The Role of Compute in Autonomous Aerial Vehicles

Autonomous-mobile cyber-physical machines are part of our future. Specifically, unmanned-aerial-vehicles have seen a resurgence in activity with use-cases such as package delivery. These systems face many challenges such as their low-endurance caused by limited onboard-energy, hence, improving the mission-time and energy are of importance. Such improvements traditionally are delivered through better algorithms. But our premise is that more powerful and efficient onboard-compute should also address the problem. This paper investigates how the compute subsystem, in a cyber-physical mobile machine, such as a Micro Aerial Vehicle, impacts mission-time and energy. Specifically, we pose the question as what is the role of computing for cyber-physical mobile robots? We show that compute and motion are tightly intertwined, hence a close examination of cyber and physical processes and their impact on one another is necessary. We show different impact paths through which compute impacts mission-metrics and examine them using analytical models, simulation, and end-to-end benchmarking. To enable similar studies, we open sourced MAVBench, our tool-set consisting of a closed-loop simulator and a benchmark suite. Our investigations show cyber-physical co-design, a methodology where robot's cyber and physical processes/quantities are developed with one another consideration, similar to hardware-software co-design, is necessary for optimal robot design.

[1]  AKHIL GUPTA,et al.  A Survey of 5G Network: Architecture and Emerging Technologies , 2015, IEEE Access.

[2]  B. Faverjon,et al.  Probabilistic Roadmaps for Path Planning in High-Dimensional Con(cid:12)guration Spaces , 1996 .

[3]  Anass Benjebbour,et al.  Design considerations for a 5G network architecture , 2014, IEEE Communications Magazine.

[4]  Roland Siegwart,et al.  Receding Horizon "Next-Best-View" Planner for 3D Exploration , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[5]  Vincent G. Ambrosia,et al.  Unmanned Aircraft Systems in Remote Sensing and Scientific Research: Classification and Considerations of Use , 2012, Remote. Sens..

[6]  Khaled M. Elbassioni,et al.  Autonomous Recharging and Flight Mission Planning for Battery-Operated Autonomous Drones , 2017, IEEE Transactions on Automation Science and Engineering.

[7]  Toon Goedemé,et al.  Choosing the Best Embedded Processing Platform for On-Board UAV Image Processing , 2015, VISIGRAPP.

[8]  Xuan Zhang Reliable electrical systems for micro aerial vehicles and insect-scale robots , 2017 .

[9]  Andrew Howard,et al.  Design and use paradigms for Gazebo, an open-source multi-robot simulator , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[10]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Matthias Althoff,et al.  CommonRoad: Composable benchmarks for motion planning on roads , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[12]  Luca Carlone,et al.  Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[13]  Michael F. P. O'Boyle,et al.  Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM , 2014, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Alan L. Yuille,et al.  UnrealCV: Connecting Computer Vision to Unreal Engine , 2016, ECCV Workshops.

[15]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

[16]  Obbu Chandra Sekhar,et al.  Design and fabrication of coulomb counter for estimation of SOC of battery , 2016, 2016 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES).

[17]  Simha Sethumadhavan,et al.  RoboBench: Towards sustainable robotics system benchmarking , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Khaled M. Elbassioni,et al.  Flight Tour Planning with Recharging Optimization for Battery-operated Autonomous Drones , 2017, ArXiv.

[19]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

[20]  Ali Farhadi,et al.  YOLO9000: Better, Faster, Stronger , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[21]  S. LaValle Rapidly-exploring random trees : a new tool for path planning , 1998 .

[22]  Wolfram Burgard,et al.  OctoMap: an efficient probabilistic 3D mapping framework based on octrees , 2013, Autonomous Robots.

[23]  Claire J. Tomlin,et al.  Quadrotor Helicopter Flight Dynamics and Control: Theory and Experiment , 2007 .

[24]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[25]  Francesco Sabatino,et al.  Quadrotor control: modeling, nonlinearcontrol design, and simulation , 2015 .

[26]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[27]  Hirohiko Suwa,et al.  An Emergency Medical Communications System by Low Altitude Platform at the Early Stages of a Natural Disaster in Indonesia , 2012, Journal of Medical Systems.

[28]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Davide Scaramuzza,et al.  How Fast Is Too Fast? The Role of Perception Latency in High-Speed Sense and Avoid , 2019, IEEE Robotics and Automation Letters.

[30]  Chris Sanders,et al.  Team MIT Urban Challenge Technical Report , 2007 .

[31]  Ashish Kapoor,et al.  AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles , 2017, FSR.

[32]  Sei Ikeda,et al.  Visual SLAM algorithms: a survey from 2010 to 2016 , 2017, IPSJ Transactions on Computer Vision and Applications.

[33]  Giorgio C. Buttazzo,et al.  Energy-Aware Coverage Path Planning of UAVs , 2015, 2015 IEEE International Conference on Autonomous Robot Systems and Competitions.

[34]  Shaojie Shen,et al.  VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator , 2017, IEEE Transactions on Robotics.

[35]  R. Weisberg A-N-D , 2011 .

[36]  Nicolas Mansard,et al.  Robot Motion Planning and Control: Is It More than a Technological Problem? , 2017, Geometric and Numerical Foundations of Movements.

[37]  Peter King,et al.  Odin: Team VictorTango's entry in the DARPA Urban Challenge , 2008, J. Field Robotics.

[38]  Oussama Khatib,et al.  Springer Handbook of Robotics , 2007, Springer Handbooks.

[39]  Vijay Kumar,et al.  High speed navigation for quadrotors with limited onboard sensing , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[40]  William Whittaker,et al.  Autonomous Driving in Traffic: Boss and the Urban Challenge , 2009, AI Mag..