Situational reasoning for road driving in an urban environment

Robot navigation in urban environments requires situational reasoning. Given the complexity of the environment and the behavior specified by traffic rules, it is necessary to recognize the current situation to impose the correct traffic rules. In an attempt to manage the complexity of the situational reasoning subsystem, this paper describes a finite state machine model to govern the situational reasoning process. The logic state machine and its interaction with the planning system are discussed. The approach was implemented on Alice, Team Caltech’s entry into the 2007 DARPA Urban Challenge. Results from the qualifying rounds are discussed. The approach is validated and the shortcomings of the implementation are identified

Reusable Agena study. Volume 2: Technical

The application of the existing Agena vehicle as a reusable upper stage for the space shuttle is discussed. The primary objective of the study is to define those changes to the Agena required for it to function in the reusable mode in the 100 percent capture of the NASA-DOD mission model. This 100 percent capture is achieved without use of kick motors or stages by simply increasing the Agena propellant load by using optional strap-on-tanks. The required shuttle support equipment, launch and flight operations techniques, development program, and cost package are also defined

Preliminary design of a 100 kW turbine generator

The National Science Foundation and the Lewis Research Center have engaged jointly in a Wind Energy Program which includes the design and erection of a 100 kW wind turbine generator. The machine consists primarily of a rotor turbine, transmission, shaft, alternator, and tower. The rotor, measuring 125 feet in diameter and consisting of two variable pitch blades operates at 40 rpm and generates 100 kW of electrical power at 18 mph wind velocity. The entire assembly is placed on top of a tower 100 feet above ground level

A model for a space shuttle safing and failure-detection expert

The safing and failure-detection expert (SAFE) is a prototype for a malfunction detection, diagnosis, and safing system for the atmospheric revitalization subsystem (ARS) in the Space Shuttle orbiter. SAFE, whose knowledge was extracted from expert-provided heuristics and documented procedures, automatically manages all phases of failure handling: detection, diagnosis, testing procedures, and recovery instructions. The SAFE architecture allows it to handle correctly sensor failures and multiple malfunctions. Since SAFE is highly interactive, it was used as a test bed for the evaluation of various advanced human-computer interface (HCI) techniques. The use of such expert systems in the next generation of space vehicles would increase their reliability and autonomy to levels not achievable before

The Mercury-Redstone project

Mercury-Redstone project development history, and contributions to future manned spacecraft design and operatio

Applications of discrete network simulation in space vehicle checkout Volume 1 of final report

Discrete network simulation in spacecraft checkou

Correct and Control Complex IoT Systems: Evaluation of a Classification for System Anomalies

In practice there are deficiencies in precise interteam communications about system anomalies to perform troubleshooting and postmortem analysis along different teams operating complex IoT systems. We evaluate the quality in use of an adaptation of IEEE Std. 1044-2009 with the objective to differentiate the handling of fault detection and fault reaction from handling of defect and its options for defect correction. We extended the scope of IEEE Std. 1044-2009 from anomalies related to software only to anomalies related to complex IoT systems. To evaluate the quality in use of our classification a study was conducted at Robert Bosch GmbH. We applied our adaptation to a postmortem analysis of an IoT solution and evaluated the quality in use by conducting interviews with three stakeholders. Our adaptation was effectively applied and interteam communications as well as iterative and inductive learning for product improvement were enhanced. Further training and practice are required.Comment: Submitted to QRS 2020 (IEEE Conference on Software Quality, Reliability and Security