1. 1 Autonomous Vehicle Systems: Coordination and Collaboration Karl Hedrick UC Berkeley

2. 2 Control of groups of UAV/UGV by a single operator - low maintenance, high return Increase in mission complexity - ability to perform multiple tasks simultaneously - mapping of many locations at once - distributed surveillance of many locations - ability to track multiple targets moving in different directions Lower cost per vehicle and each vehicle is dispensable - losing one vehicle will not compromise the entire mission Benefits of UAV Collaboration 3. 3 ISR Applications Master/Slave Mode Would like to use UAVs for: Convoy Protection Provide local as well as over-the-horizon visual coverage Search & Rescue (SAR) - Assist in search using infrared (IR) camera while flying abreast with manned helicopter Perimeter Surveillance/Border Patrol Coordinated surveillance and target recognition and tracking. 4. 4 Multi-Agent Convoy Protection Centralized Control on Ground Collaboration between multiple UAVs assigned to Convoy Protection Task generation and assignment based on mission situation and UAV profiles Ongoing coordination/synchronization between roles High freq. look-ahead coverage zone UAV1UAV3 UAV2 Lon_left Lon_right Lateral 5. 5 In Action, cont 6. 6 C3UV Collaboration Software GOALS Transmit desired mission from user to agents Provide user with fused information from agents Decompose and assign tasks among agents in response to dynamic mission definition Accomplish tasks in an efficient and robust manner Agent in range of user Agent out of range User New tasks Cancel tasks Command station Mission state est. Mission state estimate 7. 7 Communication Infrastructure User New tasks Cancel tasks Command station Piccolo Groundstation Piccolo Autopilot PC104 Piccolo Autopilot PC104 900 MHz radio 2.4 GHz ethernet 8. 8 Mission Definition User defines the mission The agents define the tasks Philosophy The user specifies what he or she would like accomplished. The system decides how to do so efficiently. 9. 9 CSL: Enables Internet Tasking Collaborative Sensing Language (CSL): XML-based: Human Readable Can be integrated with multiple languages on multiple operating systems on multiple platforms (C++, Java, Windows, Safari, Internet Explorer, Firefox, iPhone, Nokia) Provides a standard for integration with 3rd parties (outside systems can operate with the CSL Web Server and can view the feedback in Google Earth and Falcon View) Applications where the human is too busy to do much except ask for ISR and to view the collected information. 10. 10 Agents (UAVs) Transition Logic: Governs transitions of tasks and subtasks Communication: Deconflicts plans and synchronizes information between agents vs. Planner(ex. path-planner): calculates cost, generates plan and chooses todo Low-level Controller (ex. waypoint tracker) )( ][ ],[ ],[ k k k yxk PCost PPlan T vvVelocty yxPosition AgentID X )( k kk PCost T AgentID M k Tk T 11. 11 Task-Point List Every process and each agent communicates primarily through the task- point list A task-point list exists for each task and is manipulated by each process to generate a desired mode/task/mission 1 2 3 4 12. 12 Task Allocation Given n UAVs and m tasks, how do we assign tasks to UAVs? Assume that each task is simply a point to be visited, with some time spent at that point. Neglect UAV turn rate constraints assume constant velocity For each UAV, let a tour be an ordered set of targets that it will visit Let the cost of tour be the total time required to complete. For a constant velocity UAV with no turn rate constraint, this time corresponds to distance. Often this is posed as an instance of the multiple traveling salesman problem 13. 13 Multiple Traveling Salesman The Multiple Traveling Salesman Problems focuses on minimizing total cost. For n UAVs, with the cost of a tour for UAV j = Tj Our problem differs: we should focus on minimizing the max cost of any tour Given that were working with constant velocity UAVs, the cost in fuel of having a UAV circle is the same as having it do some work. For our problem, this corresponds to a minimum clock time problem. This problem is often referred to as the min-max Vehicle Routing Problem. 14. 14 The Greedy Algorithm- Real Time In constructing a tour, let the UAV with the lowest cost function for its partial tour choose the next task. This algorithm leads to balanced tours among UAVs: all UAVs perform tours of roughly equal cost. For the min-max VRP, optimal solutions will contain tours balanced to within the maximum distance between any two tasks. This is a fast algorithm that creates balanced tours Sub-optimal 15. 20 Cooperative Control: We would like to consider the team optimization problem, in a distributed manner This is a hard problem, especially in real time. Can we still get good trajectories without solving the team optimization problem? Consider a greedy algorithm (little communication no negotiation): Choose U2 conditioned on U1 16. 21 Real Flight Data: 2 plane search (max area sweeping) Wind: 9 m/s SW 17. 22 Test Platforms: 1. Sig Rascal 110 airframe Balsa frame remote control aircraft kit with 110 wingspan Modifications: 32 cc gasoline engine with vibration isolation mounts Dual fuel tanks for 60 min flight time Carbon fiber reinforcement to support payload 26 lb takeoff weight Piccolo avionics system 18. 23 Bat IVs 19. 24 PC104 stack and payload tray PC104 with 700 MHz Pentium III processor 2 GB flash memory (16 GB on vision plane) Bidirectional 1 Watt amplifier for 802.11b communication Vibration isolating suspension Wireless analog video transmitter 20. 26 CIRPAS, Camp Roberts, CA Operated by the Naval Post Graduate School 21. 27 August ONR Demonstration 22. 29 The End 23. 30