125 research outputs found
Human–agent collaboration for disaster response
In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked
Multiagent Teamwork: Hybrid Approaches
Conference paper published in CSI Communications</p
A systematic literature review of multi-agent pathfinding for maze research
Multi-agent Pathfinding, also known as MAPF, is
an Artificial Intelligence problem-solving. The aim is to
direct each agent to find its path to reach its target, both
individually and in groups. Of course, this path allows agents
to move without colliding with each other. This MAPF
application is implemented in many areas that require the
movement of various agents, such as warehouse robots,
autonomous cars, video games, traffic control, Unmanned
Aerial Vehicles (UAV), Search and Rescue (SAR), many
others. The use of multi-agent in exploring often assumes all
areas to be explored are free of obstructions. However, the
use of MAPF to achieve their goals often faces static barriers,
and even other agents can also be considered dynamic
barriers. Because it requires some constraints in the program,
such as agents cannot collide with each other. The use of
single-agent can find the shortest path through exploration.
Still, multi-agent cooperation should shorten the time to find
a target location, especially if there is more than one target.
This paper explains the Systematic Literature Review (SLR)
method to review research on various multi-agent
pathfinding. The contribution of this paper is the analysis of
multi-agent pathfinding and its potential application in
solving maze problems based on an SLR
A Reliable and Low Latency Synchronizing Middleware for Co-simulation of a Heterogeneous Multi-Robot Systems
Search and rescue, wildfire monitoring, and flood/hurricane impact assessment
are mission-critical services for recent IoT networks. Communication
synchronization, dependability, and minimal communication jitter are major
simulation and system issues for the time-based physics-based ROS simulator,
event-based network-based wireless simulator, and complex dynamics of mobile
and heterogeneous IoT devices deployed in actual environments. Simulating a
heterogeneous multi-robot system before deployment is difficult due to
synchronizing physics (robotics) and network simulators. Due to its
master-based architecture, most TCP/IP-based synchronization middlewares use
ROS1. A real-time ROS2 architecture with masterless packet discovery
synchronizes robotics and wireless network simulations. A velocity-aware
Transmission Control Protocol (TCP) technique for ground and aerial robots
using Data Distribution Service (DDS) publish-subscribe transport minimizes
packet loss, synchronization, transmission, and communication jitters. Gazebo
and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP
protocols tested our ROS2-based synchronization middleware for packet loss
probability and average latency. A thorough ablation research replaced NS-3
with EMANE, a real-time wireless network simulator, and masterless ROS2 with
master-based ROS1. Finally, we tested network synchronization and jitter using
one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on
different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our
middleware shows that a large-scale IoT infrastructure with a diverse set of
stationary and robotic devices can achieve low-latency communications (12% and
11% reduction in simulation and real) while meeting mission-critical
application reliability (10% and 15% packet loss reduction) and high-fidelity
requirements
HUMAN CONTROL OF COOPERATING ROBOTS
Advances in robotic technologies and artificial intelligence are allowing robots to emerge fromresearch laboratories into our lives. Experiences with field applications show that we haveunderestimated the importance of human-robot interaction (HRI) and that new problems arise inHRI as robotic technologies expand. This thesis classifies HRI along four dimensions - human,robot, task, and world and illustrates that previous HRI classifications can be successfullyinterpreted as either about one of these elements or about the relationship between two or moreof these elements. Current HRI studies of single-operator single-robot (SOSR) control andsingle-operator multiple-robots (SOMR) control are reviewed using this approach.Human control of multiple robots has been suggested as a way to improve effectiveness inrobot control. Unlike previous studies that investigated human interaction either in low-fidelitysimulations or based on simple tasks, this thesis investigates human interaction with cooperatingrobot teams within a realistically complex environment. USARSim, a high-fidelity game-enginebasedrobot simulator, and MrCS, a distributed multirobot control system, were developed forthis purpose. In the pilot experiment, we studied the impact of autonomy level. Mixed initiativecontrol yielded performance superior to fully autonomous and manual control.To avoid limitation to particular application fields, the present thesis focuses on commonHRI evaluations that enable us to analyze HRI effectiveness and guide HRI design independentlyof the robotic system or application domain. We introduce the interaction episode (IEP), whichwas inspired by our pilot human-multirobot control experiment, to extend the Neglect ToleranceHUMAN CONTROL OF COOPERATING ROBOTSJijun Wang, Ph.D.University of Pittsburgh, 2007vmodel to support general multiple robots control for complex tasks. Cooperation Effort (CE),Cooperation Demand (CD), and Team Attention Demand (TAD) are defined to measure thecooperation in SOMR control. Two validation experiments were conducted to validate the CDmeasurement under tight and weak cooperation conditions in a high-fidelity virtual environment.The results show that CD, as a generic HRI metric, is able to account for the various factors thataffect HRI and can be used in HRI evaluation and analysis
- …