Research robots made great strides in autonomous indoor navigation between the 1990s and the 2000s. Currently, a number of ready-made research bases have the sensing, mobility, and computational power necessary for such autonomy: The Pioneer, PatrolBot, PowerBot, and PeopleBot. These platforms can map buildings and navigate out-of-the-box, using SLAM and a variation on Monte Carlo method/Markov localization and modified value-iterated search navigation techniques, with any sensor of the 2-D range-finder class. This method creates a human readable map of the robot's workspace that can be used to control and track robots of this type as they move. Evolution Robotics offers single-camera VSLAM software, which replaces range-finding with visual pattern-matching, but this system cannot create a human readable map with which to monitor robots' position. Other groups are building stereocam-based VSLAM. Because the stereo camera provides range-finding data using the disparity between the lenses, maps can be made and robots tracked. The K-Team Khepera, Segway-based platforms and other research robots can link to external computing resources to use such software.

The precision of any of these methods depends upon the precision of the sensor, the granularity of data tracked and the speed of calculation. Range-finding lasers may have +/-1 cm accuracy while digital stereo camera accuracy is limited to a quarter pixel and thus is highly range-dependent. Vision-based systems require more computational resources than simple range-finding systems such as lasers, but may do the computation on a digital signal processor embedded with the camera. Because of cost and precision trade-offs, less expensive vision-based systems tend to be used on consumer robots while commercial and industrial robots and automated guided vehicles (AGVs) tend to use laser-based systems.

Outdoors, localization is primarily handled with GPS, however, satellite signals can frequently be lost due to weather, trees, buildings or other obstructions. When the signal is lost, the robot typically navigates using dead reckoning and inertial motion tracking. Dead reckoning relies on relative wheel motion and is highly subject to cumulative slippage errors. Inertial motion tracking uses rate gyroscopes and accelerometers to determine actual motion of the platform. The accuracy of inertial motion tracking depends upon the quality and calibration of the sensors employed. The Segway RMP 400 and Seekur robots are two of the few research platforms designed for such research; most other outdoor research robots are jerry-rigged by researchers from existing vehicles.

In constrained areas, some robots, such as the John Deere Gator, simply surround the perimeter with radio beacons and use simple triangulation from three or more beacons to localize and navigate. Beacons are also used indoors by older AGVs in factories.

Autonomous Solutions is a leader in the field of outdoor navigation software; their system is used by John Deere tractors and by some military platforms.

Much research software for autonomous robots is Free Software or Open Source Software, including Carmen from Carnegie Mellon, Player/Stage/Gazebo from the University of Southern California and the ARIA API libraries[2] from MobileRobots Inc. There is also commercial software: Webots has been continuously developed since 1998 and is currently used by more than 500 universities. It runs on Linux, Windows and Mac OS X.

More recently, in June 2006, Microsoft Research began offering free beta-test copies of a Robotics Studio software development kit with Pioneer robots in simulation for Windows XP in an attempt to counter Linux dominance onboard mobile robot platforms. An older platform, URBI with a Free Software SDK, is used in many universities. The plethora of autonomous mobile robots and software available for researchers has greatly sped the pace of development in the robotics field.


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