Personal tools

Energy Efficient Autonomous UAVs

From iis-projects

Revision as of 12:46, 29 July 2021 by Vladn (talk | contribs) (C. Indoor Localization with UWB)
Jump to: navigation, search
A-B) The PULP-Shield PCB developed at IIS by our student Hanna Müller. C) Our nano-drone prototype based on the CrazyFlie 2.0 coupled with the PULP-Shield.


Introduction and Platforms

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, monitoring, 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.

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 by 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 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 an order-of-magnitude reduction of the power spent on the propellers. Besides, 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.

a. Pico-size Quadrotor (Fünfliber drone)

b. Nano-size Quadrotor

The CrazyFlie 2.1

Our main cyber-physical platform is the nano-size Bitcraze CrazyFlie 2.1 [1]. 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 a 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 that are also in charge of running the control and estimation algorithms for flying the drone. In order to extend the computational capabilities of the drone, we equip it with the plug-in PC called the AI-Deck that enables complex artificial intelligence-based workloads to run onboard, with the possibility to achieve fully autonomous navigation capabilities.

The AI-deck contains the GWT GAP8 SoC based on the Parallel Ultra-Low-Power [2] architecture paradigm, which features 1+8 general-purpose RISC-Vcores: one, named Fabric Controller (FC), acts as the central coordinator of the MCU; the remaining are grouped in a parallel Cluster (CL) - all cores are based on the open-source RI5CY design. Furthermore, the AI-deck also features an ultra-low-power onboard camera and a WiFi module.

Our previous work presents a lightweight hardware-software solution based on a CNN that brings autonomous navigation on the Crazyflie+Ai-deck using only the onboard resources. Furthermore, we evaluated how the Parallel Ultra-Low-Power Platform [2] can enable running sophisticated machine learning algorithms for achieving autonomous navigation or localization capabilities.

Demo Video

c. Standard-size Quadrotor

The Standard-size Platform

The Standard-Size Unmanned Aerial Vehicle platform is a complete system that provides a development testbed for implementing and profiling advanced algorithms for autonomous navigation. Its primary purpose is to provide an unconstrained environment (computational power, sensor accuracy, and battery duration) to test new algorithm paradigms later to be applied, with further optimizations, in other drone architectures such as the nano-sized drone platform. Additionally, the platform can also serve as the central platform for other autonomous navigation scenarios where its unique features are required. At the center of the architecture, the MATEK F722-SE (ARM Cortex-M7) Flight Controller (FC) together with iNav, an open-source firmware platform for autonomous navigation, account for the flight’s attitude control, telemetry forwarding, and communication with external devices, such as base stations or onboard computational modules. The architecture also integrates a high-end embedded device (NVIDIA Jetson TX2) that provides computational power and memory in excess to satisfy the real-time constraints of the processing of state-of-the-art(SoA) algorithms for autonomous navigation.

Topics

a. Vision-based Autonomous Navigation

The CrazyFlie equipped with the AI-deck performing autonomous navigation

Vision-based perception algorithms traditionally employ simultaneous localization-and-mapping (SLAM), which is a technique that builds a 3D local map of the environment, which is used to plan the trajectory accordingly. Nevertheless, the main drawback of the approaches based on SLAM is that they are computationally demanding. Furthermore, while SLAM can be used for perception, it does not solve the challenging issue of inferring the control commands from the 3D map. More recent solutions, such as end-to-end convolutional neural networks (CNNs), mitigate this issue by directly estimating the optimal control commands, using camera images as input. Due to the recent advancement of low-power parallel hardware platforms such as PULP, convolutional neural networks (CNNs) can run very efficiently, even aboard a small nano-drone. Therefore, the GAP8 chip found on the AI-deck allows running complex NN models significantly faster than the classical single-core MCUs (such as the Cortex M4 found in Crazyflie).

b. Lidar/Radar-based Autonomous Nagivation

C. Indoor Localization with UWB

thumb|right|220px| aaa

Ultra-wideband (UWB) is one of the most promising and adopted ranging (i.e., distance measuring) technologies used for positioning and localization, as it enables centimeter-precision distance estimation and data transmission. In our applications, we use UWB with the time-of-arrival (ToA) technique, which determines the distance between two UWB nodes based on the travel time of a radio signal from the transmitter to the receiver. Due to its high-precision ranging, UWB enable


However, there are still drawbacks associated with UWB, such as increased ranging errors, caused by physical and electrical factors - i.e., non-line-of-sight (NLOS) conditions, antenna delays. Previous works use machine learning algorithms such as neural networks to model and compensate for the UWB ranging errors. Furthermore, using learning algorithms to identify patterns in the UWB channel impulse response (CIR) can also lead to UWB error modeling or can improve the ranging frequency by enabling concurrent ranging.

Contact Information

Hanna Mueller
Vlad Niculescu


Daniele Palossi

Collaborations

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


Projects

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.

Available Projects


Projects In Progress


Completed Projects


Publications

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


External Links


↑ top