAB-PP was created by modifying and optimizing the global path planning procedure from the ant colony optimization (ACO) algorithm. The proposed algorithm differs from the traditional ACO path planning algorithm in three respects: modified transition probability function for moving ants, modified pheromone update rule, and heterogeneous ants. The simulation results demonstrate the effectiveness of the HAB-PP.

Heterogeneous-ants-based path planner for global path planning of mobile robot applications

This paper proposes an improved localization scheme for self-localization of an mobile robot by fusing RFID localization system and ultrasonic measurements The novel localization system for an indoor mobile robot is proposed to improve the efficiency of mobile robot system The proposed system is based on previous RFID localization system, which removes the uncertainty of robot location using the distance measurements by ultra-sonic sensors We address more efficient localization algorithm than the previous system for the mobile robot in the given environment First, RFID and wheel encoder localization systempsilas uncertainty, which may result in inaccurate location data, is modeled And then, the algorithm for estimating each uncertainty is proposed for localization Finally, a proposed algorithm successfully demonstrated through simulation experiments conducted under certain assumption

Joon-Woo Lee

Papers

A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion

Energy-Efficient Coverage of Wireless Sensor Networks Using Ant Colony Optimization With Three Types of Pheromones

Ant-Colony-Based Scheduling Algorithm for Energy-Efficient Coverage of WSN

Heterogeneous-ants-based path planner for global path planning of mobile robot applications

An improved localization system with RFID technology for a mobile robot