Navigation Method For Mobile Robot Research Articles

2P1-F13 全方向移動型オドメータの開発

A lot of navigation methods for omni-directional mobile robots are proposed. In this paper, a two-wheels caster type odometer that estimates the current position and orientation of the omni-directional mobile robot is developed for the navigation. The mechanism of the developed odometer is special, and its gear train is complex. The developed odometer has a multiple rotation axis. The multiple rotation axis is composed of two shafts and bearings, and the two shafts each are possible to rotate independently. The mechanism of the multiple rotation axis transmits the different rotation of the two shafts. An estimating experiment in the current position and orientation of the omni-directional mobile robot shows the effectiveness of the developed two-wheels caster type odometer.

Navigation Techniques for Mobile Robots in Greenhouses

This article discusses navigation methods for mobile agricultural robots in greenhouses. Today, many dangers of repetitive and hazardous agricultural tasks for humans could be avoided by using robots. The problem of autonomous navigation in greenhouses has been solved using both deliberative and pseudo-reactive techniques. The first one utilizes a map-based algorithm to create a safe, obstacle-free path to circulate throughout the greenhouse. The second technique applies a sensor-feedback algorithm to move through the greenhouse corridors. Furthermore, along the trajectory a sensorial map is built. Both approaches have been implemented and tested in a real environment, with promising results.

Real‐time deadlock‐free navigation for multiple mobile robots

AbstractThis paper proposes a practicable navigation method for multiple mobile robots in a realistic environment. In the past, many navigation methods have been developed. However, they were often limited to a single robot and sometimes assumed robots with special mobility such as holonomic ones. From the viewpoint of practical applications it is indispensable that the number of robots is arbitrary and a general shape and mobility for them is allowed.In this study, deadlock‐free navigation for nonholonomic mobile robots in a practical environment is given. In the proposed scheme, states of the environment and robots are fed back in real time, and global path planning is cyclically executed. This enables an adaptation to a changing environment and reliable, deadlock‐free navigation for multiple robots. The real‐time online path planning is performed by an efficient A* search in Configuration Spaces representing the robots and environment.The proposed method is tested in several simulations which represent typical complicated navigation situations. As a result, the effectiveness of the method is verified. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 27– 36, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20714

Navigation of mobile robot using location map of place cells and reinforcement learning

AbstractIt is known that the hippocampus of rats contains neural cells called “place cells.” Place cells are neural cells that respond selectively when the rat arrives at a particular place. This paper proposes a mobile robot navigation method that uses a place cell position map and reinforcement learning. First, the place cell position map is created by a neural gas using image data and position data from observation points. Next, routes between the place cells are established, and the path to the goal is learned using the actor– critic method, which is a type of reinforcement learning method. Numerical simulations demonstrate that the goal can be reached even if there are motion errors by moving the robot along the route. © 2007 Wiley Periodicals, Inc. Syst Comp Jpn, 38(7): 65– 75, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20632

複数台移動ロボットのためのデッドロックフリーナビゲーション法

This paper proposes a practicable navigation method for multiple mobile robots in a realistic environment. In the past, many navigation methods have been developed. However, they were often limited to a single robot and sometimes assumed robots with special mobility such as holonomic ones. From the viewpoint of practical applications it is indispensable that the number of robots is arbitrary and a general shape and mobility for them is allowed. In this study, deadlock-free navigation for nonholonomic mobile robots in a practical environment is given. In the proposed scheme, states of the environment and robots are fed back in real time, and global path planning is cyclically executed. This enables an adaptation to a changing environment and reliable, deadlock-free navigation for multiple robots. The real-time online path planning is performed by an efficient A* search in Configuration Spaces representing the robots and environment. The proposed method is tested in several simulations which represent typical complicated navigation situations. As a result, the effectiveness of the method is verified. © 2008 Wiley Periodicals, Inc. Electr Eng Jpn, 163(3): 27– 36, 2008; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20714

Wheeled mobile robot navigation using proportional navigation

We present a method for wheeled mobile robot navigation based on the proportional navigation law. This method integrates the robot's kinematics equations and geometric rules. According to the control strategy, the robot's angular velocity is proportional to the rate of turn of the angle of the line of sight that joins the robot and the goal. We derive a relative kinematics system which models the navigation problem of the robot in polar coordinates. The kinematics model captures the robot path as a function of the control law parameters. It turns out that different paths are obtained for different control parameters. Since the control parameters are real, the number of possible paths is infinite. Results concerning the navigation using our control law are rigorously proven. An extensive simulation confirms our theoretical results.

A new space and time sensor fusion method for mobile robot navigation

AbstractTo fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc.

Motion control for mobile robot obstacle avoidance and navigation: a fuzzy logic-based approach

One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.

A lane based obstacle avoidance method for mobile robot navigation

This paper presents a new local obstacle avoidance method for indoor mobile robots. The method uses a new directional approach called the Lane Method. The Lane Method is combined with a velocity space method i.e., the Curvature-Velocity Method to form the Lane-Curvature Method(LCM). The Lane Method divides the work area into lanes, and then chooses the best lane to follow to optimize travel along a desired goal heading. A local heading is then calculated for entering and following the best lane, and CVM uses this local heading to determine the optimal translational and rotational velocities, considering some physical limitations and environmental constraint. By combining both the directional and velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the physical limitations of the robot motion into account.

A real-time limit-cycle navigation method for fast mobile robots and its application to robot soccer

A mobile robot should be designed to navigate with collision avoidance capability in the real world, flexibly coping with the changing environment. In this paper, a novel limit-cycle navigation method is proposed for a fast mobile robot using the limit-cycle characteristics of a 2nd-order nonlinear function. It can be applied to the robot operating in a dynamically changing environment, such as in a robot soccer system. By adjusting the radius of the motion circle and the direction of obstacle avoidance, the navigation method proposed enables a robot to maneuver smoothly towards any desired destination. Simulations and real experiments using a robot soccer system demonstrate the merits and practical applicability of the proposed method.

1A1-G03 交差点における速さを考慮した自律移動ロボットの誘導法

従来の自律移動ロボットの誘導に関する研究は, 自律移動ロボットを等速で誘導しているものが多い。そのため自律移動ロボットの運動の制限や移動する環境によっては, 環境を認識できたとしても壁に接触する場合や目標軌道上を走行できない場合が生じている。そこで本論文では, 自律移動ロボットを減速させることにより, 等速では誘導することができなかった交差点でも自律移動ロボットを適切に誘導する手法を提案する。さらに, 提案した手法により, 自律移動ロボットを適切に誘導することができることをシミュンーションにより確認した。

Evolutionary programming-based univector field navigation method for past mobile robots

Most of navigation techniques with obstacle avoidance do not consider the robot orientation at the target position. These techniques deal with the robot position only and are independent of its orientation and velocity. To solve these problems this paper proposes a novel univector field method for fast mobile robot navigation which introduces a normalized two dimensional vector field. The method provides fast moving robots with the desired posture at the target position and obstacle avoidance. To obtain the sub-optimal vector field, a function approximator is used and trained by evolutionary programming. Two kinds of vector fields are trained, one for the final posture acquisition and the other for obstacle avoidance. Computer simulations and real experiments are carried out for a fast moving mobile robot to demonstrate the effectiveness of the proposed scheme.

Qualitative navigation for mobile robots in indoor environments

This article describes a novel qualitative navigation method for mobile robots in indoor environments. The approach is based on qualitative representations of variations in sensor behavior between adjacent regions in space. These representations are used to localize and guide planning and reaction. Off-line, the system accepts as input a line-based diagram of the environment and generates a map based on a simple qualitative model of sensor behavior. During execution, the robot controller integrates this map into a reaction module. This architecture has been tested both in simulation and on a real mobile robot. Results from both trials are provided.

On the Technology of Acoustic Utility. Acoustic Information and Energy. Navigation System for an Indoor Mobile Robot by Active Ultrasonic Beacons.

The navigation method of an indoor mobile robot by active ultrasonic beacons is proposed. 2 receivers on a robot measures the time-of-flight of the ultrasonic pulses from 2 beacons on a ceiling to estimate the robot's 2 D position. This is reliable and safe localization system which can be used in human-robot coexisting environment. Since the distances from only 1 beacon can be detected at once, position should be estimated with robot's movement in a measurement interval during navigation. A moving robot should select the pair of beacons used for position estimation among many available beacons. Detection of wrong position sometimes occurs due to the interruption of ultra-sonic pulses by obstacles and humans in the real environment. For solving these problems, the dynamic position estimation, the selection of beacon pair according to position accuracy, and reliable navigation method with dead-reckoning in the real environment are discussed.

A mobile robot employing insect strategies for navigation

The ability to navigate in a complex environment is crucial for both animals and robots. Many animals use a combination of different strategies to return to significant locations in their environment. For example, the desert ant Cataglyphis is able to explore its desert habitat for hundreds of meters while foraging and return back to its nest precisely and on a straight line. The three main strategies that Cataglyphis is using to accomplish this task are path integration, visual piloting and systematic search. In this study, we use a synthetic methodology to gain additional insights into the navigation behavior of Cataglyphis. Inspired by the insect’s navigation system we have developed mechanisms for path integration and visual piloting that were successfully employed on the mobile robot Sahabot 2. On the one hand, the results obtained from these experiments provide support for the underlying biological models. On the other hand, by taking the parsimonious navigation strategies of insects as a guideline, computationally cheap navigation methods for mobile robots are derived from the insights gained in the experiments.

Learning both a World Model and Navigation Paths in an Unknown Environment

This paper presents a new method for mobile robot navigation in an un- known world. The parti-game learning approach is used to construct a world model, and to learn to navigate from a starting position to a known goal region. The navigation architecture then integrates a new approach, based on the application of the Fuzzy ART neural architec- ture, for on-line map building from actual sensor data. This leads to an improved world model, which is then used to introduce a predictive on-line trajectory ltering method resulting in a new and more eectiv e navigation approach. It is assumed that the robot knows its own current world location, is able to perform sensor-based obstacle detection (not avoidance), and straight-line motions. Simulation and real-robot results obtained with a Nomad 200 mobile robot will be presented demonstrating the eectiv eness of the discussed methods. Quanti- tative results will demonstrate (1) the exploration and planning improvements of the new navigation approach, and (2) that the constructed world model (original or improved) is gen- eral purpose in the sense that its usefulness is not restricted to be used on learning a particular path, but is valuable for learning paths with dieren t (Start,Goal) pairs.

A Method of Piloting an Autonomous Mobile Robot in Dynamically Changing Environment Including Moving Obstacles

In this paper, we present a sensor-based navigation method for an autonomous mobile robot in a dynamically changing environment with stationary and moving obstacles. A navigation task needs to navigate a robot to a destination along a predefined path, and to avoid obstacles, if necessary. However, the environment is always changing, and sensory information is quite limited (actually, we have many limitations on sensory processing in connection with a dynamically changing environment). We have to describe an efficient navigation manner onto the robot using simple sensory information. The first step in our approach was to use fuzzy control. Based on results of experiments, we propose a new navigation method for an autonomous mobile robot in a dynamically changing environment. The method determines a steering angle and a velocity to be sent to a low-level locomotion system. Two major parts are involved in this operation: one is the task of tracing a planned path; and the other is the task of avoiding obstacles. The distribution weight between their outputs is determined with a range data and differential range data between samplings.

Global localization for mobile robots by multiple hypothesis tracking

Abstract The paper presents a long-term localization method for autonomous mobile robot navigation in a known environment. The method uses observations from an ultrasonic range finder but is more global than standard incremental localization based on EKF. A probabilistic sensor fusion technique (Kalman filtering) is used to obtain local robot position update (local localization). Multiple hypothesis tracking is used to manage global localization, i.e. to determine the source of an observed signal or to point out an approximate robot position to initiate local position estimation. Results of computer simulation are presented.

A fuzzy logic based extension to Payton and Rosenblatt's command fusion method for mobile robot navigation

Payton and Rosenblatt (1990) have proposed a command fusion method for combining outputs of multiple behaviors in a mobile robot navigation system such that information loss due to command fusion can be reduced. Using linguistic fuzzy rules to explicitly capture heuristics implicit in the Payton-Rosenblatt approach, we have extended their approach to a fuzzy logic architecture for mobile robot navigation in dynamic environments, which is simpler and easier to understand and modify. We have also developed and empirically tested a new defuzzification technique for alleviating difficulties in applying existing defuzzification methods to mobile robot navigation control.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

An Efficient Navigation Method for Mobile Robot Considering Uncertainties of Dead Reckoning and Vision.

This paper describes an method to efficiently navigate a vision guided mobile robot with an odometry system. First, the vehicle is driven by a human through a corridor. During this movement, a vision system extracts features such as doors or staircases. The categories and locations of the features are recorded in a map. This location data includes uncertainties of dead reckoning and vision. Then, given a destination, the vehicle examines the map and selects a sequence of useful landmarks so that the vehicle can arrive at the destination in the minimum time. In this selection, the vehicle considers the uncertainty of the map and that of dead reckoning. The vehicle passes through unselected features with dead reckoning and then, verifies the selected landmark by vision. Experiments are performed for our autonomous vehicle which moves in our building.