Neural Network Perception for Mobile Robot Guidance

1 Introduction.- 1.1 Problem Description.- 1.2 Robot Testbed Description.- 1.3 Overview.- 2 Network Architecture.- 2.1 Architecture Overview.- 2.2 Input Representations.- 2.3 Output Representation.- 2.4 Internal Network Structures.- 3 Training Networks “On-The-Fly”.- 3.1 Training with Simulated Data.- 3.2 Training “on-the-fly” with Real Data.- 3.3 Performance Improvement Using Transformations.- 3.4 Discussion.- 4 Training Networks With Structured Noise.- 4.1 Transitory Feature Problem.- 4.2 Training with Gaussian Noise.- 4.3 Characteristics of Structured Noise.- 4.4 Training with Structured Noise.- 4.5 Improvement from Structured Noise Training.- 4.6 Discussion.- 5 Driving Results and Performance.- 5.1 Situations Encountered.- 5.2 Driving with Alternative Sensors.- 5.3 Quantitative Performance Analysis.- 5.4 Discussion.- 6 Analysis of Network Representations.- 6.1 Weight Diagram Interpretation.- 6.2 Sensitivity Analysis.- 6.3 Discussion.- 7 Rule-Based Multi-network Arbitration.- 7.1 Symbolic Knowledge and Reasoning.- 7.2 Rule-based Driving Module Integration.- 7.3 Analysis and Discussion.- 8 Output Appearance Reliability Estimation.- 8.1 Review of Previous Arbitration Techniques.- 8.2 OARE Details.- 8.3 Results Using OARE.- 8.4 Shortcomings of OARE.- 9 Input Reconstruction Reliability Estimation.- 9.1 The IRRE Idea.- 9.2 Network Inversion.- 9.3 Backdriving the Hidden Units.- 9.4 Autoencoding the Input.- 9.5 Discussion.- 10 Other Applications – The SM2.- 10.1 The Task.- 10.2 Network Architecture.- 10.3 Network Training and Performance.- 10.4 Discussion.- 11 Other Vision-based Robot Guidance Methods.- 11.1 Non-learning Autonomous Driving Systems.- 11.2 Other Connectionist Navigation Systems.- 11.3 Other Potential Connectionist Methods.- 11.4 Other MachineLearning Techniques.- 11.5 Discussion.- 12 Conclusion.- 12.1 Contributions.- 12.2 Future Work.