multi-vehicle cooperative control raffaello d’andrea mechanical & aerospace engineering...
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Multi-vehicle Cooperative ControlRaffaello D’Andrea
Mechanical & Aerospace Engineering Cornell University
Progress on RoboFlag Test-bed MLD approach to Multi-Vehicle Cooperation Obstacle Avoidance in Dynamic Environments Path Planning with Uncertainty
OUTLINE
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RoboFlag
An experimental platform for multi-vehicle cooperative control in
uncertain and adversarial environments
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ACTUATE
SENSE
LOW LEVELCONTROL
HIGH LEVELCONTROL
COMMS
VEHICLE
SENSE
COMMS
PROCESSINGGLOBALSENSING
COMMS
HIGH LEVELDECISION MAKING
CENTRALCONTROL
COMMUNICATIONS NETWORKCOMMS
COMMS COMMS
SYSTEMS OF INTEREST
HUMANINTERFACE
COMMS
COMMS
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What is RoboFlag?
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RoboFlag System
..
Overhead camerasVision computer
Arbiter
Computersfor each entity
...
RF transceiver
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Leverage RoboCup Technology
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SOFTWARE ARCHITECTURE
VEHICLEHIGH LEVEL
CONTROL
LOW LEVEL CONTROL INTERFACE
COMMUNICATIONS NETWORKSIMULATOR
WIRELESSINTERFACE
CENTRALCONTROL
MACHINE VISIONBASED
GLOBAL ANDLOCAL SENSING
VEHICLEHIGH LEVEL
CONTROL
ARBITER
VEHICLELOW LEVEL
CONTROL
VEHICLELOW LEVEL
CONTROL
LOCAL
GLOBAL
HUMANINTERFACE
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HARDWARE ARCHITECTURE
MACHINE VISIONCOMPUTER
INTERFACE ANDARBITRATION
COMPUTERWIRELESS
HARDWARE
CENTRAL CONTROLAND
COMMUNICATIONS NETWORKCOMPUTER
VEHICLE(S)HIGH LEVEL
CONTROLCOMPUTER
VEHICLE
VEHICLE
VEHICLE(S)HIGH LEVEL
CONTROLCOMPUTER
HUMAN INTERFACECOMPUTER
WIRELESSHARDWARE
LOCAL LOCAL
HARDWARE PORT
HARDWARE PORT
HUMAN INTERFACECOMPUTER
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SIMPLE COMMUNICATIONS NETWORK MODEL:
,
,
: communicating subsystem
: maximum bandwidth from to , data units/frame
: latency from to , frames
i
i j i j
i j i j
U i
B U U
L U U
Bi,j data unitsBi,j data units
buffer Li,j
Bi,j data units
buffer Li,j -1 buffer 0
Ui Uj
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RoboFlag Simulation Environment
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Hardware Environment
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People
Michael Babish (Research Support) Andrey Klochko (Programmer) JinWoo Lee (Post-Doc) 30 UG and M.Eng. students
Shared platform with DARPA MICA
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The RoboFlag Drill: Matt Earl
Defenders
( , ) ( )i i i id f d u u t U
( , ) ( )j j j ja g a v v t V Attackers
Constraints-defenders cannot enter defense zone
-no collisions
•Defender objective: pick u(t) to minimize number of attackers within defense zone at time T.•Attacker objective: pick v(t) to maximize number of attackers within defense zone at time T.
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Problem: Consider the RoboFlag Drill with the following assumptions
•irrational attackers (drones) •full and perfect information•collisions are ok•simple 2nd order defender dynamics
Model as a discrete time mixed logical dynamical (MLD) system (Bemporad & Morari 1999)
•linear dynamics•logical rules•inequality constraints
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Defenders
max max,
i i xi
i i yi
xi yi
x x u
y y u
u u u u
( ) ( ) ( ) ( )i i i ix b x b y b y b constraints (must not enter defense zone)
dynamics
intercept region
( ) {( , ) : ( ) ( )}i i iI t x y c x x c c y y c intercept region (dotted region in figure above)
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Attackersauxiliary binary variables 1 ( , )ij j j ix y I
1 ( , )j j jx y G
dynamics
( 1) ( ) ( )j j j jr t r t v a t
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Optimal control policy
we convert the system into MLD form using HYSDEL (Torrisi, Bemporad, and Mignone 2000)
,1
min ( )aN
ju
j
T
1 2
1 2 3 5
( 1) ( ) ( ) ( ), (0)
( ) ( ) ( )ox t Ax t B u t B t x x
E x t E u t E t E
min{ : }T
zf z Az b
this can be written as a mixed integer linear program (MILP)
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Results
• MILP problem: 4040 integer variables, 400 continuous variables,13580 constraints• CPLEX solves in 244 seconds on Linux PIII 866MHz
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Results
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Evaluation of this approach for cooperative control
Good
Bad
• easy to model complex systems• easy to formulate complex tasks via a cost function to minimize and constraints to be satisfied• handles very general constraints• codes available for solving mixed integer programs• gives optimal strategy
• computationally intensive• constraints only enforced at discrete times. • does not exploit structure
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A new approach to reduce computation
Exploit structure of the problem by considering the discrete and continuous parts separately.
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Form an intercept tree
- Prune intercept tree- Find feasible paths within tree
Exploit motion primitives
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Path Planning under Uncertainty:Myungsoo Jun
MAIN IDEAS:
• Construction of probability map from available data• Measurement data• A priori statistics of environments
• Convert the probability map to a directed graph• Path planning by solving shortest path problem in digraph
Capture uncertainty in information with a probabilistic approach
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Probability Map Building• Measurement update by measured data
• Time update by a priori statistics of environment
• Map building
sensor characteristics
environment statistics
From this can obtain probability distribution that at least one opponent is at a certain location
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Conversion to Digraph
1 2 3
4 5 6
7 8 9
0.02 0.015 0.01
0.013
0.001
0.02
0.02
0.015
0.015
0.0150.013
0.013
0.013
0.013
0.013
0.01
0.001
0.001
DigraphProbability Map
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Examples
Dynamic Replanning Case with Multiple Vehicles
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Simulation
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Contribution and Future Work
• Consideration of time and velocity in path planning for multiple vehicles• Consideration penalty for frequent acceleration and deceleration• Improving map building computation for real-time application
• Building a probability map in uncertain dynamic environments • Path planning of multiple vehicles in uncertain dynamic environments based on probability maps• Integrating map building and path planning
Contribution:
Future Work:
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Obstacle Avoidance in Dynamic Environments: Pritam Ganguly
Objective: Computationally fast algorithms for path planning in multi-agent adversarial environments with delayedinformation.
APPROACH•Game Theoretic: Avoiding a rational adversary in a delayedenvironment can be modeled as a non-cooperative imperfect information game . Trajectory generation is an outcome of such an approach.•Randomized Algorithm: This algorithm uses an existingtrajectory generation routine to generate feasible paths in the presence of obstacles. One way to incorporate the effect of delayis to associate with each obstacle a reachability regime over thedelayed steps. Frazzoli, Dahleh, and Feron
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Randomized Algorithm
Terminology:
•Primary Node:
An equilibrium configuration belonging to the state-space of theagent.
•Secondary Node:
An element of the state space ofthe agent which lies on the pathfrom the initial point to a primary node.
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Randomized Algorithm
Main Idea: (Frazzoli,Dahleh & Feron ‘00)
•The main idea is to search for random intermediate points in the state-space which might generate a feasible path to the destination. A feasible path being the one without any collisions.
•Among all the feasible paths the one with the lowest cost (eg. time) is then chosen.
•The underlying assumption in using this algorithm is that one already has a way of generating trajectories in the absence of obstacles.
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Randomized Algorithm
Feasible PathFeasible Pathfound, updatefound, update
costscosts
Initialize treeInitialize treewith starting positionwith starting position
Randomly generate primaryRandomly generate primary node and also a start pointnode and also a start pointfrom the already generatedfrom the already generated
treetree
Is Path(start,primary)Is Path(start,primary)feasiblefeasible
yesyes
Generate random Generate random secondary points andsecondary points andadd them to the treeadd them to the tree
Is Path(secondary,Is Path(secondary,destination) feasibledestination) feasible
yesyes
NoNo
NoNo
Main Idea and Implementation:
•This algorithm is probabilistically complete: with probability one, it returns a feasible path if there exists one.
•Instead of using a tree data structure, use a grid data structure which takes a large storage space but has faster access time.
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Future Work
• Implement the randomized algorithm framework for multiple agents in a centralized fashion, which would be a relatively easy extension to the present algorithm by increasing the state-space dimension.
• Develop a protocol enabling the decentralization of the above computation, and prove that the protocol achievesthe desired objective.
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Atif Chaudhry
Survey published research on multiple vehicle control
Investigating relationship between RoboFlag framework and desired military capabilities of autonomous vehicles