Energy Efficient Autonomous UAVs
The interest in autonomous vehicles is growing constantly, with lots of practical applications appearing on the marketplace and many more being actively studied in academia, industry and military research departments. Two of the most representative examples of such technology are unmanned aerial vehicles (UAVs) and autonomous driving systems. Autonomous systems are emerging in many fields in order to assist and help humans in a plethora of applications, like environment surveillance/inspection, etc. Fully autonomous driving is still out of reach, but virtually every major OEM (BMW, Volvo, Tesla Motors, GM) has a clear roadmap towards achieving this goal and is already commercializing advanced driver assistance systems (ADAS).
Autonomous vehicles represent a unique opportunity to effectively handle critical and expensive activities, compared to the traditional use of human operators only. Autonomous UAVs are adopted for periodic tasks of inspection, like dyke inspection aimed to find cracks on the wall, nuclear plant control or cultivated field monitoring. They can also be extremely helpful in time-critical rescue missions, enhancing the response time and the effectiveness in a natural disaster scenario (e.g. earthquake, avalanche, etc.). ADAS systems are applied by car-makers in order to help the driver in the driving process, increasing the driving comfort, protecting the drivers and reducing accidents. It is clear how autonomous systems can improve the quality of such activities under several aspects: increasing the safety of human operators, reducing the monetary and time costs and improving the effectiveness of the mission (e.g. reduced inspection time or increase the safety on the drive).
To obtain such sophisticated and useful systems, advanced capabilities and cognitive skills are required. Depending on the specific use case, the desired autonomous navigation capability is reached combining one or more basic computational tasks. Widely adopted tasks, such as obstacle avoidance, path planning, target/pedestrian detection, simultaneous localization and mapping, etc, are very complex and computationally intensive. Given the high computational requirements that such workloads expose, current state-of-the-art solutions need quite powerful computational units. The typical class of devices used for such sophisticated algorithms are multi-cores high-end CPUs and embedded heterogeneous systems featuring powerful CPUs coupled with many-core accelerators (e.g. GPUs). In both classes, the order of magnitude for the power consumption is in the range of few Watts to tens of Watts.
Such computational requirements, and power budget, limit the applicability of such techniques to vehicles that expose enough power for the computation. If we consider for instance rotorcraft or flapping-wings UAVs, the available power budget for computation is in between 5-20% of the overall power. Thus, reducing the power consumption of the control system in UAVs will become increasingly important as the size of the vehicle is scaled down. Indeed, reducing the UAV size quickly leads to order-of-magnitude reduction of the power spent on the propellers. Beside, the computational load required to implement the UAV cognitive skills does not vary with the vehicle size, and will thus constitute an increasingly larger fraction of the total system power consumption.
Limiting the on-board power consumption could be achieved by streaming information from the vehicle (e.g. video stream) to either a base-station or the cloud in order to offload the computation, but this is rarely a viable option. In fact, we would introduce additional sever constraints (e.g. communication latency, channel noise, transmission power consumption, maximum distance to the base-station, etc.), limiting the actual autonomous navigation capability of the vehicle. For this reason all the most advanced autonomous navigation systems perform computation directly on-board of the vehicle.
These problems are further exacerbated if we consider the predominance of a trend towards device miniaturization. As already seen in many fields downsizing is the forthcoming technological improvement also in robotics, with a significant reduction of the vehicle dimensions. For example, commercial off the shelf nano rotor-crafts can have only 10 cm diameter and looking into the most extreme research areas we can already find insect-size flying robots. As a consequence of the reduced size of the vehicle we also have a reduction in term of battery dimension, on-board payload and available energy for computation. Also in the automotive field similar considerations in term of computational power budget still hold, where the interest of car-makers is in energy-efficient embedded solutions that directly affect the final cost for the customers.
Thus, it becomes clear that to keep pace with the ever-increasing demand for computation capabilities and the decreasing power budgets we have to target very energy efficient hardware/software solutions. On the other hands, accomplish such challenging task would allow to this new generation of robotic helpers to penetrate in the everyday life, paving the way also for new scenarios like intelligent tiny swarms of UAVs able to cooperate, as well as a fully autonomous driving car.
Our first cyberphysical platform is the nano-size Bitcraze CrazyFlie 2.0 . It has been used in many projects due to its dimension, versatility and its open-source and open-hardware nature. Nowadays is well known how UAVs with high level autonomous navigation capabilities are a hot topic both in industry and academia due to their numerous applications. However, autonomous navigation algorithms are demanding from the computational standpoint, and it is very challenging to run them on-board of nano-scale UAVs (i.e., few centimeters of diameter) because of the limited capabilities of their MCU-based controllers. The nano-quadrotor is an appealing platform (among others) for addressing this challenging task. In this context, we presented a lightweight hardware-software solution, bringing autonomous navigation on a commercial platform using only on-board computational resources. Furthermore, we evaluated how the Parallel Ultra Low-Power Platform  can enable the execution of even more sophisticated algorithms. Demo Video
One of our favorite nano-size platform is the IIS/TIK Nano-Blimp. A nano-sized blimp is a perfect candidate for long flight times because helium, a lighter-than-air gas, can provide lift and significantly reduce the energy requirements for flight.
In the first project of this series we introduced the nano-blimp. We demonstrated that, thanks to the helium-filled balloon, the energy requirement for hovering is significantly reduced. Then, we extended the functionality of our first self-sustainable blimp prototype introducing additional motors and on-board camera, paving the way for autonomous navigation. We enabled first horizontal movement creating a blimp that is able to move in three dimensions. Then, we expanded the on-board processing capabilities with visual sensors and we incorporated, optimized, and improved a simple object tracking algorithm for autonomous flying nano-size UAVs.
Here at IIS we developed the PULP-Shield, the first pluggable PCB for extending the computational power on-board of the CrazyFlie 2.0 nano-size platform. Through this project we enabled the Parallel Ultra Low-Power Platform  to be the key computational unit to bring state-of-the-art complex vision algorithms for autonomous navigation into the nano-scale class of vehicles. Thus, we enabled for the first time the Parallel Computational Paradigm on-board of this tiny class of vehicles. Even if the PULP architecture is meant to act as the main agent of the system, we aim to keep the pre-existing MCU (STM32) present on the UAV as the "coordinator" (i.e., host) of the system. Then, PULP can play the role of the accelerator in charge of performing the compute-intensive kernels. Thus, embodying the classic paradigm Host + Accelerator, the goal is exploiting on one side the existing firmware running on the STM32 MCU, and on the other adding the high computational capability of the 8-cores chip in an ultra-low-power envelope. Future directions for this project series are both in the implementation of state-of-the-art algorithms on-board of our nano-size platforms and on the design of a new cyberphysical system only based on the PULP architecture.
- e-mail: email@example.com
- phone: +41 44 633 88 43
- address: Gloriastrasse 35, 8092 Zürich
- office: ETZ J76.2
We are pleased to inform our students that we have the opportunity to offer co-supervised Master/Semester Thesis on the Autonomous UAVs topic in collaborations with other top-quality research groups like:
- TIK: The Computer Engineering and Networks Laboratory - ETH Zürich - Web Site
- RPG: Robotic and Perception Group - University of Zürich - Web Site
- MICREL: Microelectronics Laboratory - University of Bologna - Web Site
We are listing a few projects below to give you an idea of what we do. However, we constantly have new project ideas and maybe some other approaches become obsolete in the very rapidly advancing research area. Please just contact us and come to talk with us.
- Improved State Estimation on PULP-based Nano-UAVs
- Monocular Vision-based Object Following on Nano-size Robotic Blimp
- A Waypoint-based Navigation System for Nano-Size UAVs in GPS-denied Environments
- Covariant Feature Detector on Parallel Ultra Low Power Architecture
Projects In Progress
- Towards Self-Sustainable Unmanned Aerial Vehicles
- Study and Development of Intelligent Capability for Small-Size UAVs
- Towards Autonomous Navigation for Nano-Blimps
- PULP-Shield for Autonomous UAV
- Self-Learning Drones based on Neural Networks
The group effort and the great contribution from the students of last few years has resulted in the following list of publications:
- 2018 - D. Palossi et Al., "Extending the Lifetime of Nano-Blimps via Dynamic Motor Control", Springer Journal of Signal Processing Systems (Springer JSPS), 2018 - On-line
- 2017 - D. Palossi et Al., "Target Following on Nano-Scale Unmanned Aerial Vehicles", 7th IEEE International Workshop on Advances in Sensors and Interfaces, June 15-16, Vieste, Italy, 2017 - On-line
- 2017 - B. Forsberg et Al., "GPU-Accelerated Real-Time Path Planning and the Predictable Execution Model", International Conference on Computational Science (ICCS), June 12-14, Zürich, Switzerland, 2017 - On-line
- 2017 - D. Palossi et Al., "On the Accuracy of Near-Optimal CPU-Based Path Planning for UAVs", 20th International Workshop on Software and Compilers for Embedded Systems (SCOPES), June 12-13, Sankt Goar, Germany, 2017 - On-line
- 2017 - D. Palossi et Al., "Self-sustainability in Nano Unmanned Aerial Vehicles: A Blimp Case Study", Computing Frontiers (CF), May 15-17, Siena, Italy, 2017 - On-line
- 2017 - D. Palossi et Al., "Ultra Low-Power Visual Odometry for Nano-Scale Unmanned Aerial Vehicles", Design, Automation and Test in Europe (DATE), March 27-31, Lausanne, Switzerland, 2017 - On-line
- 2016 - D. Palossi et Al., "Exploring Single Source Shortest Path Parallelization on Shared Memory Accelerator", 19th International Workshop on Software and Compilers for Embedded Systems (SCOPES), May 23-25, Sankt Goar, Germany, 2016 - On-line
- 2016 - D. Palossi et Al., "An Energy-Efficient Parallel Algorithm for Real-Time Near-Optimal UAV Path Planning", 2nd Workshop on Design of Low Power Embedded Systems (LP-EMS), May 16-18, Como, Italy, 2016 - On-line
- 2016 - F. Conti et Al., "Enabling the Heterogeneous Accelerator Model on Ultra-Low Power Microcontroller Platforms", Design, Automation and Test in Europe (DATE), March 14-18, Dresden, Germany, 2016 - On-line