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Robótica Móvel Inteligente

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Presentation on theme: "Robótica Móvel Inteligente"— Presentation transcript:

1 Robótica Móvel Inteligente
4/1/2017 Research in Intelligent Mobile Robotics (and related topics) Part 1: Navigation and Vision Anna Helena Reali Costa Laboratório de Técnicas Inteligentes Escola Politécnica da Universidade de São Paulo Carlos Henrique Costa Ribeiro Divisão de Ciência da Computação Instituto Tecnológico de Aeronáutica Anna Helena Reali Costa

2 Preface This is a two-part talk about the research on Intelligent Mobile Robotics and related topics at: LTI – USP (Laboratório de Técnicas Inteligentes – Universidade de São Paulo, Brazil) NCROMA-ITA (Laboratório de Navegação e Controle de Robôs Móveis Autônomos – Instituto Tecnológico de Aeronáutica, Brazil). These research groups are involved in the MultiBot cooperation project CAPES/GRICES with ISLab-IST. MultiBot - Meeting #1, Lisboa part I

3 LTI - EPUSP Prof. Anna Reali 5 PhD Students 3 Master Students
Alexandre Simões*, Reinaldo Bianchi, Valdinei Silva*, Valguima Odakura, Waldemar Bonventi. 3 Master Students Alexandre Cunha*, Antônio Selvatici, Luiz Carlos Maia 3 Undergraduate Students Rafael Pacheco, Márcio Seixas, Júlio Kawai 2 Final Course Projects MultiBot - Meeting #1, Lisboa part I

4 NCROMA – ITA Prof. Carlos Ribeiro 1 PhD Student 5 Master Students
Letícia Friske 5 Master Students Luís Almeida, Ricardo Maia, Juliano Pereira, Esther Colombini*, Celeny Alves* 2 Undergraduate Students Lucas Gabrielli, Fábio Miranda MultiBot - Meeting #1, Lisboa part I

5 Intelligent Mobile Robotics
Contents Intelligent Mobile Robotics Part 1: Navigation Map building Localization Perception Computer Vision Part II: Learning MultiBot - Meeting #1, Lisboa part I

6 Map Building Our goal: Test map building algorithms in real robots
fast enough? precise enough? ok for learning applications (e.g. path learning)? MultiBot - Meeting #1, Lisboa part I

7 Map Building Sildomar Takahashi, Carlos Ribeiro Roberto Barra, Ricardo Domenecci, Anna Reali
“Efficient Learning of Variable-Resolution Cognitive Maps for Autonomous Indoor Navigation”. Arleo, Millan e Floreano, IEEE-SMC, 1999. Advantages: Complete algorithmic description Simple structure Limitations: Assumes structure (orthogonal obstacles / walls) Reliance on dead-reckoning (but can be adapted to more sophisticated localization) MultiBot - Meeting #1, Lisboa part I

8 The Basic Algorithm Explores environment;
Once an obstacle is detected: Determines obstacle frontiers either via: An a priori sensor model A pre-trained neural net Includes obstacle in the global map; Defines new partition to explore. If there is none, END. Else finds route to new partition. Executes route and explores new partition. Once an obstacle is detected, Step 2. Else, Step 1. MultiBot - Meeting #1, Lisboa part I

9 Detection of obstacle frontiers
y x Janela My Mx Either a priori model or neural net model Integration over time Straight-line adjustment and correction (according to a priori actuator model) Robô Células Ocupadas Reta Calculada MultiBot - Meeting #1, Lisboa part I

10 Very “Scientific” Set-up
Walls Obstacles Robot 3 x 3,5 m MultiBot - Meeting #1, Lisboa part I

11 Global Maps: Magellan, Neural net model
MultiBot - Meeting #1, Lisboa part I

12 Global Map: Pioneer, a priori model + straight-line model-based correction
MultiBot - Meeting #1, Lisboa part I

13 Conclusions Tested algorithm (possibly with some modifications) is a good compromise efficiency/precision for realistic applications: fast yet fairly accurate. Next steps: Studies on simultaneous localization and mapping (SLAM algorithms). Valguima Odakura (Anna Reali): SLAM based on visual landmarks. Fabio Miranda (Carlos Ribeiro): Bayesian landmark learning. Techniques for map building acceleration. MultiBot - Meeting #1, Lisboa part I

14 Markov Localization Luís Almeida, Carlos Ribeiro Júlio Kawai, Anna Reali
Position estimation based on Bayesian update: Belief update based on sensor info Belief update based on action info Sensor/actuator models and initial belief distribution: arbitrary. Simple to implement. Computationally costly (Monte Carlo implementation – particle filters – is a possible fix). MultiBot - Meeting #1, Lisboa part I

15 Markov Localization st Sensor Model Markov State Estimator at
Probabilistic position grid Action Model Sensor Model Markov State Estimator pt st at pt+1 odometers sonars MultiBot - Meeting #1, Lisboa part I

16 Monte Carlo Localization + GA Optimization Luís Almeida, Carlos Ribeiro
GA on population of particles (fitness as combination of belief / particle cluster distribution) GA on population of particles (fitness as combination of belief / particle cluster distribution) MC GA MC GA MC Standard Markov update (over set of particles) Standard Markov update (over set of particles) Standard Markov update (over set of particles) Basic idea: use GA to create a better set of particles for next MC update. Initial results: ok (in need of statistical validation). MultiBot - Meeting #1, Lisboa part I

17 Next Steps Validation of GA approach.
Better sensor and actuator models. Implementation in a real robot. Literature on Monte Carlo methods (applications on signal detection and tracking): many variations to be tried... MultiBot - Meeting #1, Lisboa part I

18 Computational Vision Image Segmentation Using Color Classification
Using Background Model Using Optical Flow Based on Binocular Stereo Vision MultiBot - Meeting #1, Lisboa part I

19 Color Classification - I
Robótica Móvel Inteligente 4/1/2017 Color Classification - I Using threshold values: In the color representation space Neural Network – MLP + backpropagation alg.: Alexandre Simões, Anna Reali MultiBot - Meeting #1, Lisboa part I Anna Helena Reali Costa

20 Derived Application: Alexandre Simões, Anna Reali
Orange Classifier - CEAGESP, SP C C C C C5 Orange Classifier Branco . Verde Claro Verde Escuro Amarelo Laranja Claro Laranja Escuro R G B Danifi cado MultiBot - Meeting #1, Lisboa part I

21 Non-supervised iterative fuzzy color classification
Waldemar Bonventi, Anna Reali For number of clusters = 2 to Cmax, do: Apply FCM-GK (non-supervised fuzzy classifier) to RGB image; Calculate the ratio c/s for each cluster set: c = Cluster dimension/number of members s = Separation among clusters Choose the cluster set, based on c/s. Show color classification result for the best cluster set. MultiBot - Meeting #1, Lisboa part I

22 An example: soccer The best cluster set  6 clusters
MultiBot - Meeting #1, Lisboa part I

23 Another example: Rio de Janeiro
The best cluster set  3 clusters MultiBot - Meeting #1, Lisboa part I

24 Computational Vision Image Segmentation Using Color Classification
Using Background Model Using Optical Flow Based on Binocular Stereo Vision MultiBot - Meeting #1, Lisboa part I

25 Background Model - I  Model can not adapt to environment changes!
Background subtraction Thresholding the error between an estimate of the image without moving objects – M(C) – and the current image:  Model can not adapt to environment changes! M(C) Current Image MultiBot - Meeting #1, Lisboa part I

26 Background Model – II Márcio Seixas, Anna Reali
Time-Adaptive, Per-Pixel Mixture-of-Gaussians: Time series of observations at a given pixel (its color) is modeled by a mixture-of-gaussians. Based on the persistence and the variance of each of the gaussians of the mixture, it is determined which gaussians may correspond to background colors. Hypothesis: gaussian distributions with low variance and high persistence correspond to background model. Per-pixel models are updated as new observations are obtained (according to a learning rate).  It is capable of dealing with long-term scene changes (e.g. lighting changes)! MultiBot - Meeting #1, Lisboa part I

27 Derived Application: platform occupancy
Terminal Rodoviário de Santo Amaro TRENDS & Prefeitura de São Paulo Márcio Seixas, Anna Reali Fixed model M(C): Original: Original – M(C): Adaptive Model: MultiBot - Meeting #1, Lisboa part I

28 Computational Vision Image Segmentation Using Color Classification
Using Background Model Using Optical Flow Based on Binocular Stereo Vision MultiBot - Meeting #1, Lisboa part I

29 Optical Flow - idea MultiBot - Meeting #1, Lisboa part I

30 Vision-based robotic behavior: Antonio Selvatici, Anna Reali
Robot (with camera) navigating in a stationary scenario. Calculation of the optical-flow divergent to estimate the time-to-crash value in order to avoid collisions with obstacles. We are now investigating a robust method to directly calculate the per-pixel time-to-crash value: Original sequence Pixel time-to-crash Filtered values Gray levels: near bright; far  dark Black: unknown distance MultiBot - Meeting #1, Lisboa part I

31 Derived Application: monitoring of underground rail tracks Luiz Maia, Anna Reali
ALSTOM & Metrô de São Paulo MultiBot - Meeting #1, Lisboa part I

32 Computational Vision Image Segmentation Using Color Classification
Using Background Model Using Optical Flow Based on Binocular Stereo Vision MultiBot - Meeting #1, Lisboa part I

33 Binocular Stereo Vision Rafael Pacheco, Anna Reali
Distance-Map Calculation Calibration [Zhang, ICCV 99] Matching – blob coloring+centroid+correlation Triangulation Segmentation: based on color + distance-map. MultiBot - Meeting #1, Lisboa part I

34 Derived Application: Outdoors Measurement
TRENDS & Prefeitura de São Paulo Rafael Pacheco, A. Reali MultiBot - Meeting #1, Lisboa part I

35 Conclusions In CV, we are now investigating:
Automatic learning of fuzzy color classifiers – Waldemar Bonventi, LTI; A framework for high-level feedback to adaptive, per-pixel, mixture-of-gaussian background models – Márcio Seixas, LTI; Mathematical formulation for direct and robustly calculate the per-pixel, time-to-crash values, considering a moving observer in a stationary scenario – Antonio Selvatici, LTI; Distributed, real-time approach to calculate the optical flow, considering a stationary observer in a dynamic scenario – Luiz Maia, LTI. MultiBot - Meeting #1, Lisboa part I


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