Position estimation of mobile robots with internal and external sensors using uncertainty evolution technique
13 May 1990-pp 2011-2016
TL;DR: A more general positioning method whereby the robot can act autonomously and move along a path unlimited by positioning, which uses not only the estimated position data but also the accuracy of the dead reckoning and the external sensor effectively.
Abstract: A wheeled mobile robot on a flat plane can estimate its current location and orientation from the cumulative wheel rotation (dead reckoning). The method, however, has the drawback that estimation errors are accumulated as the robot moves, and the accuracy of estimation decreases. To address this problem, a positioning technique using an external sensor is needed. The authors propose a more general positioning method whereby the robot can act autonomously and move along a path unlimited by positioning. The robot can modify the position estimated by dead reckoning by repeating low-level sensing asynchronously and intermittently. This method uses not only the estimated position data but also the accuracy of the dead reckoning and the external sensor effectively. >
Citations
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01 Apr 1997
TL;DR: An efficient method for localizing a mobile robot in an environment with landmarks in which the robot can identify these landmarks and measure their bearings relative to each other is described.
Abstract: We describe an efficient method for localizing a mobile robot in an environment with landmarks. We assume that the robot can identify these landmarks and measure their bearings relative to each other. Given such noisy input, the algorithm estimates the robot's position and orientation with respect to the map of the environment. The algorithm makes efficient use of our representation of the landmarks by complex numbers. The algorithm runs in time linear in the number of landmarks. We present results of simulations and propose how to use our method for robot navigation.
579 citations
Cites methods from "Position estimation of mobile robot..."
...Many authors use a statistical approach for robot navi- gation, for example, Chatila and Laumond [14], Smith and Cheeseman [15], Kosaka and Kak [16], Crowley [17], Leonard and Durrant-Whyte [18], Watanabe and Yuta [19], Burlina, DeMenthon and Davis [20], and Matthies and Shafer [21]....
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...[19] Y. Watanabe and S. Yuta, “Position estimation of mobile robots with internal and external sensors using uncertainty evolution technique,” in Proc....
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...Many authors use a statistical approach for robot navigation, for example, Chatila and Laumond [14], Smith and Cheeseman [15], Kosaka and Kak [16], Crowley [17], Leonard and Durrant-Whyte [18], Watanabe and Yuta [19], Burlina, DeMenthon and Davis [20], and Matthies and Shafer [21]....
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Patent•
12 Jun 2002TL;DR: In this article, a behavior-based control system for a mobile robot is provided to effectively cover a given area by operating in a plurality of modes, including an obstacle following mode and a random bounce mode.
Abstract: A control system for a mobile robot ( 10 ) is provided to effectively cover a given area by operating in a plurality of modes, including an obstacle following mode ( 51 ) and a random bounce mode ( 49 ). In other embodiments, spot coverage, such as spiraling ( 45 ), or other modes are also used to increase effectiveness. In addition, a behavior based architecture is used to implement the control system, and various escape behaviors are used to ensure full coverage.
380 citations
Patent•
06 Aug 2007TL;DR: In this article, an autonomous floor-cleaning robot comprising a housing infrastructure including a chassis, a power subsystem, and a self-adjusting cleaning head subsystem is presented, which includes a side brush assembly mounted in combination with the chassis and powered by the motive subsystem to entrain particulates outside the periphery of the housing infrastructure.
Abstract: An autonomous floor-cleaning robot comprising a housing infrastructure including a chassis, a power subsystem; for providing the energy to power the autonomous floor-cleaning robot, a motive subsystem operative to propel the autonomous floor-cleaning robot for cleaning operations, a command and control subsystem operative to control the autonomous floor-cleaning robot to effect cleaning operations, and a self-adjusting cleaning head subsystem that includes a deck mounted in pivotal combination with the chassis, a brush assembly mounted in combination with the deck and powered by the motive subsystem to sweep up particulates during cleaning operations, a vacuum assembly disposed in combination with the deck and powered by the motive subsystem to ingest particulates during cleaning operations, and a deck adjusting subassembly mounted in combination with the motive subsystem for the brush assembly, the deck, and the chassis that is automatically operative in response to an increase in brush torque in said brush assembly to pivot the deck with respect to said chassis. The autonomous floor-cleaning robot also includes a side brush assembly mounted in combination with the chassis and powered by the motive subsystem to entrain particulates outside the periphery of the housing infrastructure and to direct such particulates towards the self-adjusting cleaning head subsystem.
373 citations
Patent•
21 Feb 2006TL;DR: An autonomous floor cleaning robot includes a transport drive and control system arranged for autonomous movement of the robot over a floor for performing cleaning operations as discussed by the authors, where the robot chassis carries a first cleaning zone comprising cleaning elements arranged to suction loose particulates up from the cleaning surface and a second cleaning zone consisting of cleaning elements arraigned to apply a cleaning fluid onto the surface and to thereafter collect the cleaning fluid up after it has been used to clean the surface.
Abstract: An autonomous floor cleaning robot includes a transport drive and control system arranged for autonomous movement of the robot over a floor for performing cleaning operations. The robot chassis carries a first cleaning zone comprising cleaning elements arranged to suction loose particulates up from the cleaning surface and a second cleaning zone comprising cleaning elements arraigned to apply a cleaning fluid onto the surface and to thereafter collect the cleaning fluid up from the surface after it has been used to clean the surface. The robot chassis carries a supply of cleaning fluid and a waste container for storing waste materials collected up from the cleaning surface.
367 citations
Patent•
12 Sep 2003TL;DR: In this article, an autonomous cleaning apparatus includes a chassis, a drive system disposed on the chassis and operable to enable movement of the cleaning apparatus, and a controller in communication with the drive system.
Abstract: An autonomous cleaning apparatus includes a chassis, a drive system disposed on the chassis and operable to enable movement of the cleaning apparatus, and a controller in communication with the drive system. The controller includes a processor operable to control the drive system to steer movement of the cleaning apparatus. The autonomous cleaning apparatus includes a cleaning head system disposed on the chassis and a sensor system in communication with the controller. The sensor system includes a debris sensor for generating a debris signal, a bump sensor for generating a bump signal, and an obstacle following sensor disposed on a side of the autonomous cleaning apparatus for generating an obstacle signal. The processor executes a prioritized arbitration scheme to identify and implement one or more dominant behavioral modes based upon at least one signal received from the sensor system.
346 citations
References
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01 Jan 1987
TL;DR: The method presented can be generalized to six degrees offreedom and provides a practical means of mating the relationships among objects, as well as estimating the uncertainty associated with the relationships.
1,421 citations
TL;DR: In this paper, a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects is described. But this method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view.
Abstract: This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arising from diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those from an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, if not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.
1,419 citations
24 Apr 1988
TL;DR: The author analyzes the effect of measurement errors, wheel slippage, and noise on the accuracy of the estimated vehicle position obtained in this manner and derives the location estimator and its uncertainty covariance matrix.
Abstract: A motion controller for the autonomous mobile vehicle commands the robot's drive mechanism to keep the robot near its desired path at all times. In order for the controller to behave properly, the controller must know the robot's position at any given time. The controller uses the information provided by the optical encoders attached to the wheels to determine vehicle position. The author analyzes the effect of measurement errors, wheel slippage, and noise on the accuracy of the estimated vehicle position obtained in this manner. Specifically the location estimator and its uncertainty covariance matrix are derived. >
180 citations
04 Sep 1989
TL;DR: This paper offers a solution to the mobile robot navigation problem, which relies on the concept of a ugeneralized geometric beacon - a feature which can be reliably observed in successive sensor measurements (a beacon), andWhich can be described in terms of some small number of geometric objects.
Abstract: A continuing problem in mobile robotics is that of achieving reliable autonomous navigation based only on information obtained from the sensors of a mobile vehicle. The basic navigation problem based on the observation of navigation beacons has been studied extensively over many hundreds of years, and is in general well-understood. The application of these techniques in robotics has faltered on the problem of reliably extracting beacons from sensor data and utilizing them in automating the navigation process. In this paper we offer a solution to the mobile robot navigation problem, which relies on the concept of a ugeneralized geometric beaconn - a feature which can be reliably observed in successive sensor measurements (a beacon), and which can be described in terms of some small number of geometric objects. This navigation algorithm is based around a simple Kalman-filter which is employed to maintain a map of these observed geometric beacons, and into which new sensor measurements can be matched. We describe three different implementations of this navigation algorithm, the first on a vehicle with only one rotating sonar, the second on a vehicle with six static sonars, and the third on a vehicle equipped with both a sonar and an active infra-red sensor. These implementations demonstrate how different geometric beacons extracted from different sensors and algorithms can be used to provide a robust and reliable estimate of mobile robot location.
45 citations
04 Sep 1989
TL;DR: Combining data from several sensors and from a pre-stored model of the domain provides a way to enhance the reliability of a perception system by integrating range measurements into a geometric model ofThe local environment.
Abstract: Unfortunately, the sensing devices which are available for mobile robots often fail in a variety of circumstances. This is especially true of the less expensive devices such as ultrasonic and infrared range sensors. Combining data from several sensors and from a pre-stored model of the domain provides a way to enhance the reliability of a perception system. Such combination may be accomplished by integrating range measurements into a geometric model of the local environment.
24 citations