optimization of aerial surveys using an algorithm inspired...
TRANSCRIPT
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João [email protected]
1st Workshop on Planning and Robotics (PlanRob) - 10/06/2013
Optimization of Aerial Surveys using an Algorithm Inspired in Musicians
Improvisation
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Index
1. Introduction
2. Problematic
3. Harmony Search algorithm
4. The m-CPP algorithm
5. Results achieved
6. Conclusions
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Introduction
• Goal:
– Compute trajectories for a fleet of mini aerial vehicles shipped with a digital camera subject to a set of
restrictions
– Mosaicking
• Applications
– Monitoring and inspections of Critical infrastructures
– Precision agriculture
• Projects:
– ROTOS (Multi-Robot System for Large Outdoor Infrastructures Protection. DPI 2010-17998)
– RHEA (Robot Fleets for Highly Effective Agriculture and Forestry Management. NMP-CP-IP 245986-2)
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Problematic
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?Full coverage
trajectories
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Harmony Search algorithm (I)Harmony Search algorithm (I)
• Basic concepts
• Soft computing, Meta-heuristic approach
• Inspired by the improvisation process of musicians
• Methodology
• Step 1: Initialization of the optimization problem
• Step 2: Initialization of the harmony memory (HM)
• Step 3: Improvisation a New Harmony from the HM set
• Step 4: Updating HM
• Step 5: Repeat steps 3 and 4 until the end criterion is satisfied
5/17Lee, K. and Z. Geem, 2005. A new meta-heuristic algorithm for continuous engineering optimization:
harmony search theory and practice. Comput. Methods Applied Mechanics Eng., 194: 3902-3933.
[Lee, K. and Z. Geem, 2005]
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Harmony Search algorithm (II)Harmony Search algorithm (II)
Step 1: Initialization of the optimization problem
Minimize F(x) subject to xi ∈
X
i , i = 1,2,...N
Where:
F(x) : Objective function
x : Set of each design variable (xi)
Xi : Set of the possible range of values for each design variable (a < Xi< b)
N : Number of design variables
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Harmony Search algorithm (III)Harmony Search algorithm (III)
Step 2: Initialization of the harmony memory (HM)
Generate random vectors
HMS: Harmony Memory Size
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Harmony Search algorithm (IV)Harmony Search algorithm (IV)
• Step 3: Improvisation a New Harmony from the HM set
• New harmony vector, x' = (x1', x
2',...,x
n' )
• Three rules:
Random selection
Memory consideration
HMCR: Harmony Memory Considering Rate
Pitch adjustment
PAR: Pitch Adjusting Rate
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Harmony Search algorithm (V)Harmony Search algorithm (V)
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Step 4: Updating HM
F(X') < F(X) ?
Step 5: Repeat steps 3 and 4 until the end criterion is satisfied
Stop criterion, Number of improvisations (NI)
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Step 1: Initialization of the optimization problem
– Employ HS algorithm to find the optimal coverage safe path
– Minimize J = J1+J
2
• Subject to
– x1 and x
i ,i = 1,...,N
– Decision variables
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The m-CPP algorithmThe m-CPP algorithm (I) (I)
X{j} = [x1,x
2,x
3,...,x
i-2,x
i-1,x
i],
i=1,...,N;
j=1,...,HMS
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The m-CPP algorithmThe m-CPP algorithm (II) (II)
Step 2: Initialization of the harmony memory (HM)
Generate candidate permutations
Random Breath Coverage algorithm
Numerical example: X{1} = [1,2,3,6,9,8,7,4,1]
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1 4 7
2 X 8
3 6 9
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The m-CPP algorithmThe m-CPP algorithm (III) (III)
Step 3: Improvisation a New Harmony from the HM set
Random selection
Memory consideration
HMCR: Harmony Memory Considering Rate
Pitch adjustment
PAR: Pitch Adjusting Rate
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The m-CPP algorithmThe m-CPP algorithm (IV) (IV)
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Step 4: Updating HM
J(X') < J(X) ?
Step 5: Repeat steps 3 and 4 until the end criterion is satisfied
Stop criterion
Number of improvisations
An admissible number of turns (a hypothesis)
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Results achieved (I)Results achieved (I)
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Heuristic approach [7] m-CPP approach
6.7%59%
12.5%
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Results achieved (II)Results achieved (II)
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Removing borders [9]
– Computing time• max 2 minutes per area
– Area coverage• Improved
– Cost• Improved for two
• Worsened for one
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ConclusionsConclusions
16/17
A novel approach to ACPP employing HS algorithm
– Improved previous approach
– Improved airspace safety
– Improved area coverage
Computation time an issue
• Large workspaces
• Divide to conquer
• Real time computing
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Grazie mille!