Land-based waypoint navigation usually requires accurate position information to effectively function in either natural or man-made terrain. Most systems solve this problem by using differential GPS and/or high-quality, expensive inertial navigation systems. In an effort to make waypoint navigation available to smaller tactical platforms, a tightly packaged, portable and inexpensive waypoint navigation system was developed. This system was implemented on the Man Portable Robotic System (MPRS) Urban Robot (URBOT) 1. The package uses inexpensive sensors and a combination of standard Kalman Filter and waypoint following techniques along with some novel approaches to compensate for the deficiencies of the GPS and gyroscope sensors. The algorithms run on a low-cost embedded processor. A control unit was also developed that allows the operator to specify path waypoints on ortho-rectified aerial photographs. 1. Background The goal of this project was to develop a robust waypoint navigation capability for a small mobile robot that did not rely on the availability of differential GPS. Here waypoint navigation is defined as the process of automatically following a predetermined path defined by a set of geodetic coordinates. The requirement for using non-differential Report Documentation Page Form Approved OMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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The Mobile Detection Assessment Response System (MDARS) is a joint Army-Navy development effort to field mobile robots at Department of Defense (DoD) sites for physical security and automated inventory missions. MDARS was initiated in 1989 to improve the effectiveness of a shrinking guard force, but was quickly expanded to address the intensive manpower requirements associated with accounting for high-dollar and critical DoD assets. Two types of autonomous platforms patrol inside warehouses (Interior) and outside of storage facilities (Exterior), carrying payloads for intruder detection, inventory assessment, and barrier assessment. The MDARS console for command and control is based upon the Multiple Resource Host Architecture (MRHA), which allows a single human guard to oversee and monitor up to 255 platforms and/or unmanned sensors.Recent improvements to satisfy mission requirements for physical security have expanded the system capabilities to enable force-protection missions in tactical situations. Rapid-prototyping approaches have facilitated investigations into aiming and firing less-than-lethal weapons on an unmanned platform, deployment of a marsupial capability to carry smaller robots, and seamless alldigital communication between unmanned sensors and unmanned ground and air vehicles. This paper provides an overview of the MDARS evolutionary development approach (using mobile robots and fixed sensors) for both physical security and force protection missions. Special treatment is provided on feedback from developmental tests at Aberdeen Proving Grounds, MD, and operational tests at Defense Distribution Depot Susquehanna PA. Report Documentation Page Form Approved OMB No. 0704-0188Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

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Any mobile robot which must operate in a dynamically changing indoor environment, such as an office, laboratory, or warehouse, must be able to detect and successfully avoid unexpected obstacles. Transient objects such as chairs, doors, trash cans, etc. change position or state frequently, and thus cannot be assigned a static representation in an "absolute" X -Y planview map of the workspace. The most simplistic path planning scheme therefore assumes there are no transient objects in this global model for the initial "find-path" operation. For collision avoidance purposes, a secondary "relative" model of the robot's immediate surroundings is created from real world sensor data collected as the robot is moving, and used to find a path around each individual obstruction as it is encountered. No information regarding the position of permanent objects is available in this smaller relative model, and the position of each transient object is forgotten as soon as it no longer obstructs the path. Conversely, if the absolute position of each detected obstruction is simply recorded in the global map, the resulting model eventually fills up with clutter and the find -path operation fails because no free path exists. This paper discusses a robust approach for map maintenance implemented on a prototype security robot, wherein transient objects are added to the global map as they are encountered, and removed from the model later if no longer detected at the same location. In this manner, subsequent find -path operations will avoid previously identified obstructions, and information on the location of both permanent as well as transient objects is available when reacting to the discovery of a new obstruction.