The detection and tracking of moving objects is an essential task in robotics. The CMU‐RI Navlab group has developed such a system that uses a laser scanner as its primary sensor. We will describe our algorithm and its use in several applications. Our system worked successfully on indoor and outdoor platforms and with several different kinds and configurations of two‐dimensional and three‐dimensional laser scanners. The applications vary from collision warning systems, people classification, observing human tracks, and input to a dynamic planner. Several of these systems were evaluated in live field tests and shown to be robust and reliable. © 2012 Wiley Periodicals, Inc.

The Navlab group at Carnegie Mellon University has a long history of development of automated vehicles and intelligent systems for driver assistance. The earlier work of the group concentrated on road following, cross-country driving, and obstacle detection. The new focus is on short-range sensing, to look all around the vehicle for safe driving. The current system uses video sensing, laser rangefinders, a novel light-stripe rangefinder, software to process each sensor individually, a map-based fusion system, and a probability based predictive model. The complete system has been demonstrated on the Navlab 11 vehicle for monitoring the environment of a vehicle driving through a cluttered urban environment, detecting and tracking fixed objects, moving objects, pedestrians, curbs, and roads. Robotics Technology and Intelligent VehiclesDuring the decade of the 1990's, researchers from automotive engineering, from robotics, and from allied fields came together to start building intelligent vehicles [1,2,3,4]. These vehicles use sensors, actuators, processors, and communications systems to either drive automatically, or to monitor a human driver and assist in navigation or warn in case of a developing dangerous situation.The field was driven by several market demand factors. First is an increasing interest in safety. While the accident rate has decreased steadily for 40 years, the total miles traveled have increased, with the net result that the number of road fatalities in the US has stayed approximately constant, averaging 40,000 / year [5]. Furthermore, there is an increasing sense that vehicles have become safer (air bags, anti-lock brakes, crumple zones); roadways have become safer (the Interstate highway system); societal pressures have become stronger (mandatory child safety seats, serious enforcement of drunk driving laws) but the drivers themselves have not necessarily improved. Currently, in the US, 70% of all crashes are primarily caused by the drivers, and in another 20% driver error is a major contributing factor [5,6].Beyond safety, the next major driving factors have to do with congestion and pollution. In the US, traffic continues to grow at 4% / year, while with the completion of the Interstate highway system, major new road construction greatly lags traffic growth. Computercontrolled vehicles promise to smooth out traffic flow, permitting more vehicles to travel in

A promising approach to enabling the rapid deployment and reconfiguration of automated assembly systems is to make use of cooperating, modular, robust robotic agents. Over the past 5 years, the authors have been constructing just such a system suitable for assembly of high-precision, high-value products. Within this environment, each robotic agent executes its own program, coordinating its activity with that of its peers to produce globally cooperative precision behavior. To simplify the problems associated with deploying such systems, each agent adheres to a strict notion of modularity, both physically and computationally. The intent is to provide an architecture within which it is straightforward to specify strategies for the robust execution of potentially complex and fragile cooperative behaviors. The underlying behaviors use a runtime environment that includes tools to automatically sequence the activities of an agent. Taken together, these abstractions enable a designer to rapidly and effectively describe the high-level behavior of a collection of agents while relying on a set of formally correct control strategies to properly execute and sequence the necessary continuous behaviors.

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