Learning to Drive Among Obstacles

This paper reports on an outdoor mobile robot that learns to avoid collisions by observing a human driver operate a vehicle equipped with sensors that continuously produce a map of the local environment. We have implemented steering control that models human behavior in trying to avoid obstacles while trying to follow a desired path. Here we present the formulation for this control system and its independent parameters, and then show how these parameters can be automatically estimated by observation of a human driver. We present results from experiments with a vehicle (both real and simulated) that avoids obstacles while following a prescribed path at speeds up to 4 m/sec. We compare the proposed method with another method based on principal component analysis, a commonly used learning technique. We find that the proposed method generalizes well and is capable of learning from a small number of examples

Authors

• Bradley Hamner
• Sebastian A. Scherer
• Sanjiv Singh

Keywords

• Mobile robots
• Humans
• Intelligent robots
• Automatic control
• Vehicle dynamics
• Vehicle detection
• Collision avoidance
• Orbital robotics
• Robot sensing systems
• Vehicle driving
• Mobile Robot
• Training Data
• Gradient Descent
• Optimal Control
• Angular Velocity
• Global Positioning System
• Simulated Annealing
• Learnable Parameters
• Inertial Measurement Unit
• Vehicle Position
• Obstacle Avoidance
• Random Guessing
• Laser Ranging
• High Frequency Oscillations
• Steering Angle
• Destination Point
• Training Segments
• Path Segment
• All-terrain Vehicle
• Simulated Paths
• Obstacle Location
• Speed Control
• Eigenvectors