Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components

Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components ByungDuk Song, Jonghoe Kim, Jeongwoon Kim, Hyorin Park, James R. Morrison* and David Hyunchul Shim Department of Industrial and Systems Engineering Department of Aerospace Engineering KAIST, South Korea Friday, May 31, 2013

Presentation Overview • Motivation • UAV service system concept • Comparison with existing research • UAV service system: Components and prototype • Central planning: Deterministic customer paths • UAV guidance system • Automatic replenishment station • System demonstration • Concluding remarks

Motivation • Large expensive UAVs • Usually military purpose • Operate for many hours • Travel long distances • Small inexpensive UAVs • A lot of application area such as tracking, communication relay, environmental / fire / national boundary monitoring, cartography, disaster relief and so on. • Limited duration of mission • Limited distance • Methods to ensure persistent operation can increase effectiveness of small UAVs • Collection of UAVs, refueling stations, automatic guidance • Algorithms to orchestrate the system operations

UAV Service System Concept • Random arrival of customer information • Random path and duration • Heterogeneous UAVs Persistent UAV service • Vision technology • UAV operation system • Automatic replenishment station • Central planning

UAV Service System Concept • Random path and duration • Heterogeneous UAVs Persistent UAV service • Vision technology • UAV operation system • Automatic replenishment station • Central planning

UAV Service System Concept • Heterogeneous UAVs Persistent UAV service • Vision technology • UAV operation system • Automatic replenishment station • Central planning

Comparison with Existing Research <Automatic landing & recharge> <Decentralized task assignment algorithm> Persistent path following with multiple shared service stations distributed across the field of operations Prototype components for a system seeking to provide a persistent UAV security escort service <Automated 1.5 Hour persistent surveillance mission with three autonomous vehicles> • [1] M. Alighanbari and J. P. How, “Decentralized task assignment for unmanned aerial vehicle”, Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, December 2005 • [2] M. Valenti, D. Dale, J. P. How and D. P. de Farias, “Mission health management for 24/7 persistent surveillance operations”, AIAA Guidance, Navigation and Control Conference and Exhibit, August 2007

UAV Service System: Components and Prototype Customer information UAV schedule Central planning by MILP Web or smart phone UAV guidance system Automatic control feedback Tracking UAV Customer Automatic replenishment Replenishment station

Central Planning: Deterministic Customer Paths Customer information UAV schedule Central planning by MILP Web or smart phone UAV guidance system Automatic control feedback Tracking UAV Customer Automatic replenishment Replenishment station

Persistent UAV Service ■ Persistent UAV service system with heterogeneous UAVs and multiple service stations - A system of UAVs that is supported by automated replacement systems can support long term or even indefinite duration missions in a near autonomous mode with multiple service stations - The UAVs can return to any service station, replenish their resources and resume their duties

Customer Paths ■ To follow a time-space trajectory, the trajectory is divided into pieces (split jobs) Service station 2 Split Job 1 UAV 2 Start UAV 2 5 8 1 9 6 7 4 10 2 UAV 1 End UAV 1 3 UAV 1 ▪ Objective moves Service station 1 - From point (50,250) to (950,350) UAV 2 - From 13:10 to 13:20

Assumptions ■ Assumptions 1. Moving target’s path and location at specific times are known. • 2. UAVs start its travel from a recharge station 3. Recharge time for a UAV is constant 4. Initially all UAV batteries or fuel tanks are empty 5. UAV travel speed is constant

Initial Mathematical Formulation ■ Notation

Initial Mathematical Formulation ■ Notation ■ Decision Variables ▪ Xijkr = 1 if UAV k processes split job j or recharges at station j after processing split job i or recharging at station i during the rth flight; 0, otherwise • ▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise. • ▪ Cikr is job i’s start time by UAV k during its rth flight or UAV k’s recharge start time at station i; • otherwise its value is 0.

Initial Mathematical Formulation ■ Mathematical formulation Initial recharge station constraints Start time constraints Recharge station constraints Fuel constraints Dummy job constraints Split job assignment constraints Decision variables

Reduce Variables and Constraints ■ Mathematical formulation Initial recharge station constraints Start time constraints Recharge station constraints Fuel constraints Dummy job constraints Split job assignment constraints Decision variables

Improved Formulation ■ Mathematical formulation Initial recharge station constraints Start time constraints Recharge station constraints Fuel constraints Decision variables Split job assignment constraints

Improved Formulation ■ Complexity : Number of decision variables and constraints

Computational Results ■ Comparison of computational result

UAV Guidance System Customer information UAV schedule Central planning by MILP Web or smart phone UAV guidance system Automatic control feedback Tracking UAV Customer Automatic replenishment Replenishment station

UAV Guidance System ■ Roles of UAV guidance system 1. Receive and implement the schedule from the MILP. 2. Convert the video from the UAV cameras into usable information for directing the motion of the UAVs 3. Enable a human overseer to monitor the UAV progress via video and adjust feedback control gain values for various situations 4. Allows for a human overseer to initiate emergency actions such as immediate landing.

UAV Guidance System ■ System components < AR drone 2.0 > 1280 720 pixel front camera 320 240 pixel belly camera < WIFI > < Ipad 3>

UAV Guidance System 1. The color video from the camera is acquired via TCP port and processed using OpenCV framework. 2. The image is separated into three RGB channels. These three images are used to determine the color of the targeted image. 3. Control inputs including the longitudinal-lateral tilt angles, height and yaw angular velocity are calculated from the number and mean coordinate of target pixels in the processed image.

UAV Guidance System ■ P-D gain controller block diagram

Automatic Replenishment Station Customer information UAV schedule Central planning by MILP Web or smart phone UAV guidance system Automatic control feedback Tracking UAV Customer Automatic replenishment Replenishment station

Automatic Replenishment Station ▪ Each AR Drone 2.0 uses a three cell lithium polymer battery ▪ four copper leads (three for each terminal and one for the ground terminal) were threaded from the battery inside the UAV to the four feet of the drone ▪ The service station consists of four pads, one for each foot of the drone. ▪ Each such pad connects to the UAV battery via the leads on the drone feet

System Demonstration: Layout ■ Demonstration description < Demonstration layout > Split job 8 Split job 7 < Schedule by MILP > Split job 2 Split job 1 ∙ ∙ ∙ 5m

System Demonstration: Video ■ Demonstration video

Concluding Remarks • Towards a persistent UAV service • Components of such a system • System orchestration MILP (deterministic customer paths) • Improved formulation • Reduced computational time • UAV guidance system • Vision for UAV localization relative to customer, location flags and platforms • Feedback control for UAV via iPad controller • Automatic replenishment stations (battery recharge) • Demonstration of proposed UAV service system • Future directions • Real time customer requests • Random customer behavior during service • Implementation outdoors • Improved UAV localization algorithms

Back up materials

Improvement : Efficient formulation • To enhance the cplex computational power, efficient mathematical formulation was developed • Delete unnecessary decision variable and dummy job concepts - Delete Yjkr decision variable because Cjkr decision variable can replace it. • ▪ Yikr = 1 if UAV k processes split job i during its rth flight; 0, otherwise. • ▪ Cikr is job i’s start time by UAV k during its rth flight or UAV k’s recharge start time at station i; • otherwise its value is 0.

Improvement : Efficient formulation • Delete the concept of dummy job which is used for idle UAVs by allowing direct flight from start(end)station to end(start) station Dummy job stations

Literature Review • Scheduling methods without a distance or time restriction • T. Shima and C. Schumacher, “Assignment of cooperating UAVs to simultaneous tasks using genetic algorithm,” In Proc. AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco, 2005 • J. Zeng, X. Yang L. Yang and G. Shen, “Modeling for UAV resource scheduling under mission synchronization,” Journal of Systems Engineering and Electronics, Vol. 21, No. 5, 2010, pp. 821-826 • Scheduling methods for limited flight duration • A. L. Weinstein and C. Schumacher, “UAV scheduling via the vehicle routing problem with time windows,” In Proc. AIAA Infotech@Aerospace 2007 Conference and Exhibit, Rohnert Park, California, 2007 • T. Shima, S. Rasmussen and D. Gross, “Assigning micro UAVs to task tours in an urban terrain,” IEEE Transactions on Control Systems Technology, Vol. 15, No. 4, 2007, pp. 601 – 612 • Y.S. Kim, D.W. Gu and I. Postlethwaite, “Real-time optimal mission scheduling and flight path selection, IEEE Transactions on Automatic Control, Vol. 52, No. 6, 2007, pp. 1119-1123. • B. Alidaee, H. Wang, and F. Landram, “A note on integer programming formulations of the real-time optimal scheduling and flight selection of UAVS,” IEEE Transactions of Control Systems Technology, Vol. 17, No. 4, 2009, pp.839-843 • Scheduling method for persistent UAV operation • M. Alighanbari and J. P. How, “Decentralized task assignment for unmanned aerial vehicle”, Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference 2005 seville, spain, december 12-15, 2005 • Battery recharge/exchange methods • J. How, thesis papers at MIT, 2005, 2007 • A.S. Kurt, B.H. Clarence, R.R. Johnhenri, D.W. Richardson, Z.H. White, Q. Elizabeth and G. Anouck, “Autonomous Battery Swapping System for Small-scale Helicopters”, 2010 IEEE International Conference on Robotics and Automation • R. Godzdanker, M. J. Rutherford and K. P. Valavanis, “ISLANDS: A self-leveling platform for autonomous miniature UAVs”, 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp 170-175 • A.O.S. Koji, K.F. Paulo and James R. Morrison, “Automatic battery replacement system for UAVs: Analysis and design” Journal of Intelligent and Robotic Systems, Special Issue on Unmanned Aerial Vehicles (Springer), a Special Volume on Selected Papers from ICUAS’11, Vol. 65, No. 1, pp. 563-586, January 2012. First published online September 9, 2011 • M. Valenti, D. Dale, J. P. How and D. P. de Farias, “Mission health management for 24/7 persistent surveillance operations”, AIAA Guidance, Navigation and Control Conference and Exhibit, 20-23 August 2007, Hilton Head, South Carolina

Persistent UAV Service: An Improved Scheduling Formulation and Prototypes of System Components