1,151 research outputs found
Robot navigation in dense human crowds: Statistical models and experimental studies of human–robot cooperation
We consider the problem of navigating a mobile robot through dense human crowds. We begin by exploring a fundamental impediment to classical motion planning algorithms called the “freezing robot problem”: once the environment surpasses a certain level of dynamic complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. We argue that this problem can be avoided if the robot anticipates human cooperation, and accordingly we develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a “multiple goal” extension that models the goal-driven nature of human decision making. We validate this model with an empirical study of robot navigation in dense human crowds (488 runs), specifically testing how cooperation models effect navigation performance. The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 0.8 humans/m^2, while a state-of-the-art non-cooperative planner exhibits unsafe behavior more than three times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m^2. We also show that our non-cooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Learning a Group-Aware Policy for Robot Navigation
Human-aware robot navigation promises a range of applications in which mobile
robots bring versatile assistance to people in common human environments. While
prior research has mostly focused on modeling pedestrians as independent,
intentional individuals, people move in groups; consequently, it is imperative
for mobile robots to respect human groups when navigating around people. This
paper explores learning group-aware navigation policies based on dynamic group
formation using deep reinforcement learning. Through simulation experiments, we
show that group-aware policies, compared to baseline policies that neglect
human groups, achieve greater robot navigation performance (e.g., fewer
collisions), minimize violation of social norms and discomfort, and reduce the
robot's movement impact on pedestrians. Our results contribute to the
development of social navigation and the integration of mobile robots into
human environments.Comment: 8 pages, 4 figure
Simultaneous Prediction and Planning in Crowds using Learnt Models of Social Response
The ability of autonomous mobile robots to work alongside humans and animals in real world environments has the potential to revolutionise the way in which many routine and labour intensive tasks are completed. Whilst we are seeing increasing applications in controlled environments, such as traffic and warehousing, robots are still far from ubiquitous in everyday life.
In unstructured environments, such as agriculture or pedestrian crowds, where interactions between agents are not guided by infrastructure, there exist additional challenges that need to be overcome before we are likely to see the widespread adoption of mobile robots.
Safe navigation in shared environments requires the accurate perception of nearby individuals using a robot's on board sensors. Additionally, the future motion of detected individuals needs to be predicted both for collision avoidance and efficient navigation. These predictions should reflect the inherent uncertainty of the individual's future, including the ways in which an individual might respond to its neighbours, including the robot itself. As such, there exists a dependency between any prediction of an individual's motion and the planned path of the robot, which needs to be accounted for both during the prediction and planning stages of navigation.
This thesis focuses on how prediction and planning can be approached in a single framework to address this dependency, using learnt models of social response within a sampling based path planner for simultaneous prediction and planning (SPP). Additional challenges faced in navigating shared and unstructured environments are also addressed, including predicting the uncertain branching and multi-modal nature of agent motion during social interactions, and overcoming the on-board limitations of mobile robots --- such as resource and sensing constraints --- in order to achieve extended autonomy
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
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