Small Unmanned Systems Business Expo Presenter Karl Hedrick

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 UAV‟s 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)
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yxk
PCost
PPlan
T
vvVelocty
yxPosition
AgentID
X
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PCost
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AgentID
M
k
Tˆk
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 we‟re 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 IV’s

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