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This paper describes the AC-coupled, single-sided, p-in-n silicon microstrip sensors used in the SemiConductor Tracker (SCT) of the ATLAS experiment at the CERN Large Hadron Collider (LHC). The sensor requirements, specifications and designs are discussed, together with the qualification and quality assurance procedures adopted for their production. The measured sensor performance is presented, both initially and after irradiation to the fluence anticipated after 10 years of LHC operation. The sensors are now successfully assembled within the detecting modules of the SCT, and the SCT tracker is completed and integrated within the ATLAS Inner Detector. Hamamatsu Photonics Ltd supplied 92.2% of the 15,392 installed sensors, with the remainder supplied by CiS.

This paper presents Multi-Step A∗ (MSA∗), a search algorithm based on A∗ for multi-objective 4-D vehicle motion planning (three spatial and one time dimensions). The research is principally motivated by the need for offline and online motion planning for autonomous unmanned aerial vehicles (UAVs). For UAVs operating in large dynamic uncertain 4-D environments, the motion plan consists of a sequence of connected linear tracks (or trajectory segments). The track angle and velocity are important parameters that are often restricted by assumptions and a grid geometry in conventional motion planners. Many existing planners also fail to incorporate multiple decision criteria and constraints such as wind, fuel, dynamic obstacles, and the rules of the air. It is shown that MSA∗ finds a cost optimal solution using variable length, angle, and velocity trajectory segments. These segments are approximated with a grid-based cell sequence that provides an inherent tolerance to uncertainty. The computational efficiency is achieved by using variable successor operators to create a multiresolution memory-efficient lattice sampling structure. The simulation studies on the UAV flight planning problem show that MSA∗ meets the time constraints of online replanning and finds paths of equivalent cost but in a quarter of the time (on average) of a vector neighborhood-based A∗.

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