SLIDE 1 Exploração direcionada
Algoritmo
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Ativa e obtém as leituras dos sensores;
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Realiza a atualização local do mapa;
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Atualiza o atributo potencial das células da região visitada;
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Determina a preferência das regiões do ambiente e associa um valor de distorção às células da região.
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Calcula o vetor gradiente descendente da posição do robô;
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Desloca-se seguindo a direção definida por este gradiente;
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Repete o processo até que todo o ambiente esteja completamente explorado.
SLIDE 2
Exploração direcionada
Ambientes de Teste
SLIDE 3
Exploração direcionada
Com preferência Sem preferência
SLIDE 4
Exploração direcionada
Ambientes de Teste Numeros de Passos Numeros de Visitas
SLIDE 5
Exploração direcionada
Com preferência Sem preferência
SLIDE 6
Exploração direcionada
Numeros de Passos Numeros de Visitas Ambientes de Teste
SLIDE 7
Exploração direcionada
Ambiente Simulado SLAM + Exploração Gulosa SLAM + Exploração Integrada
SLIDE 8
Exploração direcionada
Ambiente Simulado SLAM + Exploração Gulosa SLAM + Exploração Integrada
SLIDE 9
Planejador BVP
SLIDE 10
Planejador BVP
SLIDE 11 Hierarchical BVP
l Combination of BVP Path Planning and the Full Multigrid
method (FMG) [9].
l FMG solves PDE through a combination of solutions at
several resolution levels.
SLIDE 12
Hierarchical BVP
(1) (2) Considering the error of approximation
Ae = −A˜ p
and using eq. (2), we obtain where r is the residual and defined by The error is relaxed and used to correct the potential Assuming the operator , eq. 1 becomes
SLIDE 13
Hierarchical BVP
SLIDE 14
Hierarchical BVP
SLIDE 15
Hierarchical BVP
Operators: Restriction (R) Prolongation (P) Full weighting restriction Bilinear interpolation
SLIDE 16
Hierarchical BVP
Level 0 33 x 33
SLIDE 17
Hierarchical BVP
Level 0 33 x 33 Solves the coarsest level
SLIDE 18
Hierarchical BVP
Level 0 33 x 33 The robot starts the navigation in this level
SLIDE 19
Hierarchical BVP
prolongs the potential Level 0 Level 1 33 x 33 65 x 65
SLIDE 20
Hierarchical BVP
Level 0 Level 1 33 x 33 65 x 65 restricts the residual
SLIDE 21
Hierarchical BVP
Level 0 Level 1 33 x 33 65 x 65 restricts the residual Compute the error approximation
SLIDE 22
Hierarchical BVP
prolongs the error and updates the potential Level 0 Level 1 33 x 33 65 x 65 restricts the residual
SLIDE 23
Hierarchical BVP
prolongs the error and updates the potential Level 0 Level 1 33 x 33 65 x 65 restricts the residual The robot can navigate using the potential field at this level
SLIDE 24
Hierarchical BVP
prolongation prolongs the potential Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129
SLIDE 25
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restricts the residual
SLIDE 26
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restricts the residual restricts the residual
SLIDE 27
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restricts the residual restricts the residual Compute the error approximation
SLIDE 28
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restricts the residual restricts the residual prolongs and update the error
SLIDE 29
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restricts the residual restricts the residual prolongs and update the error prolongs and update the error
SLIDE 30
Hierarchical BVP
Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 Update de potential. The robot can use the highest resolution grid to navigate
SLIDE 31
Hierarchical BVP
prolongation prolongation Level 0 Level 1 Level 2 33 x 33 65 x 65 129 x 129 restriction restriction restriction
SLIDE 32
Hierarchical BVP
SLIDE 33
Hierarchical BVP
17x17 129 x129 Navigation switching the grids
SLIDE 34
Hierarchical BVP
17x17 129 x129 Navigation switching the grids
SLIDE 35
Hierarchical BVP
17x17 129 x129 Navigation switching the grids
SLIDE 36
Hierarchical BVP
Resolution Time (seco me (seconds) HBVP PP BVP PP (SOR) BVP PP (GS) A* 9 x 9 2.29 x 10-5 2.04 x 10-3 2.01x10-3 6.58 x 10-5 17 x 17 2.37 x 10-4 2.10 x 10-3 3.61 x 10-3 2.10 x 10-4 33 x 33 1.24 x 10-3 5.52 x 10-3 3.11 x 10-2 5.57 x 10-4 65 x 65 1.51 x 10-2 3.53 x 10-2 4.88 x 10-1 1.70 x 10-3 129 x 129 2.64 x 10 -2 2.90 x 10-1 7.94 5.36 x10-3 257 x 257 2.39 x 10-1 2.56 130.32 1.95 x 10-2
SLIDE 37 BIBLIOGRAFIA
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[7] Prestes, E., Idiart, M. Sculpting Potential Fields in the BVP Path Planner. IEEE International Conference on Robotics and Biomimetics, 2009.
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[8] Prestes, E. Idiart, M. Computing Navigational Routes in Inhomogeneous Environments using BVP Path Planner. IEEE/RSJ International Conference on Robotics and Systems, 2010.
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[9] Silveira, R. , Prestes, E. Nedel, L. Fast Path Planning using Multi-Resolution Boundary Value Problems. IEEE/RSJ International Conference on Robotics and Systems, 2010.
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[10] Prestes, E. Engel, P. Exploration driven by Local Potential Distortions. Submetido ao IEEE/RSJ International Conference on Robotics and Systems, 2011.
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[11] Stachniss, C., Grisetti, G., Burgard, W. Information Gain-based Exploration using Rao-Blackwellized Particle Filters. Proc. of Robotics: Science and Systems (RSS), 2005.
