Sensor-enabled vehicles use an autonomous navigation system to move through the atmosphere or down roads and terrain without direct, continuous human direction. Vehicles that use autonomous navigation systems include robots, spacecraft, ships, airplanes, and cars.
Naturally, there is a lot of overlap between this use case and remote navigation systems.
Autonomous navigation can improve both commercial and private thoroughfare safety in two main ways. First, by removing human beings from the cockpit entirely, spacecraft, for example, can go out and explore parts of the solar system that humans cannot. Second, certain vehicle sensors can “see” vehicles and objects hidden from human sight by other vehicles or by buildings. These navigation systems also don’t get distracted by people in the back seat or on the side of the road.
Additionally, many vehicles or robots with these navigation systems can be bought and used much cheaper than human pilots or operators. This is especially true when companies ask that they run 24/7.
Naturally, autonomous navigation systems should have destination and point-of origin data as well as accurate and responsive sensors. The type of sensor technology and placement depends on the vehicles in use. Additionally, the system should have good quality IoT capabilities which can collect and analyze information coming from all sensors, from every vehicle in use, right away.
Most essential navigation data is external. These include road maps and land or airway boundaries, and route conditions—including piracy hazards and traffic. In the same vein, weather data and forecasts are essential.
Some vehicles that navigate within buildings, whether homes or warehouses, may also need ADA regulation data or the local equivalent. The Americans with Disabilities Act set standards for the height and width of doors and hallways; robots or other vehicles not built to fit these standards will not find much success on the market.
Autonomous navigation systems frequently integrate with smartphones or home computers—especially cars. Other industries, however, may use a custom interface or analytics platform, or they may integrate with an enterprise-based SaaS provider system.
Other sources of external data can provide user enrichment. Tesla, for example, uses time and calendar data to anticipate routes a user plans on taking. Thus, someone returning home from work at 7 p.m. doesn’t need to select their route or destination; the navigation system already knows.
As noted earlier, many industries use autonomous navigation systems, and each of these faces specific challenges. Cars, for example, must anticipate distracted drivers in other vehicles swerving into the wrong lane. Meanwhile, spacecraft must forecast and prepare for space weather conditions that can damage equipment. All industries, however, must contend with adverse weather, limitations on the range of IoT communications, and the potential for hackers to break in and cause chaos. All navigation systems, therefore, must maintain physical equipment and IoT communications security.
Technion: ANPL: Autonomous Navigation and Perception Lab
IEEE: Autonomous navigation and control of unmanned aerial systems in the national airspace
The Russian maritime industry is moving forward aggressively with plans to deploy autonomous navigation systems on its commercial shipping fleet.
These steps are part of a broader effort across Russia for the deployment of maritime autonomous surface ships (MASS) and the development of a full range of technical systems for autonomous navigation. Russian Prime Minister Mikhail Mishustin recently approved a decree to support the introduction of a national-wide experiment in MASS operations. It allows every shipping company to test MASS operations.
The Maritime Executive: Russia Moving Forward with Autonomous Navigation on Commercial Vessels
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