Pyka and Windracers are building aircraft from scratch, arguing this allows autonomy to be designed in from the outset and the aircraft tailored to the job.

Others are retrofitting existing larger planes.

Backed by Boeing’s investment arm, US-based Reliable Robotics is currently testing its system on the Cessna 208B Grand Caravan, a single-pilot cargo plane that can carry about 1360kg of payload over hundreds of kilometers.

Retrofitting on certified aircraft lets the company focus exclusively on proving the autonomous system’s safety rather than also having to seek approval for a new aircraft, says Robert Rose, its co-founder and CEO.

Merlin Labs, also US-based, has been working its way up through progressively larger military aircraft and is now applying its system to the two-pilot Lockheed Martin C-130J military transport plane, with commercial multi-crew cargo planes next.

“It is a common autonomy brain that can transition between different aircraft,” explains Matt George, Merlin’s founder and CEO.

The companies also differ in their approach to AI.

Reliable is avoiding it altogether, arguing it would complicate certification.

Merlin, meanwhile, is taking a far more AI-centric approach.

The divide is evident in so-called detect and avoid systems.

One of autonomous flight’s biggest challenges is replicating a pilot’s ability to spot and maneuver safely around other aircraft and obstacles, and there is virtually no margin for error.

With no perfect solution yet, companies are adding different sensor systems as well as duplicating those that already come as standard to provide extra back-up.

Reliable has added forward-looking air-to-air radar to detect other aircraft more than eight kilometers ahead, with software that follows fixed rules to decide how the plane should respond.

It is “better than a pilot’s eyeballs” says Rose.

Merlin, meanwhile, is using AI-powered cameras to detect and classify objects.

Pyka has used lidar from the outset to detect trees, vehicles, large birds and terrain. But it’s short range, so the company also plans to add AI-powered cameras, its first real use of AI onboard.

“For a lot of things there’s no need to use AI…but for figuring out that six pixels in the distance are an airplane versus some other smudge, it is perfect territory,” says Norcia.

The AI dilemma also extends to communicating with air traffic control.

In shared airspace, aircraft must be able to receive, interpret and respond to radio instructions, typically from air traffic control.

Reliable’s solution is to have a remote pilot on the ground, initially fully trained, to handle communications and make safety-critical decisions.

Merlin plans to use generative AI, trained on thousands of hours of recorded exchanges, to interpret instructions and respond itself.

“Our problem is harder… but we want to move beyond remote piloting,” says George.

Merlin plans to reduce pilots in stages, from two to one and eventually none.

Pyka, says Norcia, is content to let others “blaze the trail” in finding the best way to operate in shared airspace.

Meanwhile, even if fully autonomous passenger flight remains elusive, many expect the technology being pioneered will inch into commercial aviation, potentially making piloted flying safer.

That, notes ALPA, the US pilots’ association, would be a welcome development.