Optimal Trajectory for Network Establishment of Remote UAVs –1–1 Prachya Panyakeow, Ran Dai,...
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Transcript of Optimal Trajectory for Network Establishment of Remote UAVs –1–1 Prachya Panyakeow, Ran Dai,...
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Optimal Trajectory for Network Establishment of Remote UAVs
Prachya Panyakeow, Ran Dai, and Mehran Mesbahi
American Control Conference June 2013
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Motivation• Cooperative control of multi-vehicle systems
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Motivation• Cooperative control of multi-vehicle systems
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Motivation• Cooperative control of multi-vehicle systems
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MotivationReconnaissance, surveillance, monitoring, imaging, data processing
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MotivationWhy disperse?• Coverage issue• Limit field of view
Why form a connected network?• Energy efficiency of a formation• Information sharing
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Outline• Background and Problem Formulation• Optimal path planning for target tree-graph connectivity
Control law (PMP with end-point manifold) Control sequence (Nonlinear Opt Control necessary conditions) Eliminate candidates (Geometry Estimation Method) Computational issues
• Nonlinear Programming Method• Pros/Cons for each approach• Future work
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Background and Problem Formulation• Objective: Find paths that bring scattered
UAVs into proximity to form a connected network at terminal time with minimum total control effort.
• Nonlinear Dynamics of Each UAVs:
• Assumptions: • UAVs are first far from each other• The connected network at final time is denoted
as graph• The mobile agents represent the vertex set
in the final connected network• The communication or relative sensing channel
represents edge set• The initial states are given as and terminal
time is given as
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• Elements in Adjacency matrix A are determined by edges of• Euclidean distance based connection:
• Laplacian Matrix ,• Network connectivity constraint:
Review and BackgroundRelated works: Spanos and Murray, 2004, Robust connectivity of networked vehicles. Zavlanos and Pappas, 2007, Maintaining connectivity of mobile networks Kim and Mesbahi, 2006, Maximizing the second smallest eigenvalue of a state-
dependent graph Laplacian. Dai, Maximoff, and Mesbani, 2012, Formation of connected network for
fractionated spacecraft
Euclidean distance
Based connection
d
1
0D
10
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UAV Network Establishment
Problem Formulation:
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UAV Network Establishment
Problem Formulation:
Logical Constraint
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UAV Network Establishment
Problem Formulation:
Logical Constraint
Nonlinear Constraint
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Optimal Target Tree-Graph Connectivity
Problem Formulation:
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Hamiltonian:
End-point Manifold:
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
PMP with End-Point Manifold
Control Law
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Dubin’s Problem
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Control Sequence
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Proposition 1: (Path-Synthesis)found from intermediate/final conditions
Control law/sequence Necessary conditions
5n-2 unknowns
Final Constraints
Intermediate Constraints
2n-2
n-1
n1
n
5n-2 nonlinear equations
Substitute
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Optimal Trajectories Candidates:
Candidates RLL-RRL Candidates RRL-RLL(Global Sol.)
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Geometry Estimation Method for eliminating the candidates
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Optimal Target Tree-Graph Connectivity
Direct Method: Solve the Nonlinear Optimal Control Problem
Geometry Estimation Method for eliminating the candidates
Direction to turn
Choose initial guess for
Solve forUsing proposition 1
Check result
Terminate
No
Yes
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Optimal Target Tree-Graph ConnectivityNonlinear Optimal Control with Geometry Estimation Method
Pros:• Optimal Solution• Provides the solution (switching time) for a given graph
Cons:• Computational issues (NP Hard)
• Number of final network configurations is exponential• Cayley’s Theorem: Number of distinct labeled trees on n agents is
• Global search of all tree graphs with five agents: 1~2 minutes• Global search of all tree graphs with eight agents: 1~2 days!• An efficient algorithm is required to approach the problem
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Nonlinear Programming MethodParameterized Optimization Problem:
Transform the original problem to
Using the same 3 segment bang-bang control scheme, as unknown
Same control law/sequence
• Relaxation of Logical On/Off Constraint• Relaxation of Connectivity Nonlinear Constraint
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Nonlinear Programming MethodModel of communication/relative sensing link:
The entry of weight adjacency matrix is assigned as
• An exponential function for power of communication link
Relaxation of the
logical constraints
The communication efficacy drops off continuously as the distance between the agents increases
d
1
0D
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NLP Method (Parameter Optimization Problem)Relaxation of connectivity constraints viaMatrix similarity transformation:
Original mixed integer problemwith nonlinear constraints
Semi-definite constraintNonlinear constraint
Parameter Optimization Problemwith semi-definite constraints
= small positive number to guaranteeThe weight network is connected
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Optimal Target-Tree Method NLP MethodVS
Scalability of the two methods
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Optimal Target-Tree Method NLP MethodVS
•Global Optimal Solution•Exact results for a given graph•Have to search for all possible graphs•Not scalable for large-scale systems
•Sub-Optimal Solution (NP Hard)•Base from the same bang-bang control law/sequence • Faster Convergence without going through all possible graph•Scalable for large-scale systems
Scalability of the two methods
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Future Work
• Vary UAV speed between stall/max• Optimal dispersion• Considering scenarios that combine
Optimal path planning for network connectivity Maintain the formation Collision Avoidance Optimal path planning for network dispersion