Socially compliant mobile robot navigation

The range of applications for robots currently broadens from performing repetitive, industrial tasks to flexible services in the same environment with humans. Nowadays, vacuum cleaning robots are already widely used domestic robots and we expect more and more services from mobile robotic platforms in the future. Robots that move in our vicinity should navigate in a socially compliant way that does not unnecessarily hinder nearby humans. Traditional path planning methods that compute time-optimal paths for the robot are therefore not always desirable. Instead, robots should understand and comply with social norms that allow the natural navigation behavior of humans. We belief that a robot that has a better understanding of this human-like behavior is more predictable and better accepted by humans. In this thesis, we present a novel navigation scheme for mobile robots that actively integrates the natural navigation behavior of pedestrians into planning, instead of considering nearby humans as passive obstacles. The robot performs this reasoning in a predictive manner, which gives it the possibility to adapt its behavior early in an encounter to enable smooth evasive movements in contrast to abrupt collision avoidance maneuvers. In particular, we present a novel model of the cooperative, interactive navigation behavior of multiple agents. A mobile robot can use such a model to predict the navigation behavior of nearby pedestrians, and at the same time to plan socially compliant paths to its target location. The intention-driven model captures goal-directed behavior, i.e. the intention to reach a target efficiently but as comfortably as possible. To this end, we use features that model important properties of the navigation behavior such as the time to reach the target, velocities, and the proximity to obstacles. In addition to these continuous properties, our model reasons about discrete navigation decisions such as evading obstacles on the left, or on the right, which we capture in terms of topological classes of trajectories. During navigation, the robot maintains a probability distribution over trajectories in these classes, and chooses the socially most compliant one for navigation. This enables the robot to quickly react to sudden changes in its environment. The contribution of this thesis is a novel model of the interactive navigation behavior of multiple agents and its application to mobile robot navigation. In addition to socially compliant navigation in the presence of humans, our methods enable shared-autonomy navigation and socially compliant leader following. To allow a robot to observe the behavior of humans it has to interact with, we present an online skeleton tracking method. In addition, we present a learning-from-demonstration approach for autonomous cars that is based on our model. In extensive experiments, carried out in simulation and with real robots, we evaluated the methods proposed throughout this thesis. These experiments suggest that our algorithms outperform traditional path planning methods and that our techniques are applicable to navigate mobile robots in a socially compliant way.