To help small aerial robots navigate in the dark and other low-visibility environments, colleagues at Massachusetts' Worcester Polytechnic Institute have developed an ultrasound-based perception system inspired by bat echolocation.
Current robots rely heavily on cameras or light detection and ranging, known as lidar, or both. But these sensors fail in visually challenging conditions, such as smoke, fog, dust, snow or complete darkness.
The team was led by Nitin Sanket, an Assistant Professor of Robotics Engineering at the institution. Sanket is a scientific engineer who develops bio-inspired microrobots.
The advance, inspired by bats, suggests that ultrasound may be an alternative to existing navigation technologies that add weight and cost to a drone or falter in poor conditions.
“Bats that weigh less than two paper clips can accurately navigate in dark, damp, and dusty caves by sending out short chirps and listening to the weak echoes with a limited number of neurons.
“By creating an ultrasound-based system that needs just two tiny sensors and little computation, we can open up opportunities for small aerial robots to perceive their surroundings, make decisions, and independently operate longer in cluttered, hazardous places where current aerial robots struggle.”
Sanket’s research focuses on robotics inspired by nature, such as bees and bats
“My research team looked at nature’s experts at navigating in poor visibility: bats,” Sanket says.
“They thrive in dark, damp and dusty caves and can detect obstacles as thin as a human hair using echolocation while weighing as little as two paper clips. They emit sound waves and listen to weak echoes reflected from objects.
“However, enabling this sensing on aerial robots is extremely challenging because propellers generate a lot of noise. It is a bit like trying to listen to your friend while a jet engine is taking off next to you.”
To overcome this issue, the team came up with two key suggestions. First, a physical acoustic shield inspired by a bat’s ear cartilage which reduces propeller noise around the acoustic sensors. In this case, the shield would act as the small drone’s ears.
Second, a neural network called Saranga, which recovers weak echo signals from very noisy measurements by learning patterns over time, inspired by how bats process sound.
Together, these features enable the robot to estimate obstacle locations in 3D and navigate safely using milliwatt-level sensing power.
The developers came up with this version of drones, which they believe can be very useful for search and rescue missions, especially in confined, dynamic and dangerous environments, because they are small and inexpensive.
“Search-and-rescue operations often happen in environments where visibility is very poor, such as forest fires, collapsed buildings, caves or dusty outdoor conditions,” Sanket explains.
“In these scenarios, traditional sensors like cameras and lidar often become unreliable. Bats do not rely only on vision and instead use echolocation to perceive the world. Ultrasound sensing doesn’t depend on lighting conditions and works in smoke, dust and darkness.
“Our work shows that it is possible to bring this capability to aerial robots despite strong onboard propeller noise. Sonar boosted by noise shielding and machine learning promises to enable a new class of small, low-cost robots that can operate in environments where current systems fail.”
He adds that the discovery can enable highly functional, autonomous, tiny aerial robots for critical humanitarian applications, such as search and rescue, combating poaching and cave exploration.
AI-enabled sonar navigation could also lead to safer, faster and more cost-effective robots for time-sensitive operations where human or larger helicopter access is limited.
Moreover, this could be a step toward these drones being able to deploy as swarms of aerial robots, much like groups of bats, to explore hazardous environments and search for survivors.
“Breakthroughs in mathematical modeling, neural network design and sensor characterisation will enable other low-power applications for these drones, such as environmental monitoring. Our work can reduce power by 1,000 times, weight by ten times and cost by 100 times compared to current solutions.”
The new technology is looking to challenge the status quo where most unmanned aerial navigation systems rely on cameras, depth sensors or lidar, which degrade in low visibility.
Radar works in these conditions but is power-intensive for small drones. Prior work has explored ultrasound sensing mainly on ground robots, but applying it to aerial robots has been difficult due to propeller noise and weak signals.
“We are working on improving flying speed, sensing range and system size,” Sanket says of the future.
“We are also exploring new bio-inspired designs and combining ultrasound with other types of sensing. Ultimately, our goal is to build reliable, low-power aerial robots that can operate reliably in dynamic environments and enable real-world deployment in search and rescue.”