990 research outputs found
Data Driven Approach to Multi-Agent Low Level Behavior Generation in Medical Simulations
A multi-agent scenario generation framework is designed, implemented and evaluated in the context of a preventive medicine education virtual reality system with data collected from a sensor network at the University of Iowa Hospital. An agent in the framework is a virtual human that represents a healthcare worker. The agent is able to make certain decisions based on the information it gathers from its surroundings in the virtual environment. Distributed sensor networks are becoming very commonplace in public areas for public safety and surveillance purposes. The data collected from these sensors can be visualized in a multi-agent simulation. The various components of the framework include generation of unique agents from the sensor data and low level behaviors such as path determination, directional traffic flows, collision avoidance and overtaking. The framework also includes a facility to prevent foot slippage with detailed animations during the travel period of the agents. Preventive medicine education is the process of educating health care workers about procedures that could mitigate the spread of infections in a hospital. We built an application called the 5 Moments of Hand Hygiene that educates health care workers on the times they are supposed to wash their hands when dealing with a patient. The purpose of the application was to increase the compliance rates of this CDC mandated preventive measure in hospitals across the nation. A user study was performed with 18 nursing students and 5 full-time nurses at the Clemson University School of Nursing to test the usability of the application developed and the realism of the scenario generation framework. The results of the study suggest that the behaviors generated by the framework are realistic and believable enough for use in preventive medicine education applications
A motion planning method for simulating a virtual crowd
A model of motion planning for agent-based crowd simulation is one of the key techniques for simulating how an agent selects its velocity to move towards a given goal in each simulation time step. If there is no on-coming collision with other agents or obstacles around, the agent moves towards the designated goal directly with the desired speed and direction. However, the desired velocity may lead the agent to collide with other agents or obstacles, especially in a crowded scenario. In this case, the agent needs to adjust its velocity to avoid potential collisions, which is the main issue that a motion planning model needs to consider. This paper proposes a method for modelling how an agent conducts motion planning to generate velocity for agent-based crowd simulation, including collision detection, valid velocity set determination, velocity sampling, and velocity evaluation. In addition, the proposed method allows the agent to really collide with other agents. Hence, a rule-based model is applied to simulate how the agent makes a response and recovers from the collision. Simulation results from the case study indicate that the proposed motion planning method can be adapted to different what-if simulation scenarios and to different types of pedestrians. The performance of the model has been proven to be efficient
ADAPT: The Agent Development and Prototyping Testbed
We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. Our animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. Additionally, our navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a character’s animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model
Cooperative Avoidance Control-based Interval Fuzzy Kohonen Networks Algorithm in Simple Swarm Robots
A novel technique to control swarm robot’s movement is presented and analyzed in this paper. It allows a group of robots to move as a unique entity performing the following function such as obstacle avoidance at group level. The control strategy enhances the mobile robot’s performance whereby their forthcoming decisions are impacted by its previous experiences during the navigation apart from the current range inputs. Interval Fuzzy-Kohonen Network (IFKN) algorithm is utilized in this strategy. By employing a small number of rules, the IFKN algorithms can be adapted to swarms reactive control. The control strategy provides much faster response compare to Fuzzy Kohonen Network (FKN) algorithm to expected events. The effectiveness of the proposed technique is also demonstrated in a series of practical test on our experimental by using five low cost robots with limited sensor abilities and low computational effort on each single robot in the swarm. The results show that swarm robots based on proposed technique have the ability to perform cooperative behavior, produces minimum collision and capable to navigate around square shapes obstacles
Online Flocking Control of UAVs with Mean-Field Approximation
We present a novel approach to the formation controlling of aerial robot
swarms that demonstrates the flocking behavior. The proposed method stems from
the Unmanned Aerial Vehicle (UAV) dynamics; thus, it prevents any unattainable
control inputs from being produced and subsequently leads to feasible
trajectories. By modeling the inter-agent relationships using a pairwise energy
function, we show that interacting robot swarms constitute a Markov Random
Field. Our algorithm builds on the Mean-Field Approximation and incorporates
the collective behavioral rules: cohesion, separation, and velocity alignment.
We follow a distributed control scheme and show that our method can control a
swarm of UAVs to a formation and velocity consensus with real-time collision
avoidance. We validate the proposed method with physical and high-fidelity
simulation experiments.Comment: To appear in the proceedings of IEEE International Conference on
Robotics and Automation (ICRA), 202
Sim2real and Digital Twins in Autonomous Driving: A Survey
Safety and cost are two important concerns for the development of autonomous
driving technologies. From the academic research to commercial applications of
autonomous driving vehicles, sufficient simulation and real world testing are
required. In general, a large scale of testing in simulation environment is
conducted and then the learned driving knowledge is transferred to the real
world, so how to adapt driving knowledge learned in simulation to reality
becomes a critical issue. However, the virtual simulation world differs from
the real world in many aspects such as lighting, textures, vehicle dynamics,
and agents' behaviors, etc., which makes it difficult to bridge the gap between
the virtual and real worlds. This gap is commonly referred to as the reality
gap (RG). In recent years, researchers have explored various approaches to
address the reality gap issue, which can be broadly classified into two
categories: transferring knowledge from simulation to reality (sim2real) and
learning in digital twins (DTs). In this paper, we consider the solutions
through the sim2real and DTs technologies, and review important applications
and innovations in the field of autonomous driving. Meanwhile, we show the
state-of-the-arts from the views of algorithms, models, and simulators, and
elaborate the development process from sim2real to DTs. The presentation also
illustrates the far-reaching effects of the development of sim2real and DTs in
autonomous driving
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
A major challenge to deploying robots widely is navigation in human-populated
environments, commonly referred to as social robot navigation. While the field
of social navigation has advanced tremendously in recent years, the fair
evaluation of algorithms that tackle social navigation remains hard because it
involves not just robotic agents moving in static environments but also dynamic
human agents and their perceptions of the appropriateness of robot behavior. In
contrast, clear, repeatable, and accessible benchmarks have accelerated
progress in fields like computer vision, natural language processing and
traditional robot navigation by enabling researchers to fairly compare
algorithms, revealing limitations of existing solutions and illuminating
promising new directions. We believe the same approach can benefit social
navigation. In this paper, we pave the road towards common, widely accessible,
and repeatable benchmarking criteria to evaluate social robot navigation. Our
contributions include (a) a definition of a socially navigating robot as one
that respects the principles of safety, comfort, legibility, politeness, social
competency, agent understanding, proactivity, and responsiveness to context,
(b) guidelines for the use of metrics, development of scenarios, benchmarks,
datasets, and simulators to evaluate social navigation, and (c) a design of a
social navigation metrics framework to make it easier to compare results from
different simulators, robots and datasets.Comment: 43 pages, 11 figures, 6 table
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