General Feature Extraction for Mapping and Localization of a Mobile Robot Using Sparsely Sampled Sonar Data

This study introduces a method of general feature extraction for building a map and localization of a mobile robot using only sparsely sampled sonar data. Sonar data are acquired by using a general fixed-type sensor ring that frequently provides false returns on the locations of objects. We first suggest a data association filter that can classify sets of sonar data that are associated with the same hypothesized feature into one group. A feature extraction method is then introduced to decide the exact geometric parameters of the hypothesized feature in the group. We also show the possibility of extracting a circle feature consistently as well as a line or a point feature by using the proposed filter. These features are then assembled to build a global map and applied to extended Kalman filter-based localization of the robot. We demonstrate the validity of the proposed filter with the results of mapping and localization produced by real experiments.     

Robust RBPF-SLAM for Indoor Mobile Robots Using Sonar Sensors in Non-Static Environments

We propose a robust simultaneous localization and mapping (SLAM) with a Rao-Blackwellized particle filter (RBPF) algorithm for mobile robots using sonar sensors in non-static environments. The algorithm consists of three parts: sampling from multiple ancestor sets, estimating intermediate paths for map updates and eliminating spurious landmarks using negative information from sonar sensors. The proposed sampling method, in which particles are sampled from multiple ancestor sets, increases the robustness of the estimation of the robot's pose, even if environmental changes corrupt observations. This step increases the probability of some particles being sampled from correct ancestor sets that are updated by observations reflected from stationary objects. When particles are sampled from several time steps earlier, however, observations at intermediate time steps cannot be used to update the map because of the lack of information about the intermediate path. To update the map with all sensor information, the intermediate path is estimated after particles are sampled from ancestor sets. Finally, spurious landmarks still exist on the map representing objects that were eliminated or that were extracted by error in cluttered areas. These are eliminated in the final step using negative information from the sonar sensors. The performance of the proposed SLAM algorithm was verified through simulations and experiments in various non-static environments.     

Navigation Using an Environmental Magnetic Field for Outdoor Autonomous Mobile Robots

GPS and laser range finders are generally utilized in current robot navigation. However, information from the magnetic field and electronic compass is not, since it is dynamically changing at every position. In this paper, the relationship between the intensity of a magnetic field in the environment and its position is taken into account by utilizing a three-axis magnetic sensor to scan the magnetic field in the environment to build a database. The mobile robot navigates by performing trajectory tracking based on the database. The experimental results show that by applying the proposed method, the mobile robot is able to navigate in an outdoor environment with reliable accuracy.     

An evolutionary algorithm for simultaneous localization and mapping (SLAM) of mobile robots

This paper presents a novel algorithm for simultaneous localization and mapping (SLAM) of mobile robots. The algorithm, termed Evolutionary SLAM, is based on an island model genetic algorithm (IGA). The IGA searches for the most probable map(s) such that the underlying robot's pose(s) provide(s) a robot with the best localization information. The correspondence problem in SLAM is solved by exploiting the property of natural selection, to support only better-performing individuals to survive. The algorithm does not follow any explicit heuristics for loop closure, rather it maintains multiple hypotheses to solve the loop-closing problem. The algorithm processes sensor data incrementally and, therefore, has the capability to work online. Experimental results in different indoor environments validate the robustness of the proposed algorithm.     

Development of a Positioning Technique for an Urban Area Using Omnidirectional Infrared Camera and Aerial Survey Data

This paper describes an outdoor positioning system for vehicles that can be applied to an urban canyon by using an omnidirectional infrared (IR) camera and a digital surface model (DSM). By means of omnidirectional IR images, this system enables robust positioning in urban areas where satellite invisibility caused by buildings hampers high-precision GPS measurements. The omnidirectional IR camera can generate IR images with an elevation of 20–70° for the surrounding area of 360°. The image captured by the camera is highly robust to light disturbances in the outdoor environment. Through the IR camera, the sky appears distinctively dark; this enables easy detection of the border between the sky and the buildings captured in white due to the difference in the atmospheric transmittance rate between visible light and IR rays. The omnidirectional image, which includes several building profiles, is compared with building-restoration images produced by the corresponding DSM in order to determine the self-position. Field experiments in an urban area show that the proposed outdoor positioning method is valid and effective, even if high-rise buildings cause satellite blockage that affects GPS measurements.     

Sensor Fault Tolerance Method by Using a Bayesian Network for Robot Behavior

This paper presents FTBN, a new framework that performs learning autonomous mobile robot behavior and fault tolerance simultaneously. For learning behavior in the presence of a robot sensor fault this framework uses a Bayesian network. In the proposed framework, sensor data are used to detect a faulty sensor. Fault isolation is accomplished by changing the Bayesian network structure using interpreted evidence from robot sensors. Experiments including both simulation and a real robot are performed for door-crossing behavior using prior knowledge and sensor data at several maps. This paper explains the learning behavior, optimal tracking, exprimental setup and structure of the proposed framework. The robot uses laser and sonar sensors for door-crossing behavior, such that each sensor can be corrupted during the behavior. Experimental results show FTBN leads to robust behavior in the presence of a sensor fault as well as performing better compared to the conventional Bayesian method.     

A General Framework for Planning Landmark-Based Motions for Mobile Robots

In this paper, we present a novel generic approach for planning landmark-based motion. The method consists in selecting automatically the most relevant landmarks along a preplanned geometric path. It proposes a strategy to correct the trajectory and to smoothly switch among the landmarks of the environment. Experimental results highlight the relevance of the proposed formalism.     

New Entropy-Based Adaptive Particle Filter for Mobile Robot Localization

Over the last decade, particle filters have been applied with great success to a variety of state estimation problem. The standard particle filter suffers poor efficiency during the estimation process, especially in the global localization and kidnapped problem. In this paper, we proposed a novel information entropy-based adaptive approach to improve the efficiency of particle filters by adapting the number of particles. The information entropy-based adaptive particle filter approaches use the information entropy to present the uncertainty of a mobile robot to the environment. By continuously obtaining the sensor information, the robot gradually reduces the uncertainty to the environment and, therefore, reduces the particle number for the estimation process. We derived the mathematic equation relating the information entropy with particle number. Extensive localization experiments using a mobile robot showed that our approach yielded drastic improvements and efficiency performance over a standard particle filter with fixed particles and over other adaptive approaches.     

Online Short-Term Multiple Sound Source Mapping for a Mobile Robot by Robust Motion Triangulation

The paper describes a two-dimensional (2-D) sound source mapping system for a mobile robot. The robot localizes the directions of sound sources while moving and estimates the positions of sound sources using triangulation from a short time period of directional localization results. Three key components are denoted. (i) Directional localization and separation method of different pressure sound sources by combining the Delay and Sum Beam Forming (DSBF) and the Frequency Band Selection (FBS) algorithms. (ii) The design of the microphone array by beam forming simulation to increase the resolution of the localization procedure and its robustness to ambient noise. (iii) Sound position estimation by using the RAndom SAmple Consensus (RANSAC) algorithm. Then we achieved 2-D multiple sound source mapping from time-limited data with high accuracy. Applying FBS as a binary filter after DSBF improves robustness for multiple sound source localization under robotic movement. In addition, a moving sound source separation method is shown by using segments of the DSBF enhanced signal derived from the localization process.     

Motion Planning Strategy for Finding an Object with a Mobile Manipulator in Three-Dimensional Environments

In this paper, we investigate the problem of minimizing the average time required to find an object in a known three-dimensional environment. We consider a 7-d.o.f. mobile manipulator with an 'eye-in-hand' sensor. In particular, we address the problem of searching for an object whose unknown location is characterized by a known probability density function. We present a discrete formulation, in which we use a visibility-based decomposition of the environment. We introduce a sample-based convex cover to estimate the size and shape of visibility regions in three dimensions. The resulting convex regions are exploited to generate trajectories that make a compromise between moving the manipulator base and moving the robotic arm. We also propose a practical method to approximate the visibility region in three dimensions of a sensor limited in both range and field of view. The quality and success of the generated paths depend significantly on the sensing robot capabilities. In this paper, we generate searching plans for a mobile manipulator equipped with a sensor limited in both field of view and range. We have implemented the algorithm and present simulation results.     

A Localization Algorithm for Autonomous Mobile Robots via a Fuzzy Tuned Extended Kalman Filter

The capability to acquire the position and orientation of an autonomous mobile robot is an important element for achieving specific tasks requiring autonomous exploration of the workplace. In this paper, we present a localization method that is based on a fuzzy tuned extended Kalman filter (FT-EKF) without a priori knowledge of the state noise model. The proposed algorithm is employed in a mobile robot equipped with 16 Polaroid sonar sensors and tested in a structured indoor environment. The state noise model is estimated and adapted by a fuzzy rule-based scheme. The proposed algorithm is compared with other EKF localization methods through simulations and experiments. The simulation and experimental studies demonstrate the improved performance of the proposed FT-EKF localization method over those using the conventional EKF algorithm.     

A Method for Accurate Map Construction Using Time Registration from a Moving SOKUIKI Sensor

Mobile robots have various sensors that are considered as their eyes and a two-dimensional laser scanner (SOKUIKI sensor) is one of them. This sensor is commonly used for constructing environment maps or detecting obstacles. It is very important to estimate accurately the sensor's position and direction when scan data is obtained in order to use correctly such a sensor for these purposes. On the other hand, scanning sensors have an essencial problem: time lag. That is, the time when the laser is transmitted to each direction is different. Thus the map has many errors if the measurement time lag problem is not resolved. This paper presents a method for laser scan data synchronization using time registration and, as a practical application, we show its effectiveness for accurate map construction.     

Improved Pose Estimation for Mobile Robots by Fusion of Odometry Data and Environment Map

This paper proposes a new approach for calibration of dead reckoning process. Using the well-known UMBmark (University of Michigan Benchmark) is not sufficient for a desirable calibration of dead reckoning. Besides, existing calibration methods, usually require explicit measurement of actual motion of the robot. Some recent methods, use the smart encoder trailer or long range finder sensors such as ultrasonic or laser range finders for automatic calibration. Manual measurement is necessary in the case of the robots that are not equipped with long range detectors or such smart encoder trailer. Our proposed approach, uses an environment map that is created by fusion of proximity data, in order to calibrate the odometry error automatically. In the new approach, the systematic part of the error is adaptively estimated and compensated by an efficient and incremental maximum likelihood algorithm. Actually, environment map data are fused with the odometry and current sensory data in order to acquire the maximum likelihood estimation. The advantages of the proposed approach are demonstrated in some experiments with Khepera robot. It is shown that the amount of pose estimation error is reduced by a percentage of more than 80%.     

Construction of a Gait Adaptation Model in Human Split-Belt Treadmill Walking Using a Two-Dimensional Biped Robot

A number of studies have measured kinematics, dynamics and oxygen uptake while a person walks on a treadmill. In particular, during walking on a split-belt treadmill, in which the left and right belts have different speeds, remarkable differences in kinematics are observed between normal subjects and subjects with cerebellar disease. In order to construct a gait adaptation model of such human split-belt treadmill walking, we proposed a simple control model and developed a new two-dimensional biped robot walk on a split-belt treadmill. We combined the conventional limit-cycle-based control consisting of joint PD control, cyclic motion trajectory planning and a stepping reflex with a newly proposed adjustment of P-gain at the hip joint of the stance leg. The data obtained in experiments on the robot (normal subject model and cerebellum disease subject model) have highly similar ratios and patterns to data obtained in experiments on normal subjects and subjects with cerebellar disease carried out by Bastian et al. We also showed that the P-gain at the hip joint of the stance leg was the control parameter of adaptation for symmetric gaits in split-belt walking and that P-gain adjustment corresponded to muscle stiffness adjustment by the cerebellum. Consequently, we successfully proposed a gait adaptation model for human split-belt treadmill walking, and confirmed the validity of our hypotheses and the proposed model using the biped robot.     

Integration Strategies Using a Modular Architecture for Mobile Robots in the Rehabilitation Field

This paper describes an integration strategy based upon a modular architecture which is meant to improve access to assistive technical devices in the rehabilitation field. This system concept is now known as M3S: Multiple Master Multiple Slave. With M3S, it is possible to connect input devices (like joysticks and keyboards) to end-effectors (like wheelchairs, robots and infra-red remote controllers) to form an integral aid which offers disabled people better opportunities to function as independently as possible. Since M3S is based upon a modular architecture, it allows users (disabled people, attendants, therapists) to compile a specific package of any combination of technical aids to a complete integral system, while still permitting them to extend or modify the system later on. Furthermore the system can be used right-away without any special adaptations using the M3S plug-and-play capabilities. The power of such an integrated system have been shown in several user evaluations in various countries around Europe. The M3S specification is an open standard available for free, M3S has also been proposed to the ISO for formal standardization. For the development of M3S devices a complete set of software tools is available at no cost, hardware starter kits are available for a small fee. Information about M3S can be acquired from the M3S web server (http://www.tno.nl/m3s) or directly from the M3S Dissemination office.     

Development of A Behavior‐Based Cooperative Search Strategy for Distributed Autonomous Mobile Robots Using Zigbee Wireless Sensor Network

To achieve efficient and objective search tasks in an unknown environment, a cooperative search strategy for distributed autonomous mobile robots is developed using a behavior‐based control framework with individual and group behaviors. The sensing information of each mobile robot activates the individual behaviors to facilitate autonomous search tasks to avoid obstacles. An 802.15.4 ZigBee wireless sensor network then activates the group behaviors that enable cooperative search among the mobile robots. An unknown environment is dynamically divided into several sub‐areas according to the locations and sensing data of the autonomous mobile robots. The group behaviors then enable the distributed autonomous mobile robots to scatter and move in the search environment. The developed cooperative search strategy successfully reduces the search time within the test environments by 22.67% (simulation results) and 31.15% (experimental results).