To facilitate a series of “far-reaching operational improvements,” Kongsberg Maritime has developed a new Launch and Recovery System (LARS) for its HUGIN range of AUVs. According to Kongsberg, LARS is a result of a “uniquely integrated combination of advanced in-house technologies” that the Kongsberg Group offers, which have been reinforced with years of accumulated expertise. LARS is expected to operate from midships, with the release and capture of HUGIN marine robots occurring beneath the sea surface. “There is limitless scope in this new LARS design,” says Bjørn Gjelstad, Marine Robotics R&D manager, Kongsberg Maritime.

Aerial delivery technologies provider IrvinGQ has developed and tested an airdrop system that can be used to rapidly deploy Milrem Robotics’ UGV into warzones or large-scale terrain fires. Expected to be used for defense or firefighting missions, respectively, Milrem Robotics’ THeMIS or the Multscope Rescue UGV will be equipped with IrvinGQ’s ATAX airdrop system. Other aerial deployment possibilities of Milrem Robotics’ UGVs include helicopter underslung or in the cargo area of a helicopter such as the Chinook CH-47.

To help with development of its UAS strategy, telecommunications company Telstra is partnering with analytics, assurance and optimization technologies provider TEOCO. Telstra will use TEOCO’s AirborneRF technology to assess the readiness of its radio access network for future UAS applications, including communications, navigation, surveillance, safety, and identity. According to TEOCO, AirborneRF, which is already being used by several tier-one operators across the world, ensures “effective, mission-critical” connectivity to enable effective traffic management and control for UAS in the lower airspace. 

To help driverless cars learn to navigate a variety of worse-case scenarios before they begin operating on real roads, researchers at MIT have invented a simulation system to train driverless cars that creates a photorealistic world with “infinite” steering possibilities.  According to the researchers, control systems—also known as “controllers” —for autonomous vehicles largely rely on real-world datasets of driving trajectories from human drivers. The vehicles use this data to learn how to emulate safe steering controls in different situations. Researchers note, though, that real-world data from hazardous “edge cases,” such as nearly crashing or being forced off the road or into other lanes, are rare.