Odometry, also referred to as dead reckoning, is one of the least expensive and most widely used methods for mobile robot localization. However, mobile robots implementing dead reckoning are plagued with inaccuracy caused by systematic and non-systematic errors. In many cases, the most dominant source of inaccuracy is systematic errors. Systematic errors are caused by differences between the nominal and the actual dimensions of vehicle parameters (such as wheel radius and wheelbase measurements). Because systematic errors are inherent to the vehicle, the dead reckoning inaccuracy grows unbounded. Fortunately, it is possible to largely eliminate systematic errors by calibrating the parameters such that the differences between the nominal dimensions and the actual dimensions are minimized. This work presents a method for calibration of mobile robot parameters using an optimization engine. A cost function is developed based on the UMBmark (University of Michigan Benchmark) test pattern. This method is presented as a simple time efficient calibration tool for use during startup procedures of a differentially driven mobile robot. Comparisons are made between this method and an analytical calibration method developed at the University of Michigan. Results show that this tool consistently gives greater than 50% improvement in overall dead reckoning accuracy on an outdoor mobile robot, with respect to itself prior to calibration.
KEYWORDS: Algorithm development, Mobile robots, Sensors, Free space, Global Positioning System, Cameras, Space robots, Data acquisition, Tolerancing, Raster graphics
In autonomous mobile robot applications, local obstacle avoidance methods are frequently coupled with an initialized global map in order to achieve a complete navigation scheme. Unfortunately, global map information is often unavailable for global map initialization. In these situations, the designer must develop a navigation scheme such that the mobile robot either doesn't need a global map to achieve its goal or it builds the global map autonomously. This work presents a simple algorithm for developing a quasi-free space global map. The algorithm is based on the premise that the robot will be given multiple attempts at a particular goal. During early attempts, the mobile robot explores using solely local obstacle avoidance. While exploring, the robot records where it has been and uses this information on subsequent attempts. Further, this paper also outlines the look-ahead method by which the global map is implemented. Finally, both simulated and experimental results are presented. Testing of this algorithm was performed on an outdoor mobile robot, known as Navigator, which was developed at Virginia Tech.
A differentially steered three-wheeled vehicle has proven to be an effective platform for outdoor navigation. Many applications for this vehicle configuration, including planetary exploration and landmine/UXO location, require accurate localization. In spite of known problems, odometry, also called dead reckoning, remains one of the least expensive and most popular methods for localization. This paper presents the results of an investigation into the benefits of instrumenting the rear caster wheel to supplement the drive wheel encoders in odometry. A linear observer is used to fuse the data between the drive wheel encoders and the caster data. This method can also be extended using the standard form of the Kalman filter to allow for noise. Improvements in position estimation in the face of common problems such as slip and dimensional errors are quantified.
Virginia Tech has developed a novel two-wheeled vehicle termed the biplanar bicycle. This vehicle differs from the traditional bicycle in that both wheels rotate about a common axis, thus orienting the wheels side by side instead of front to back. Drive motors generate the propulsive torque by acting against a central reaction mass (the vehicle body) suspended from the axle. This vehicle offers several advantages over traditional wheeled vehicles. Among these are inherent stability, self-righting ability, and excellent mobility in two degrees of freedom (the ground plane). The physical architecture makes the vehicle exceedingly nimble through a wide range of speeds. Further, the entire mass of this vehicle is distributed between the two drive wheels, thus maximizing the available frictional driving force. The vehicle dynamics resulting from the system geometry affords very low slip and good acceleration. These advantages, coupled with a simple mechanical design, make the biplanar bicycle a good candidate for use as a platform vehicle in autonomous applications. Some potential applications include rail line inspection, landmine/UXO detection, and planetary exploration. This paper presents results from prototype testing of the biplanar bicycle. Various issues concerning sensors and control strategies unique to this vehicle are examined. While the conceptual basis of this vehicle may be counterintuitive, the design is fundamentally sound and viable for autonomous vehicle applications.
Virginia Tech is currently developing a new autonomous vehicle as a research platform. This vehicle is being used to investigate techniques in autonomous landmine/UXO detection. In addition, it was entered in the 2000 Intelligent Ground Vehicle Competition. This vehicle senses its surroundings using two (non-stereo) color CCD cameras, a SICK laser range finder, and wheel encoders. The cameras give a color representation of the area in front of the vehicle; while the laser range finder provides range data for obstacles in a 180-degree arc in front of the vehicle. Encoder feedback is used to determine position and velocity of the vehicle. This paper presents the techniques used to fuse this diverse and asynchronous data into a useful representation. The software architecture, which allows the various sensor fusion methods to be tested in a modular fashion, is also presented, along with the results from field-testing.
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