Experimental Mobile Robot Research Articles

Trajectory control of a mobile robot using neural network for frail blind persons

Abstract This paper describes a novel application of mobile robot technology to the construction of mobility for the frail blind. Trajectory control of an experimental mobile robot using proposed neural network based controller structure for frail blind persons are presented. The robot is a non-holonomic experimental mobile robot with all electronic equipments. A prescribed double triangle reference trajectory is employed to test the robot with proposed controller structure for following exact trajectory of blind disable persons. The gain parameters of the P controller are tuned with neural network. The results of the proposed adaptive neural network based P controller are improved that this controller has superior performance at adapting large disturbances on the mobile robot.

다중 GPS를 이용한 EKF 기반의 실외 위치 추정 시스템

In this paper, a high precision outdoor positioning system is newly proposed using multiple GPS receivers based on the Extended Kalman Filter (EKF). Typically, the GPS signal has the instantaneous errors that degrade the positioning seriously. Using the multiple GPS receivers instead of an expensive DGPS receiver, the positioning reliability and accuracy are improved in this research as a low cost solution. To incorporate the small displacement, an INS data have been tightly coupled to the GPS data, which has the inherit disadvantage of the cumulative error occurring over time. To achieve a stabilized and accurate positioning system, the multiple GPS receiver data are fused with the INS data through the EKF process. Through real navigation experiments of an outdoor mobile robot, the performance of the proposed system has been verified to be accurate comparable to DGPS system with a lower cost.

INTELLIGENT MOTION CONTROL FOR OMNIDIRECTIONAL MOBILE ROBOTS USING ANT COLONY OPTIMIZATION

This article presents an intelligent system-on-a-programmable-chip-based (SoPC) ant colony optimization (ACO) motion controller for embedded omnidirectional mobile robots with three independent driving wheels equally spaced at 120 degrees from one another. Both ACO parameter autotuner and kinematic motion controller are integrated in one field-programmable gate array (FPGA) chip to efficiently construct an experimental mobile robot. The optimal parameters of the motion controller are obtained by minimizing the performance index using the proposed SoPC-based ACO computing method. These optimal parameters are then employed in the ACO-based embedded kinematic controller in order to obtain better performance for omnidirectional mobile robots to achieve trajectory tracking and stabilization. Experimental results are conducted to show the effectiveness and merit of the proposed intelligent ACO-based embedded controller for omnidirectional mobile robots. These results indicate that the proposed ACO-based embedded optimal controller outperforms the nonoptimal controllers and the conventional genetic algorithm (GA) optimal controllers.

Distributed learning with biogeography-based optimization: Markov modeling and robot control

Biogeography-based optimization (BBO) is an evolutionary algorithm that is motivated by biogeography, which is the science that describes how biological species are geographically distributed. We extend the standard BBO algorithm to distributed learning, which does not require centralized coordination of the population. We call this new algorithm distributed BBO (DBBO). We derive a Markov model for DBBO, which provides an exact mathematical model of the DBBO population in the limit as the generation number approaches infinity. We use standard benchmark functions to compare BBO and DBBO with several other evolutionary optimization algorithms, and we show that BBO and DBBO give competitive results, especially for multimodal problems. Benchmark results show that DBBO performance is almost identical to BBO. We also demonstrate DBBO on a real-world application, which is the optimization of robot control algorithms, using both simulated and experimental mobile robots.

Local Path Planner for Mobile Robot in Dynamic Environment based on Distance Time Transform Method

For unknown field explorations in disaster areas, mobile robots that can replace human workers in dangerous environments can greatly improve disaster response efforts by reducing additional risk to human life. However, realizing such robot systems requires various technologies. In particular, path planning is quite important because mobile robots in the real world are surrounded by dynamic obstacles such as people, which may hinder a robot’s activities. In this research, we propose a collision avoidance method for a mobile robot in dynamic environments, considering the near-term motion and “personal space” of dynamic obstacles. Our method consists of the following three steps: “estimation,” “conversion,” and “planning.” In the estimation step, dynamic and static obstacles are recognized and their future positions are estimated from their previous motions. Next, in the conversion step, a time axis is added to construct a 3-D time–space coordinate system. Finally, in the planning step, a distance–time transform is applied to plan a safe 3-D path from the robot's current position to the desired goal. The proposed method has been implemented on our mobile robot and mobile robot simulator and experiments were conducted to verify its usefulness.

A robust lane recognition technique for vision-based navigation with a multiple clue-based filtration algorithm

This paper proposes a novel multiple clue-based filtration algorithm (MCFA), which is developed to detect lane markings on roads using camera vision images for autonomous mobile robot navigation. The main goal of the algorithm is the robust estimation of the relative position and angle of the lane in the image by using multiple clues based on different characteristics of the lane. In particular, robustness against environmental changes is enhanced greatly since a dynamic model of the lane, besides static features of the lane such as color, intensity, etc., is incorporated for reliable estimation. The efficiency of the algorithm is verified through mobile robot experiments under various extreme illumination conditions in outdoor environments. The increased robustness performance enables reliable closed-loop control of a mobile robot that operates in a variety of navigation-related missions.

Self-Localization of an Omnidirectional Mobile Robot Based on an Optical Flow Sensor

An omnidirectional mobile robot has the advantage that three degrees of freedom of motion in a 2D plane can be set independently, and it can thus move in arbitrary directions while maintaining the same heading. Dead reckoning is often used for self-localization using onboard sensors in omnidirectional robots, by means of measuring wheel velocities from motor encoder data, as well as in car-like robots. However, omnidirectional mobile robots can easily slip because of the nature of omni-wheels with multiple free rollers, and dead reckoning will not work if even one wheel is not attached to the ground. An odometry method where the data is not affected by wheel slip must be introduced to acquire high quality self-location data for omnidirectional mobile robots. We describe a method to obtain robot ego-motion using camera images and optical flow calculation, i.e., where the camera is used as a velocity sensor. In this paper, a silicon retina vision camera is introduced as a mobile robot sensor, which has a good dynamic range under various lighting conditions. A Field-Programmable Gate Array (FPGA) optical flow circuit for the silicon retina is also developed to measure ego-motion of the mobile robot. The developed optical flow calculation system is introduced into a small omnidirectional mobile robot and evaluation experiments for the mobile robot ego-motion are carried out. In the experiments, the accuracy of self-location by the dead reckoning and optical flow methods are evaluated by comparison using motion capture. The results show that the correct position is obtained by the optical flow sensor rather than by dead reckoning.

FEB를 이용한 이동로봇의 장애물 회피와 국지경로계획

This paper presents a new obstacle-avoidance method for mobile robots. This approach, called the FEB (Fast Elastic Band) method, has been developed and successfully tested on the experimental mobile robot PHOPE-1S. The FEB method eliminates the shortcomings of the elastic band method previously introduced, yet retains all the advantages of its predecessor. The FEB algorithm is computationally efficient, and it allows continuous and fast motion of the mobile robot without stopping for obstacles. The FEB-controlled mobile robot traverses very densely cluttered obstacle courses and is able to pass through narrow openings or narrow corridors without oscillations. The results of the simulation and experiment have verified the validity of the proposed method.

Dependable control of uncertain linear systems based on set-theoretic methods

This article concerns the feedback control of discrete-time systems subject to disturbances and uncertainties in both model parameters and signal measurements. The uncertainties are assumed to be unknown but bounded and thus characterised by closed intervals or sets. The main result is a new approach to design a feedback controller keeping the system state in a target set. First, a method is proposed that computes minimal enclosures of the set of reachable states, which are consistent with the uncertain input and output measurements and the system dynamics. Then, a control method to keep the current state set in the target set is developed, which extends control techniques based on invariant polyhedra. The method is illustrated by a mobile robot experiment.

A sub goal seeking approach for reactive navigation in complex unknown environments

Reactive-based approaches are widely used in autonomous navigation. However, in complex unknown environments, pure reactive-based navigation still poses a few challenges since it can be easily trapped by a local minimum and may produce some extra manoeuvres. This paper presents the design of a reactive-based approach for navigation in complex and unknown environments called sub goal seeking, in which depth point maps of the environment are analysed to extract free spaces around the robot. These spaces are then evaluated the one that is most likely to lead to the final goal is chosen as a sub goal. The robot then drives towards these sub goals, instead of the final goal until it is visible. By analysing the environmental structure, dead-ends within robot sensory range are able to be detected thus reducing the chance of being trapped and also reducing unnecessary manoeuvres. This paper also evaluates the performance of the sub goal seeking approach using three criteria, goal achievable ability, safety and maneuvering through extensive simulation and real mobile robot experiments.

실외 자율주행 로봇을 위한 다수의 동적 장애물 탐지 및 선속도 기반 장애물 회피기법 개발

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.

A novel pseudo—model following integral variable structure control for vision feedback autonomous mobile robots

The control of autonomous mobile robots is an important topic in the literature of intelligent robotics. With great progress in intelligent systems, mobile robots are often autonomously driven. The computer vision replaces the human eye to be the feedback component and, through image processing, the necessary information can be deciphered. However, owing to the complexity of the whole system, a mathematical model is usually not available. Hence, many model—based robust control rules cannot be applied to vision—guided autonomous mobile robots. At the same time, the calculation of image processing takes up a long time, from fetching the image to creating the image information. In order to solve these problems, this paper proposes a novel pseudo—model following integral variable structure control (PMF—IVSC). By the grey model (GM(1, 2)) method, i.e. the grey model with one output and two inputs, the plant and the reference model are converted into an accumulated model and a pseudo—reference model respectively. Applying the IVSC, the accumulated model is governed to follow the pseudo—reference model. The PMF—IVSC will successfully solve the unknown system's model following control. Finally, an experimental mobile robot is developed to examine the potential of the proposed control scheme. This paper carries out the vision—based experiment of automatically driving while lane following. The experimental results show that the proposed controller will eliminate the swinging phenomenon and increase the accuracy while tracking. The results also show the practical capability of the proposed rule in intelligent robotic systems.

Hardware Feedback Self-Organizing Map and its Application to Mobile Robot Location Identification

We propose new hardware architecture for the self-organizing map (SOM) and feedback SOM (FSOM). Due to the parallel structure in the SOM and FSOM algorithm, customized hardware considerably speeds-up processing. Proposed hardware FSOM identifies the location of a mobile robot from a sequence of direction data. The FSOM is self-trained to cluster data to identify where the robot is. The proposed FSOM design is described in C and VHDL, and its performance is tested by simulation using actual sensor data from an experimental mobile robot. Results show that the hardware FSOM succeeds in self-learning to find the robot’s location. The hardware FSOM is estimated to process 6,992 million weight-vector elements per second.

Global Map Building and Navigation of Mobile Robot Based on Ultrasonic Sensor Data Fusion

In mobile robotics, ultrasonic sensors became standard devices for collision avoiding. Moreover, their applicability for map building and navigation has exploited in recent years. In this paper, as the preliminary step for developing a multi-purpose autonomous carrier mobile robot to transport trolleys or heavy goods and serve as robotic nursing assistant in hospital wards. The aim of this paper is to present the use of multi-sensor data fusion such as ultrasonic sensor, IR sensor for mobile robot to navigate, and presents an experimental mobile robot designed to operate autonomously within both indoor and outdoor environments. The global map building based on multi-sensor data fusion is applied for recognition an obstacle free path from a starting position to a known goal region, and simultaneously build a map of straight line segment geometric primitives based on the application of the Hough transform from the actual and noisy sonar data. We will give an explanation for the robot system architecture designed and implemented in this study and a short review of existing techniques, Hough transform, since there exist several recent thorough books and review paper on this paper. Experimental results with a real Pioneer DX2 mobile robot will demonstrate the effectiveness of the discussed methods.

Mixed<tex>$H_2/H_infty$</tex>Design for a Decentralized Discrete Variable Structure Control With Application to Mobile Robots

In this paper, a decentralized discrete variable structure control via mixed H2/H infinity design was developed. In the beginning, the H2-norm of output error and weighted control input was minimized to obtain a control such that smaller energy consumption with bounded tracking error was assured. In addition, a suitable selection of this weighted function (connected with frequency) could reduce the effect of disturbance on the control input. However, an output disturbance caused by the interactions among subsystems, modeling error, and external load deteriorated system performance or even brought about instability. In this situation, the H infinity-norm of weighted sensitivity between output disturbance and output error was minimized to attenuate the effect of output disturbance. Moreover, an appropriate selection of this weighted function (related to frequency) could reject the corresponding output disturbance. No solution of Diophantine equation was required; the computational advantage was especially dominated for low-order system. For further improving system performance, a switching control for every subsystem was designed. The proposed control (mixed H2/H infinity DDVSC) was a three-step design method. The stability of the overall system was verified by Lyapunov stability criterion. The simulations and experiments of mobile robot were carried out to evaluate the usefulness of the proposed method.

An observer-controller combination for a unicycle mobile robot

This paper considers the problem of output feedback trajectory tracking with a unicycle mobile robot system. A state-feedback controller for the non-linear error dynamics of the robot is combined with an observer that estimates the orientation error based on available trajectory information and measurement of the position coordinates. A stability analysis, derivation and implementation of the resulting controller is given and tested on an experimental mobile robot.

CeDAR: A real-world vision system

We report on the development of a multi-purpose active visual sensor system for real-world application. The C abl e - D rive A ctive-Vision R obot, CeDAR, has been designed for use on a diverse range of platforms to perform a diverse range of tasks. The novel, biologically inspired design has evolved from a systems-based approach. The mechanism is compact and lightweight and is capable of motions that exceed human visual performance and earlier mechanical designs. The control system complements the mechanical design to implement the basic visual behaviours of fixation, smooth pursuit and saccade, with stability during high-speed motions, high precision and repeatability. Real-time algorithms have been developed that process stereo colour images, resulting in a suite of basic visual competencies. We have developed a scheme to fuse the results of the visual algorithms into robust task-oriented behaviours by adopting a statistical framework. CeDAR has been successfully used for experiments in autonomous vehicle guidance, object tracking and visual sensing for mobile robot experiments.

Study on Articulated Mobile Robot Introduction and Experiment in Perspective Control

The perspective control for Articulated Body Mobile Robot KORYU is discussed. Being constructed by linear connected articulations, the segments of the KORYU except that of foremost one can predict future posture by the information of adjacent frontal segment while locomotion. The paper introduces the method to make exact prediction of future posture for any segment of KORYU by using Maximum Principle and the method is then developed to be used for the locomotion on arbitrary topology of the uneven ground. The validity is verified by experiment using the mechanical model ""KR-I"".

459 人間の巧みさの発現と包摂構造 : スポーツにおける巧みさへのアプローチ

This study has investigated the approach to the intelligence or proficient skills in sports for breaking through the problems of theconventional sports sciences with human power. It is based on the emergence of the human's dexterity or proficient skills and the mobile robot experiments with the subsumption architecture (SA). It insists that the human dexterity or the intelligence and the proficient skills in sports exist in the subsumption architecture.

Odor-Based Navigational Strategies for Mobile Agents

Although most species are sensitive to various chemicals, and olfactory skills such as search strategies for finding nutritious substance are seemingly simple, these basic skills are still not fully understood. Traditionally, chemotaxis has been considered as the fundamental chemosensory navigational mechanism for most species. Previous studies have demonstrated, however, that biased random walk is the more fundamental navigational strategy in various types of diffusion fields. Biased random walk is a robust and slow search process, but it has been shown that its efficiency can be enhanced if it is combined with chemotaxis. The present article summarizes previous findings of the authors in olfactory navigation and extends the work to searching in dynamic flow fields, including turbulence. In addition, a cooperative, multi-agent search method has been investigated and shown to be successful in enhancing search efficiency. The significance of these findings is discussed in the context of future plans to implement these strategies in experimental mobile robots.