Search for: All records

Abstract An internationally collaborative airborne campaign in July 2023 – led by the University of Bergen (Norway) and NASA, with contributions from many other institutions – discovered that thunderstorms near Florida and Central America produce gamma rays far more frequently than previously thought. The campaign was called Airborne Lightning Observatory for Fly’s Eye Geostationary Lightning Mapper (GLM) Simulator (FEGS) and Terrestrial Gamma-ray Flashes (TGFs), which shortens to ALOFT. The campaign employed a unique sampling strategy with NASA’s high-altitude ER-2 aircraft, equipped with gamma-ray and lightning sensors, flying near ground-based lightning sensors. Realtime updates from instruments, downlinked to mission scientists on the ground, enabled immediate return to thunderstorm cells found to be producing gamma rays. This maximized the observations of radiation created by strong electric fields in clouds, and showed how gamma-ray production may be physically linked to thunderstorm lifecycle. ALOFT also sampled storms entirely within the stereo-viewing region of the GLM instruments on GOES-16/18 and performed multiple underflights of the International Space Station Lightning Imaging Sensor (ISS LIS), while using an upgraded FEGS instrument that demonstrated the operational value of observing multiple wavelengths (including ultraviolet) with future spaceborne lightning mappers. In addition, a robust complement of airborne active and passive microwave sensors – including X- and W-band Doppler radars, as well as radiometers spanning 10-684 GHz – sampled some of the most intense convection ever overflown by the ER-2. These observations will benefit planned convection-focused NASA spaceborne missions. ALOFT is an exemplar of a high-risk, high-reward field campaign that achieved results far beyond original expectations. 

The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.