1,066 research outputs found
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
Social-aware robot navigation in urban environments
In this paper we present a novel robot navigation approach based on the so-called Social Force Model (SFM). First, we construct a graph map with a set of destinations that completely describe the navigation environment. Second, we propose a robot navigation algorithm, called social-aware navigation, which is mainly driven by the social-forces centered at the robot. Third, we use a MCMC Metropolis-Hastings algorithm in order to learn the parameters values of the method. Finally, the validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.Peer ReviewedPostprint (author’s final draft
Human aware robot navigation
Abstract. Human aware robot navigation refers to the navigation of a robot in an environment shared with humans in such a way that the humans should feel comfortable, and natural with the presence of the robot. On top of that, the robot navigation should comply with the social norms of the environment. The robot can interact with humans in the environment, such as avoiding them, approaching them, or following them. In this thesis, we specifically focus on the approach behavior of the robot, keeping the other use cases still in mind. Studying and analyzing how humans move around other humans gives us the idea about the kind of navigation behaviors that we expect the robots to exhibit. Most of the previous research does not focus much on understanding such behavioral aspects while approaching people. On top of that, a straightforward mathematical modeling of complex human behaviors is very difficult. So, in this thesis, we proposed an Inverse Reinforcement Learning (IRL) framework based on Guided Cost Learning (GCL) to learn these behaviors from demonstration. After analyzing the CongreG8 dataset, we found that the incoming human tends to make an O-space (circle) with the rest of the group. Also, the approaching velocity slows down when the approaching human gets closer to the group. We utilized these findings in our framework that can learn the optimal reward and policy from the example demonstrations and imitate similar human motion
Adaptive control for robot navigation in human environments based on social force model
In this paper, we introduce a novel control scheme based on the social force model for robots navigating in human environments. Social proxemics potential field is constructed based on the theory of proxemics and used to generate social interaction force for design of robot motion control. A combined kinematic/dynamic control is proposed to make the robot follow the target social force model, in the presence of kinematic velocity constraints. Under the proposed framework, given a specific social convention, robot is able to generate and modify its path smoothly without violating the proxemics constraints. The validity of the proposed method is verified through experimental studies using the V-rep platform
Motion Planning
Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms
Modelling Social Interaction between Humans and Service Robots in Large Public Spaces
With the advent of service robots in public places (e.g., in airports and shopping malls), understanding socio-psychological interactions between humans and robots is of paramount importance. On the one hand, traditional robotic navigation systems consider humans and robots as moving obstacles and focus on the problem of real-time collision avoidance in Human-Robot Interaction (HRI) using mathematical models. On the other hand, the behavior of a robot has been determined with respect to a human. Parameters for human-human interaction have been assumed and applied to interactions involving robots. One major limitation is the lack of sufficient data for calibration and validation procedures. This paper models, calibrates and validates the socio-psychological interaction of the human in HRIs among crowds. The mathematical model is an extension of the Social Force Model for crowd modelling. The proposed model is calibrated and validated using open source datasets (including uninstructed human trajectories) from the Asia and Pacific Trade Center shopping mall in Osaka (Japan).In summary, the results of the calibration and validation on the multiple HRIs encountered in the datasets show that humans react to a service robot to a higher extend within a larger distance compared to the interaction range towards another human. This microscopic model, calibration and validation framework can be used to simulate HRI between service robots and humans, predict humans' behavior, conduct comparative studies, and gain insights into safe and comfortable human-robot relationships from the human's perspective
Intention prediction for interactive navigation in distributed robotic systems
Modern applications of mobile robots require them to have the ability to safely and
effectively navigate in human environments. New challenges arise when these
robots must plan their motion in a human-aware fashion. Current methods
addressing this problem have focused mainly on the activity forecasting aspect,
aiming at improving predictions without considering the active nature of the
interaction, i.e. the robot’s effect on the environment and consequent issues such as
reciprocity. Furthermore, many methods rely on computationally expensive offline
training of predictive models that may not be well suited to rapidly evolving
dynamic environments.
This thesis presents a novel approach for enabling autonomous robots to navigate
socially in environments with humans. Following formulations of the inverse
planning problem, agents reason about the intentions of other agents and make
predictions about their future interactive motion. A technique is proposed to
implement counterfactual reasoning over a parametrised set of light-weight
reciprocal motion models, thus making it more tractable to maintain beliefs over the
future trajectories of other agents towards plausible goals. The speed of inference
and the effectiveness of the algorithms is demonstrated via physical robot
experiments, where computationally constrained robots navigate amongst humans
in a distributed multi-sensor setup, able to infer other agents’ intentions as fast as
100ms after the first observation.
While intention inference is a key aspect of successful human-robot interaction,
executing any task requires planning that takes into account the predicted goals and
trajectories of other agents, e.g., pedestrians. It is well known that robots
demonstrate unwanted behaviours, such as freezing or becoming sluggishly
responsive, when placed in dynamic and cluttered environments, due to the way in
which safety margins according to simple heuristics end up covering the entire
feasible space of motion. The presented approach makes more refined predictions
about future movement, which enables robots to find collision-free paths quickly
and efficiently.
This thesis describes a novel technique for generating "interactive costmaps", a
representation of the planner’s costs and rewards across time and space, providing
an autonomous robot with the information required to navigate socially given the
estimate of other agents’ intentions. This multi-layered costmap deters the robot from
obstructing while encouraging social navigation respectful of other agents’ activity.
Results show that this approach minimises collisions and near-collisions, minimises
travel times for agents, and importantly offers the same computational cost as the
most common costmap alternatives for navigation.
A key part of the practical deployment of such technologies is their ease of
implementation and configuration. Since every use case and environment is
different and distinct, the presented methods use online adaptation to learn
parameters of the navigating agents during runtime. Furthermore, this thesis
includes a novel technique for allocating tasks in distributed robotics systems,
where a tool is provided to maximise the performance on any distributed setup by
automatic parameter tuning. All of these methods are implemented in ROS and
distributed as open-source. The ultimate aim is to provide an accessible and efficient
framework that may be seamlessly deployed on modern robots, enabling
widespread use of intention prediction for interactive navigation in distributed
robotic systems
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