A Novel Path Prediction Strategy for Tracking Intelligent Travelers

There are various technologies for positioning and tracking of intelligent travelers such as wireless local area networks (WLAN). However, the loss of actual positioning data is a common problem due to unexpected disconnection between tracking references and the traveler. Disconnection of the mobile terminal (MT) from the access points (AP) in WLAN-based systems is the example case of the problem. While enhancement of the physical system itself can reduce the risk of disconnections, complementary algorithms provide even more robustness in localization and tracking of the traveler. This research aims to develop a novel path prediction system which could keep track of the traveler during temporary shortage of actual positioning data. The system takes the advantage of the past trajectory information to compensate for the missing information during disconnections. A novel decision support system (DSS) is devised with the ability of learning decisional as well as kinematical behaviors of intelligent travelers. The system is then used in path prediction mode for reconstructing the missing parts of the trajectory when actual positioning data is unavailable. An ActivMedia Pioneer robot navigating under fuzzy artificial potential fields (APF) and blind-folded human subjects are the two types of intelligent travelers. The reactive motion of robots and path planning strategies of the blinds are similar in that both of them locally acquire knowledge and explore the space based on route-like spatial cognition. It is proposed and shown that route-like intelligent motion is based on a combination of decisional and kinematical factors. The system is designed in such a way to integrate these two types of motion factors using causal inference mechanism of the fuzzy cognitive map (FCM). The FCM nodes are a novel selection of kinematical factors. Genetic algorithm (GA) is then used to train the FCM to be able to replicate the decisional behaviors of the intelligent traveler. Experimental works show the capabilities of the developed DSS in human path prediction using both simulated and actual WLAN-based positioning dataset. Locational error is set to be limited to 1 m which is suitable for wireless tracking of human subjects with up to 10% improvement compared to the most related works. Both simulation and actual experiments were also carried out on the Pioneer platform. The accuracy in prediction of robot trajectory was obtained about 83% with considerable improvement compared to the recent methods. Apart from the positioning algorithm of this dissertation, there are several applications of this DSS to other areas including assistive technology for the blind and human-robot interaction

Development of a Mobile Robot Local Navigation System Based on Fuzzy-Logic Control and Actual Virtual Target Switching

Robot local path planning in an unknown and changing environment with uncertainties is one of the most challenging problems in robotics which involves the integration of many different bodies of knowledge. This makes mobile robotics a challenge worldwide which for many years has been investigated by researchers. Therefore in this thesis, a new fuzzy logic control system is developed for reactive navigation of a behavior-based mobile robot. The motion of a Pioneer 3TM mobile robot was simulated to show the algorithm performance. The robot perceives its environment through an array of eight sonar range finders and self positioning-localization sensors. The robot environment consists of walls and dead end traps from any size and shape, as well as other stationary obstacles and it is assumed to be fully unknown. Robot behaviors consist of obstacle avoidance, target seeking, speed control, barrier following and local minimum avoidance. While the fuzzy logic body of the algorithm performs the main tasks of obstacle avoidance, target seeking, and speed adjustment, an actual-virtual target switch strategy integrated with the fuzzy logic algorithm enables the robot to show wall following behavior when needed. This combinational approach which uses a new kind of target shift, significantly results in resolving the problem of multiple minimum in local navigation which is an advantage beyond the pure fuzzy logic approach and the common virtual target switch techniques. In this work, multiple traps may have any type of shape or arrangement from barriers forming simple corners and U-shape dead ends to loops, maze, snail shape, and many others. Under the control of the algorithm, the mobile robot makes logical trajectories toward the target, finds best ways out of dead ends, avoids any types of obstacles, and adjusts its speed efficiently for better obstacle avoidance and according to power considerations and actual limits. From TRAINER Software and Colbert Program which were used in the simulation work, the system managed to solve all the problems in sample environments and the results were compared with results from other related methods to show the effectiveness and robustness of the proposed approach

Optimal accuracy and runtime trade-off in wavelet based single-trial P300 detection

Single-trail detection of P300 from EEG signals is the main challenge of diagnostic purposes and research applications. In this article, Wavelet Transform is used for feature extraction from EEG signals. The goal is to prove the capability of wavelet transform in P300 feature extraction. A number of established wavelet feature extraction methods were evaluated from accuracy and computation speed perspectives. To conduct uniform evaluation, Support Vector Machine (SVM) was used for classification of all methods. The results show that DWT can be fast in computing signal features with lower accuracy, while a combination of DWT and T-CWT is proven to be more accurate when real-time computation is concerned

Motion modelling using concepts of fuzzy artificial potential fields

Artificial potential fields (APF) are well established for reactive navigation of mobile robots. This paper describes a fast and robust fuzzy-APF on an ActivMedia AmigoBot.Obstacle-related information is fuzzified by using sensory fusion, which results in a shorter runtime. In addition, the membership functions of obstacle direction and range have been merged into one function, obtaining a smaller block of rules. The system is tested in virtual environments with non-concave obstacles. Then, the paper describes a new approach to motion modelling where the motion of intelligent travellers is modelled by consecutive path segments. In previous work, the authors described a reliable motion modelling technique using causal inference of fuzzy cognitive maps (FCM) which has been efficiently modified for the purpose of this contribution. Results and analysis are given to demonstrate the efficiency and accuracy of the proposed motion modelling algorithm

A bacteria foraging algorithm for solving integrated multi-period cell formation and subcontracting production planning in a dynamic cellular manufacturing system

The bacteria foraging algorithm (BFA) is a new computation technique inspired by the social foraging behaviour of Escherichia coli (E. coli) bacteria. Since the introduction of the BFA by Kevin M. Passino, there have been many challenges in employing this algorithm to problems other than those for which the algorithm was proposed. This research aims to apply this emerging optimisation algorithm to develop a mixed-integer programming model for designing cellular manufacturing systems (CMSs), and production planning in dynamic environments. In dynamic environments, product mix and part demand vary under multi-period planning horizons. Thus the best-designed cells for one period may not be adequate for subsequent periods, requiring their reconstruction. The advantages of the proposed model are as follows: consideration of batch inter-cell and intra-cell material handling by assuming the sequence of operations, allowing for alternative process plans for part types, and consideration of machine copying, with an emphasis on the effect of trade-offs between production and outsourcing costs. The goal is to minimise the sum of the machines constant and variable costs, inter-cell and intra-cell material handling costs, reconstruction costs, partial subcontracting costs, and inventory carrying costs. In addition, a newly-developed BFA-based optimisation algorithm has been compared with the branch and bound algorithm. The results suggest that the proposed algorithm performs better than related works

Development of a new minimum avoidance system for a behavior-based mobile robot

A new fuzzy logic algorithm is developed for mobile robot navigation in local environments. A Pioneer robot perceives its environment through an array of eight sonar sensors and self positioning-localization sensors. While the fuzzy logic body of the algorithm performs the main tasks of obstacle avoidance and target seeking, an actual–virtual target switching strategy resolves the problem of limit cycles in any type of dead-ends encountered on the way to the target. This is an advantage beyond pure fuzzy logic approach and common virtual target techniques. In this work, multiple traps may have any shape or arrangement from barriers forming simple corners and U-shape dead-ends to loops, maze, snail shape, and other complicated shapes. Robot trajectories are demonstrated by simulation work and compared with results from other related methods to prove the robustness of this method

Virtual force field algorithm for a behaviour-based autonomous robot in unknown environments

The present paper describes a real-time motion-planning approach which lies in the integration of three techniques: fuzzy logic (FL), virtual force field (VFF), and boundary following (BF). The FL algorithm is used for velocity control based on sonar readings. The path-planning algorithm is based on the VFF and BF methods. The proposed navigation system differs from previous works in terms of using different algorithms for planning robot motion. Other improvements concern functional and computational aspects of the design and integration of the modules. The robot shows robust performance in complex situations and local minimum scenarios. Simulation results show the effectiveness of the developed system in various environments with long walls, U-shaped, maze-like, and other types of clutter

An agile FCM for real-time modeling of dynamic and real-life systems

Fuzzy cognitive map (FCM) is a well-established model of control and decision making based on neural network and fuzzy logic methodologies. It also serves as a powerful systematic way for analyzing real-life problems where tens of known, partially known, and even unknown factors contribute to complexity of a system. FCM-based inference requires a neural activation function much like other neural network systems. In modeling, in addition to an activation function, FCM involves with weight training to learn about relationships as they exist among contributing factors. Therefore, numerous contributing factors could be analyzed to understand the behaviors of factors within a real-life system and to represent it in form of tangible matrices of weights. This article discusses a new incremental FCM activation function, named cumulative activation, and introduces a new weight training technique using simulated annealing (SA) known as agile FCM. Smooth variation of FCM nodes that is due to cumulative nature of inference results into faster convergence, while a unique minimum cost solution is guaranteed using the SA training module that is entirely expert-independent. A combination of these two techniques suits time-related applications where inclusion of temporal features is necessary. The resulted system is examined through numerical example datasets where the candidate FCM shows sensitivity to dynamic variables over time. A real-life example case is included as well to further support the effectiveness of the developed FCM in modeling of natural and complex systems

A review on positioning techniques and technologies: a novel AI approach

There are variety of positioning techniques applied to tracking of mobile objects such as mobile robots, handheld devices carried by human subject, etc. With the advent of new technologies, new strategies have emerged from combination of algorithms and those technical capabilities. This study is dedicated to a review of past and current approaches to positioning, including their advantages and shortcomings. More focus is given on trilateral radiolocation especially for indoor human motion tracking. A solution is sought to resolve the problem of algorithm failure with applicability to all areas of positioning and tracking including trilateration when less than three reference points are available

Automatic navigation of mobile robots in unknown environments

Online navigation with known target and unknown obstacles is an interesting problem in mobile robotics. This article presents a technique based on utilization of neural networks and reinforcement learning to enable a mobile robot to learn constructed environments on its own. The robot learns to generate efficient navigation rules automatically without initial settings of rules by experts. This is regarded as the main contribution of this work compared to traditional fuzzy models based on notion of artificial potential fields. The ability for generalization of rules has also been examined. The initial results qualitatively confirmed the efficiency of the model. More experiments showed at least 32 % of improvement in path planning from the first till the third path planning trial in a sample environment. Analysis of the results, limitations, and recommendations is included for future work