The challenges faced by the U.S. quick-service restaurant (QSR) sector are well documented. These include a shrinking labor pool, increased competition, rising operating costs, and an increasingly demanding customer base (Gregerson 2009). Beyond other quick-service chains, QSR outlets face competition from the fast casual segment and prepared foods in grocery stores and convenience stores (Dahlstrom et al. 2004). Compounding this, operating costs are rising while customers with limited discretionary income are demanding more in terms of speed, convenience quality, and value for money (National Restaurant Association 2009).Competition has always been a challenge, so QSR operators have maintained a steady interest in the application of technological solutions to maintain product quality and service consistency, while also keeping costs down through efficiencies in terms of speed of service, order processing, and labor and inventory management (Bickers 2005;Gregerson 2009;Minnick 2007a). The many now-familiar applications of automation and technology include pointof-sale systems, self-service kiosks, order confirmation systems, call center technology, automatic drink dispensers and cooking equipment, and kitchen management systems (Minnick 2007b). Also familiar are the benefits of applying technology in production management and resource planning, including cost minimization, revenue management, and human resource support (e.g., Ansel and Dyer 1999;Kimes 2008;Puzo and Dulen 2008). In this article, we explain the possibilities of using robotics to yield further efficiencies for QSR operation-primarily through knowledge management.The definition of robotics is under continual revision and development. The technology base within robotics has expanded significantly to encompass new definitions of intelligence, thus refocusing robotics definitions towards the software that enables such intelligence. Robotic software systems are now being developed to extend human AbstractGiven the twin objectives of ensuring consistent quality and controlling costs, quick-service restaurants have been in the forefront of automating their operations whenever possible. This article explores a novel approach to achieving these two objectives by using modern robotics technologies to improve operations. Instead of applying robotics technology for direct labor replacement, robotics can augment workers' cognitive capacity. This alternative application of robotics technologies encompasses two key components: (1) robotic sensing for demand prediction and (2) robotic planning for production management. Using the example of Zaxby's Franchising Inc., this article explains the improvements possible with modern robotics technologies and the challenges of implementing it. Unusual for quick-service, Zaxby's uses a fresh, cook-to-order concept, which resulted in backlogs during busy times. Zaxby's robotics application substantially reduced both service times and food waste. The system tracks customer arrivals, starts the cooking process as customers arri...
Autonomous Cross-Country Navigation requires planning algorithms which supports rapid traversal of challenging terrain while maintaining vehicle safety. The central theme of the work is the implementation of a planning system which solves this problem. The planning system uses a recursive trajectory generation algorithm, which generates spatial trajectories and then heuristically modifies them to achieve safe paths around obstacles. Velocities along the spatial trajectory are then set to insure a dynamically stable traversal. Ongoing results are presented from the system as implemented on the NAVLAB II, an autonomous off-road vehicle at Carnegie Mellon University. The planning system was successful in achieving 5.1km autonomous test runs with obstacle avoidance on rugged natural terrain at speeds averaging 1.8 mIs. Runs of up to 0.3km at 4.5m/s were acheived when only checking for obstacles
Autonomous cross-country navigation is essential for outdoor robots moving about in unstructured environments.Most existing systems use range sensors to determine the shape of the terrain, plan a trajectory that avoids obstacles, and then drive the trajectory Performance has been limited by the range and accuracy of sensors, insufficient vehicle-terrain interaction models, and the availability of high-speed computers. As these elements improve, higher-speed navigation on rougher terrain becomes possible. We have developed a software system for autonomous navigation that provides for greater capability. The perception system supports a large braking distance by fusing multiple range images to build a map of the terrain in front of the vehicle. The system identifies range shadows and interpolates undersampled regions to account for rough terrain effects. The motion planner reduces computational complexity by investigating a minimum number of trajectories. Speeds along the trajectory are set to provide for dynamic stabi1ity The entire system was tested in simulation, and a subset of the capability was demonstrated on a real vehicle. Results to date include a continuous 5.1 kilometer run across moderate terrain with obstacles. This paper begins with the applications, prior work, limitations, and current paradigms for autonomous cross-country navigation, and then describes our contribution to the area.In this paper, we define autonomous cross-country navigation to be the safe traverse of an unmanned vehicle across unstructured terresthal or planetary terrain with little or no human intervention. The purpose of navigation can be the transport of cargo or personnel, the deployment of sensors for measuring the environment, or the delivery and support of tools for engaging the environment. This technology has broad application in a number of scenarios. The first class of applications involves navigation in inaccessible or isolated environments, such as the exploration of Mars. In such environments, the cost and safety issues prohibit on-site human operation. Remote operation is not viable due to long communication delays, and autonomy is essential. Proficiency can be low provided the system is robust. The second class involves navigation in hazardous environments, such as toxic waste site characterization and remediation, search and rescue, reconflaissance, and mining. In these environments, the possibilities for on-site human operation range from undesirable to prohibitive due to safety issues. Remote operation of the vehicle is possible but performance may suffer due to limited sensory feedback to the operator. Robustness is essential, and autonomy is preferred if the system's proficiency exceeds that of the remote operator. The third class involves navigation in commercial environments, such as excavation and construction. In these environments, the on-site human operators are generally highly skilled. Remote operation is of little use, and proficiency and robustness are of utmost importance. Autonomy is viable only if...
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