Autonomous navigation for guide following in crowded indoor environments

The requirements for assisted living are rapidly changing as the number of elderly patients over the age of 60 continues to increase. This rise places a high level of stress on nurse practitioners who must care for more patients than they are capable. As this trend is expected to continue, new technology will be required to help care for patients. Mobile robots present an opportunity to help alleviate the stress on nurse practitioners by monitoring and performing remedial tasks for elderly patients. In order to produce mobile robots with the ability to perform these tasks, however, many challenges must be overcome. The hospital environment requires a high level of safety to prevent patient injury. Any facility that uses mobile robots, therefore, must be able to ensure that no harm will come to patients whilst in a care environment. This requires the robot to build a high level of understanding about the environment and the people with close proximity to the robot. Hitherto, most mobile robots have used vision-based sensors or 2D laser range finders. 3D time-of-flight sensors have recently been introduced and provide dense 3D point clouds of the environment at real-time frame rates. This provides mobile robots with previously unavailable dense information in real-time. I investigate the use of time-of-flight cameras for mobile robot navigation in crowded environments in this thesis. A unified framework to allow the robot to follow a guide through an indoor environment safely and efficiently is presented. Each component of the framework is analyzed in detail, with real-world scenarios illustrating its practical use. Time-of-flight cameras are relatively new sensors and, therefore, have inherent problems that must be overcome to receive consistent and accurate data. I propose a novel and practical probabilistic framework to overcome many of the inherent problems in this thesis. The framework fuses multiple depth maps with color information forming a reliable and consistent view of the world. In order for the robot to interact with the environment, contextual information is required. To this end, I propose a region-growing segmentation algorithm to group points based on surface characteristics, surface normal and surface curvature. The segmentation process creates a distinct set of surfaces, however, only a limited amount of contextual information is available to allow for interaction. Therefore, a novel classifier is proposed using spherical harmonics to differentiate people from all other objects. The added ability to identify people allows the robot to find potential candidates to follow. However, for safe navigation, the robot must continuously track all visible objects to obtain positional and velocity information. A multi-object tracking system is investigated to track visible objects reliably using multiple cues, shape and color. The tracking system allows the robot to react to the dynamic nature of people by building an estimate of the motion flow. This flow provides the robot with the necessary information to determine where and at what speeds it is safe to drive. In addition, a novel search strategy is proposed to allow the robot to recover a guide who has left the field-of-view. To achieve this, a search map is constructed with areas of the environment ranked according to how likely they are to reveal the guide’s true location. Then, the robot can approach the most likely search area to recover the guide. Finally, all components presented are joined to follow a guide through an indoor environment. The results achieved demonstrate the efficacy of the proposed components

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL

A mosaic of eyes

Autonomous navigation is a traditional research topic in intelligent robotics and vehicles, which requires a robot to perceive its environment through onboard sensors such as cameras or laser scanners, to enable it to drive to its goal. Most research to date has focused on the development of a large and smart brain to gain autonomous capability for robots. There are three fundamental questions to be answered by an autonomous mobile robot: 1) Where am I going? 2) Where am I? and 3) How do I get there? To answer these basic questions, a robot requires a massive spatial memory and considerable computational resources to accomplish perception, localization, path planning, and control. It is not yet possible to deliver the centralized intelligence required for our real-life applications, such as autonomous ground vehicles and wheelchairs in care centers. In fact, most autonomous robots try to mimic how humans navigate, interpreting images taken by cameras and then taking decisions accordingly. They may encounter the following difficulties

A robot swarm assisting a human fire-fighter

Emergencies in industrial warehouses are a major concern for fire-fighters. The large dimensions, together with the development of dense smoke that drastically reduces visibility, represent major challenges. The GUARDIANS robot swarm is designed to assist fire-fighters in searching a large warehouse. In this paper we discuss the technology developed for a swarm of robots assisting fire-fighters. We explain the swarming algorithms that provide the functionality by which the robots react to and follow humans while no communication is required. Next we discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also the means to locate the robots and humans. Thus, the robot swarm is able to provide guidance information to the humans. Together with the fire-fighters we explored how the robot swarm should feed information back to the human fire-fighter. We have designed and experimented with interfaces for presenting swarm-based information to human beings