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Scientists Discover Way to Send Information into Black Holes Without Using Energy

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This Drone Fly & Navigate Like A Bat & Can Save Lives In Smoke & Fog

Imagine a tiny drone, no bigger than the palm of your hand, flying through thick fog, smoke, or a dark, cluttered building—and finding its way safely. Thanks to a team led by Nitin J. Sanket, an assistant professor at Worcester Polytechnic Institute (WPI), this scenario is closer to reality than ever. Their research demonstrates that using ultrasound sensors combined with artificial intelligence (AI) allows small aerial robots to navigate challenging environments while consuming minimal power and processing capacity.

Published in Science Robotics, this work shows that bats—nature’s masters of navigation—can inspire practical, life-saving technology. Bats, which weigh less than two paper clips, effortlessly fly through dark, damp caves by emitting short chirps and analyzing the faint echoes returning from objects around them. Remarkably, they do this using only a small number of neurons in their brains. Sanket and his team wondered: could drones do something similar?

Why Ultrasound Over Traditional Sensors?

Current autonomous aerial robots rely on a combination of cameras, lidar (light detection and ranging), radar, and other sensors to perceive their surroundings and make navigation decisions. While effective in many conditions, these systems have drawbacks:

  • Weight and cost: Lidar and radar units are often heavy and expensive. For small drones, every gram counts.

  • Power consumption: High-powered sensors drain batteries quickly, limiting flight time.

  • Environmental interference: Darkness, fog, smoke, or snow can reduce the effectiveness of light-based systems.

  • Noise interference: Propeller noise complicates the analysis of echoes for small drones, making it harder to identify obstacles accurately.

By contrast, ultrasound requires less hardware, less energy, and lower computational power, making it ideal for palm-sized drones. Sanket explains, “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.”

How the Bat-Inspired Drone Works

Sanket’s team built an X-shaped quadrotor drone, roughly six inches wide and weighing about one pound. The key components of their bat-inspired system include:

  1. Ultrasound sensors: These emit high-frequency sound waves and listen for returning echoes. The echoes reveal the distance and shape of nearby obstacles.

  2. Acoustic shield: This physical barrier reduces propeller noise, allowing the sensors to focus on meaningful echoes.

  3. AI-based echo analysis: Using deep learning, the team trained the drone’s computer to interpret weak ultrasound echoes in a way similar to how a bat’s brain processes sound.

The system is efficient: it can navigate autonomously while consuming far less power than traditional lidar-based drones, allowing it to fly longer on a single battery charge.

Testing in Real-World Conditions

To test their innovation, the researchers conducted 180 experimental flights under various conditions:

  • Outdoors in wooded areas to simulate natural obstacles like trees and shrubs.

  • Indoors with laboratory obstacles, including transparent plastic and metal poles.

  • Challenging lighting conditions, from complete darkness to low light.

  • Visually degraded environments, including artificial fog and blown snow.

The results were impressive. The drone successfully navigated through complex courses 72% to 100% of the time, depending on the obstacle configuration. While it struggled slightly with thin obstacles like slender tree branches and metal poles—which reflect weaker ultrasound signals—the overall performance demonstrates significant promise for search-and-rescue missions.

Advantages of Ultrasound Navigation

Sanket’s approach offers several key benefits:

  • Lightweight and compact: The drone uses only two sensors and a small processing unit.

  • Energy-efficient: Low-power sensors allow longer flight times, critical in emergency situations.

  • Robust in poor conditions: Ultrasound can penetrate smoke, fog, and darkness, where cameras and lidar may fail.

  • Simpler computation: By offloading complex pattern recognition to AI, the drone can make quick navigation decisions without heavy onboard processing.

These advantages make ultrasound navigation particularly suitable for drones operating in disaster zones, collapsed buildings, or other hazardous environments where traditional systems may falter.

Nature-Inspired Robotics: Learning from Bats and Bees

Sanket’s broader research focuses on bio-inspired robotics, designing robots that mimic the abilities of animals such as bats and bees. These creatures have evolved highly efficient navigation and perception systems over millions of years. By studying nature, engineers can develop technology that is not only effective but also energy-efficient and compact.

For instance, bees navigate complex environments using minimal brainpower, and bats fly through dark, cluttered caves using only sound. Translating these natural strategies into robotic systems allows engineers to design smaller, smarter, and more resilient drones.

Implications for Search-and-Rescue

In real-world emergencies, such as earthquakes, building collapses, or forest fires, seconds can mean the difference between life and death. Palm-sized drones that can operate autonomously in degraded environments have enormous potential to assist human rescuers by:

  • Mapping hazardous areas quickly without risking human lives.

  • Finding survivors in low-visibility conditions, such as smoke-filled rooms or foggy terrains.

  • Providing real-time information to rescue teams, enabling faster decision-making.

Sanket emphasizes, “In a real search-and-rescue mission, a few more seconds of flight time could mean the difference between life and death for a survivor.” By reducing power requirements and simplifying sensor systems, ultrasound-based drones could remain in the air longer, cover more ground, and increase the chances of successful rescues.

Challenges and Future Directions

While the technology is promising, the research team acknowledges some limitations:

  • Thin or small obstacles: Slender objects such as poles or tree branches reflect weaker ultrasound signals, which can make navigation less reliable.

  • Flight speed: Current prototypes are optimized for obstacle avoidance rather than high-speed flight.

  • Battery life: The drone’s current battery allows about five minutes of flight per charge, which could limit operational range.

Future work aims to address these challenges. Sanket envisions smaller, lighter drones with improved flight times and speeds, expanding the possibilities for ultrasound-powered aerial robots in practical rescue missions.

A Step Toward Smarter, Safer Drones

The research by Sanket and his team represents a significant leap in autonomous aerial robotics, demonstrating that nature-inspired solutions can outperform traditional high-power systems in certain environments. Ultrasound-based navigation offers a cost-effective, lightweight, and energy-efficient alternative for drones that must operate in visually degraded or hazardous conditions.

As technology advances, these drones may become invaluable tools for emergency responders, environmental monitoring, and exploration in areas too dangerous or inaccessible for humans. By taking inspiration from bats, a creature smaller than two paper clips, researchers are proving that big innovations can come from tiny packages.

Reference:
Manoj Velmurugan et al., Milliwatt ultrasound for navigation in visually degraded environments on palm-sized aerial robots, Science Robotics (2026). DOI: 10.1126/scirobotics.adz9609

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