Spatio-Temporal Case-Based Reasoning for Efficient Reactive Robot Navigation

Abstract

This paper presents an approach to automatic selection and modification of behavioral assemblage parameters for autonomous navigation tasks. The goal of this research is to make obsolete the task of manual configuration of behavioral parameters, which often requires significant knowledge of robot behavior and extensive experimentation, and to increase the efficiency of robot navigation by automatically choosing and finetuning the parameters that fit the robot task-environment well in real-time. The method is based on the Case-Based Reasoning paradigm. Derived from incoming sensor data, this approach computes spatial features of the environment. Based on the robot’s performance, temporal features of the environment are then computed. Both sets of features are then used to select and finetune a set of parameters for an active behavioral assemblage. By continuously monitoring the sensor data and performance of the robot, the method reselects these parameters as necessary. While a mapping from environmental features onto behavioral parameters, i.e., the cases, can be hard-coded, a method for learning new and optimizing existing cases is also presented. This completely automates the process of behavioral parameterization. The system was integrated within a hybrid robot architecture and extensively evaluated using simulations and indoor and outdoor real world robotic experiments in multiple environments and sensor modalities, clearly demonstrating the benefits of the approach.