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Militaries are now employing Micro, Mini & Nano UAVs into their operations. They provide situational awareness to a small group of soldiers by flying several stories above them for 10-20 minutes at a time before placed back into a pocket to recharge. These will be used to carry out tasks in urban environments, such as deliveries, surveillance, and search and rescue. Small drones are considered better because they are more agile, are harder to detect, and are easier for pilots to control.
US Military categorizes Micro and Mini as Group 1: Soldier Portable Recon drones. These have Weight up to 20 pounds, Operating Altitude of 1,200 feet, and Speeds of 100 kts. Micro-UAV’s have generally been UAV’s which are lighter than 5 kg.
Nano drones are the smallest and they usually have the same dimensions as insects. The DoD classifies nano-UAV’s as those being less than 7.5 cm in any dimension. Engineers have been talking about MAVs or Nano Air Vehicles for more than decade but there were many challenges to be overcome. These small drones can be hampered by strong winds, snow, or rain. In addition, electromagnetic fields can disrupt their operation.
Aerodynamic design challenges for NAVs are driven by a combination of low Reynolds number physics (<15,000) and the requirement for a multi-functional platform structure. MAV’s and NAV’s have Reynolds number of less than 100,000 for, which causes the aircraft instabilities and in turn limits the overall control and maneuverability. Due to physical limitations of the low energy efficiency of smaller motors, this is a considerable problem for extremely small aircrafts.
The range and duration of flight have been the most significant problems facing micro-UAV’s both in the past and present. Due to the small size of the aircraft, the amount fuel and battery power that can be carried is currently very limited. Landing micro-UAV’s to refuel not only takes them off-station, but it requires skilled manpower and adds the risk of crashing.
Propulsion and energy storage systems for NAVs will require a highly efficient power source with sufficient energy and power density to fly and execute relevant missions. There was technical challenge is to integrate navigation, guidance and control onto a single chip to meet the restrictive size, weight and power requirements . Furthermore, if NAVs are required to operate autonomously and in “swarms” then the challenge is made even greater due to the much larger processing and sensory requirements these operations entail William A. Davis, Major, USAF in “Nano Air Vehicles, a Technology Forecast”
Unlike larger UAV’s that rely on traditional jamming signal codes and encryption codes, the signal to micro-UAV’s is currently relatively open to interception. The automation in military MAV’s is is also following a relatively slow and incremental path though the initial steps have already been taken by translating the autopilot capabilities of manned aircraft, including takeoff, landing, and navigating between known waypoints.
New technologies enabling Micro and Nano UAV development
The size, weight, power and cost (SWaP-c) parameters can be very critical in micro and nano uavs and require the embedded systems to meet strict design and weight constraints.
The key technological breakthrough for micro-UAV’s will be the development of a suitable high energy-density power source. Some potential breakthrough technologies include lithium-air batteries and high performance fuel cells. While hydrogen fuel cells have been known and investigated for several decades now, any improvement is likely to be incremental. Perhaps more promising are lithium air batteries, which have demonstrated an energy density of ~10 kWh/kg, more than an order of magnitude higher than conventional lithium-ion batteries.
One of the key driver for development of micro and nano uavs have been availability of inexpensive microelectromechanical systems (MEMS) such as accelerometers and gyroscopes which are critical components for their navigation as well as the miniaturisation of various crucial supplied parts.
For instance, researchers at the Massachusetts Institute of Technology have developed a honeybee-sized drone featuring a new navigation chip. The Navion chip is 20 mm square in size and requires only 24 milliwatts of power. It is capable of processing complex images at up to 171 frames per second.
The recent improvements in batteries and other key electronic components mean that the technology is now becoming more practical and affordable. ‘People can do things now that they couldn’t foresee even five to 10 years ago,’ said Dr Stephen Prior, a UAV specialist from the University of Southampton. ‘Microprocessors are becoming cheaper, faster and more capable, and the technology from the remote-control world is starting to become pervasive and affordable.’
Payload has been one of the limitations of micro-UAV’s when compared with bigger UAV’s and other manned aircrafts, due to their size. The requirement is of sensor components that are smaller and more rugged — and often times available off the shelf. Sensor minitiarization has enabled even smaller UAVs to be equipped with an impressive array of sensitive optical sensors, laser rangefinders and thermal cameras for tasks ranging from intelligence gathering to hunting down terrorists.
Israel-based Elbit Systems has developed a new type of miniature designator marker (MDM) for its small-class UAVs. Laser designators allow users to rapidly home in on hostile targets. “The operational need to designate from small UAVs, particularly below cloud cover, has become an ever-growing requirement to enable significantly shortening the sensor-to-shooter duration,” said Dalia Rosen, Elbit’s VP of corporate communications. Elbit’s high-performance MDM weighs only 100 g and is assembled using automated robotic techniques; it’s too small to be manufactured using standard methods, according to Rosen.
Research is being carried out on the following areas:
– Sensor fusion – Synthesizing information from multiple sensors for computation tasks
– Communications – Handle communications from multiple sources and coordinate information
– Path planning – Determining the optimal path for flight and adjusting to hazards or attacks
– Cooperative Tactics – Allowing UAV swarms to communicate and coordinate activities
– Targeting – Automated processes for identifying and tracking the target
Breakthrough radar payload for MAVs
Plextek, an Essex-based design and innovation technology consultancy has built a a compact millimetric radar (60GHz) for MAVs. One of the challenges addressed is that of operating a small UAV in a non-line-of-sight environment such as an urban canyon or into a building. Plextek’s Head of Defence Peter Doig explains that 60GHz is particularly suited to use in an indoor environment as it is strongly reflected by most surfaces, allowing it to penetrate deeply into buildings. It also has advantages over other systems in navigation, detecting objects, and sense and avoidance.
He says: “Conventional sensors such as camera systems only provide range or depth perception after significant, complex processing of the sensor data and that’s not easily compatible with the processing power available on a small UAV.” Laser-based range detection systems, such as LIDAR, have high resolution and high precision but do not function so well in conditions of rain, fog or dust and don’t have the same ability as radar to detect moving objects.
Plextek built two variants of a concept demonstrator, a mechanical scanning variant which can scan in two dimensions and a one-dimensional scan system, both of which have successfully undergone a range of trials and tests. This has demonstrated that the radar can detect objects of interest to a UAV: for example, a 10mm diameter wire rope out to 35m, a chain link fence out to 120m, a quadcopter out to 75m and a person at a range of more than 150m. This would allow the flight control on the UAV to perform collision avoidance.
At these extremely high frequencies (EHF), systems often require highly directional antennas that have narrow beamwidths and can be manufactured in a cost-effective manner. In this project, the antenna forms part of a radar system operating at 60 GHz and the design was taken from initial concept through detailed system performance and design calculations to an integrated, highly manufacturable, single-board radar sensor in just a few months.
The novel antenna for the mm-wave radar incorporates Substrate Integrated Waveguide (SIW) technology within a multi-layer, low-loss, microwave laminate. This method involves using the PCB itself to create an enclosed transmission line that can be implemented on the same microwave laminate as the radar transceiver circuitry, thereby yielding a compact design that makes best use of the PCB area.
A corporate-feed network connects the radar transceiver to a multi-element slot array antenna comprising of 32 x 16 slot elements. The aperture is weighted to reduce sidelobe levels, an essential requirement for radar systems. The resulting antenna pattern exhibits a sharp ‘pencil beam’ with high gain and nominal half-power beamwidths of 4° by 9°.
The complete radar system requires minimal assembly because both transmit and receive antennas are inherently aligned. Trials of the micro-radar system have demonstrated the ability to detect relevant targets at distances up to 100m. Ultimately, a uniquely capable radar sensor having low size, weight and power (SWaP) has been realised.
Plextek took the radar developed for the Dstl project to provide a solution capable of detecting objects and movements from a fixed site out to 400m and also implemented a mobile system where the radar is mounted onto the roof of a vehicle to scan the runway while moving. The Ministry of Defence also has a need for smart sensing in UAVs for autonomous last-mile resupply to deliver mission-critical supplies; in response, Plextek has put forward a proposal to demonstrate the radar’s capability. The company hopes that this system will also allow organisations such as Amazon to make deliveries using UAVs at night and in inclement weather conditions.
Breakthrough in Drone Miniaturization
In recent years, engineers have worked to shrink drone technology, building flying prototypes that are the size of a bumblebee and loaded with even tinier sensors and cameras. Thus far, they have managed to miniaturize almost every part of a drone, except for the brains of the entire operation — the computer chip.
Current minidrone prototypes are small enough to fit on a person’s fingertip and are extremely light, requiring only 1 watt of power to lift off from the ground. Their accompanying cameras and sensors use up an additional half a watt to operate. “The missing piece is the computers — we can’t fit them in terms of size and power,” Karaman says. “We need to miniaturize the computers and make them low power.”
Standard computer chips for quadcoptors and other similarly sized drones process an enormous amount of streaming data from cameras and sensors, and interpret that data on the fly to autonomously direct a drone’s pitch, speed, and trajectory. To do so, these computers use between 10 and 30 watts of power, supplied by batteries that would weigh down a much smaller, bee-sized drone.
Now, engineers at MIT have taken a first step in designing a computer chip that uses a fraction of the power of larger drone computers and is tailored for a drone as small as a bottlecap. The key contribution of their work is a new approach for designing the chip hardware and the algorithms that run on the chip. “We are finding that this new approach to programming robots, which involves thinking about hardware and algorithms jointly, is key to scaling them down,” Karaman says.
The team, led by Sertac Karaman, the Class of 1948 Career Development Associate Professor of Aeronautics and Astronautics at MIT, and Vivienne Sze, an associate professor in MIT’s Department of Electrical Engineering and Computer Science, developed a low-power algorithm, in tandem with pared-down hardware, to create a specialized computer chip.
The new chip processes streaming images at 20 frames per second and automatically carries out commands to adjust a drone’s orientation in space. The streamlined chip performs all these computations while using just below 2 watts of power — making it an order of magnitude more efficient than current drone-embedded chips.
Karaman, says the team’s design is the first step toward engineering “the smallest intelligent drone that can fly on its own.” He ultimately envisions disaster-response and search-and-rescue missions in which insect-sized drones flit in and out of tight spaces to examine a collapsed structure or look for trapped individuals. Karaman also foresees novel uses in consumer electronics.
“Imagine buying a bottlecap-sized drone that can integrate with your phone, and you can take it out and fit it in your palm,” he says. “If you lift your hand up a little, it would sense that, and start to fly around and film you. Then you open your hand again and it would land on your palm, and you could upload that video to your phone and share it with others.”
The researchers decided to build a specialized chip from the ground up, developing algorithms to process data, and hardware to carry out that data-processing, in tandem. Specifically, the researchers made slight changes to an existing algorithm commonly used to determine a drone’s “ego-motion,” or awareness of its position in space. They then implemented various versions of the algorithm on a field-programmable gate array (FPGA), a very simple programmable chip. To formalize this process, they developed a method called iterative splitting co-design that could strike the right balance of achieving accuracy while reducing the power consumption and the number of gates.
A typical FPGA consists of hundreds of thousands of disconnected gates, which researchers can connect in desired patterns to create specialized computing elements. Reducing the number gates with co-design allowed the team to chose an FPGA chip with fewer gates, leading to substantial power savings. “If we don’t need a certain logic or memory process, we don’t use them, and that saves a lot of power,” Karaman explains.
Each time the researchers tweaked the ego-motion algorithm, they mapped the version onto the FPGA’s gates and connected the chip to a circuit board. They then fed the chip data from a standard drone dataset — an accumulation of streaming images and accelerometer measurements from previous drone-flying experiments that had been carried out by others and made available to the robotics community.
Ultimately, the team plans to implement the optimized algorithm on an application-specific integrated circuit, or ASIC, a more specialized hardware platform that allows engineers to design specific types of gates, directly onto the chip. “We think we can get this down to just a few hundred milliwatts,” Karaman says. “With this platform, we can do all kinds of optimizations, which allows tremendous power savings.” This research was supported, in part, by Air Force Office of Scientific Research and the National Science Foundation.
