4,805 research outputs found
Artificial Intelligence and Systems Theory: Applied to Cooperative Robots
This paper describes an approach to the design of a population of cooperative
robots based on concepts borrowed from Systems Theory and Artificial
Intelligence. The research has been developed under the SocRob project, carried
out by the Intelligent Systems Laboratory at the Institute for Systems and
Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the
project stands both for "Society of Robots" and "Soccer Robots", the case study
where we are testing our population of robots. Designing soccer robots is a
very challenging problem, where the robots must act not only to shoot a ball
towards the goal, but also to detect and avoid static (walls, stopped robots)
and dynamic (moving robots) obstacles. Furthermore, they must cooperate to
defeat an opposing team. Our past and current research in soccer robotics
includes cooperative sensor fusion for world modeling, object recognition and
tracking, robot navigation, multi-robot distributed task planning and
coordination, including cooperative reinforcement learning in cooperative and
adversarial environments, and behavior-based architectures for real time task
execution of cooperating robot teams
Heterogeneous Multi-Robot Collaboration for Coverage Path Planning in Partially Known Dynamic Environments
This research presents a cooperation strategy for a heterogeneous group of robots that comprises two Unmanned Aerial Vehicles (UAVs) and one Unmanned Ground Vehicles (UGVs) to perform tasks in dynamic scenarios. This paper defines specific roles for the UAVs and UGV within the framework to address challenges like partially known terrains and dynamic obstacles. The UAVs are focused on aerial inspections and mapping, while UGV conducts ground-level inspections. In addition, the UAVs can return and land at the UGV base, in case of a low battery level, to perform hot swapping so as not to interrupt the inspection process. This research mainly emphasizes developing a robust Coverage Path Planning (CPP) algorithm that dynamically adapts paths to avoid collisions and ensure efficient coverage. The Wavefront algorithm was selected for the two-dimensional offline CPP. All robots must follow a predefined path generated by the offline CPP. The study also integrates advanced technologies like Neural Networks (NN) and Deep Reinforcement Learning (DRL) for adaptive path planning for both robots to enable real-time responses to dynamic obstacles. Extensive simulations using a Robot Operating System (ROS) and Gazebo platforms were conducted to validate the approach considering specific real-world situations, that is, an electrical substation, in order to demonstrate its functionality in addressing challenges in dynamic environments and advancing the field of autonomous robots.The authors also would like to thank their home Institute, CEFET/RJ, the federal Brazilian
research agencies CAPES (code 001) and CNPq, and the Rio de Janeiro research agency, FAPERJ, for
supporting this work.info:eu-repo/semantics/publishedVersio
The Impact of Heterogeneity on Operator Performance in Future Unmanned Vehicle Systems
Recent studies have shown that with appropriate operator decision support
and with sufficient automation, inverting the multiple operators to
single-unmanned vehicle control paradigm is possible. These studies,
however, have generally focused on homogeneous teams of vehicles, and
have not completely addressed either the manifestation of heterogeneity
in vehicle teams, or the effects of heterogeneity on operator capacity.
An important implication of heterogeneity in unmanned vehicle teams
is an increase in the diversity of possible team configurations available
for each operator, as well as an increase in the diversity of possible attention
allocation schemes that can be utilized by operators. To this end, this
paper introduces a discrete event simulation (DES) model as a means to
model a single operator supervising multiple heterogeneous unmanned
vehicles. The DES model can be used to understand the impact of varying
both vehicle team design variables (such as team composition) and
operator design variables (including attention allocation strategies). The
model also highlights the sub-components of operator attention allocation
schemes that can impact overall performance when supervising heterogeneous unmanned vehicle teams. Results from an experimental case study are then used to validate the model, and make predictions about operator performance for various heterogeneous team configurations.The research was supported by Charles River Analytics, the Office of Naval Research (ONR), and MIT Lincoln Laboratory
Distributed Bees Algorithm Parameters Optimization for a Cost Efficient Target Allocation in Swarms of Robots
Swarms of robots can use their sensing abilities to explore unknown environments and deploy on sites of interest. In this task, a large number of robots is more effective than a single unit because of their ability to quickly cover the area. However, the coordination of large teams of robots is not an easy problem, especially when the resources for the deployment are limited. In this paper, the Distributed Bees Algorithm (DBA), previously proposed by the authors, is optimized and applied to distributed target allocation in swarms of robots. Improved target allocation in terms of deployment cost efficiency is achieved through optimization of the DBAās control parameters by means of a Genetic Algorithm. Experimental results show that with the optimized set of parameters, the deployment cost measured as the average distance traveled by the robots is reduced. The cost-efficient deployment is in some cases achieved at the expense of increased robotsā distribution error. Nevertheless, the proposed approach allows the swarm to adapt to the operating conditions when available resources are scarce
An Analysis of Heterogeneity in Futuristic Unmanned Vehicle Systems
Recent studies have shown that with appropriate operator decision support and with enough automation aboard
unmanned vehicles, inverting the multiple operators to single-vehicle control paradigm is possible. These studies,
however, have generally focused on homogeneous teams of vehicles, and have not completely addressed either the
manifestation of heterogeneity in vehicle teams, or the effects of heterogeneity on operator capacity. An important
implication of heterogeneity in unmanned vehicle teams is an increase in the diversity of possible team
configurations available for each operator, as well as an increase in the diversity of possible attention allocation
schemes that can be utilized by operators. To this end, this paper introduces a resource allocation framework that
defines the strategies and processes that lead to alternate team configurations. The framework also highlights the
sub-components of operator attention allocation schemes that can impact overall performance when supervising
heterogeneous unmanned vehicle teams. A subsequent discrete event simulation model of a single operator
supervising multiple heterogeneous vehicles and tasks explores operator performance under different heterogeneous
team compositions and varying attention allocation strategies. Results from the discrete event simulation model
show that the change in performance when switching from a homogeneous team to a heterogeneous one is highly
dependent on the change in operator utilization. Heterogeneous teams that result in lower operator utilization can
lead to improved performance under certain operator strategies.Prepared for Charles River Analytic
Multi-Robot Task Allocation: A Spatial Queuing Approach
Multi-Robot Task Allocation (MRTA) is an important area of research in autonomous multi-robot systems. The main problem in MRTA is to match a set of robots to a set of tasks so that the tasks can be completed by the robots while optimizing a certain metric such as the time required to complete all tasks, distance traveled by the robots and energy expended by the robots. We consider a scenario where the tasks can appear dynamically and the location of tasks are not known a priori by the robots. Additionally, for a task to be completed, it needs to be performed by multiple robots. This setting is called the MR-ST-TA (multi-robot, single-task, time- extended assginment) category of MRTA; solving the MRTA problem for this category is a known NP-hard problem. In this thesis, we address this problem by proposing a new algorithm that uses a spatial queue-based model to allocate tasks between robots while comparing its performance to several other known methods. We have implemented these algorithms on an accurately simulated model of Corobot robots within the Webots simulator for diļ¬erent numbers of robots and tasks. The results show that our method is adept in all proļ¬ered environments, especially scenarios that beneļ¬t from path planning, whereas other methods display inherent weakness at one end of the spectrum: a decentralized greedy approach exhibits ineļ¬cient behavior as the robot to task ratio dips below one, whereas the Hungarian method (an oļ¬ine algorithm) fails to keep pace as the robot count increases
coordinated selection and timing of multiple trajectories of discretely mobile robots
Abstract The paper addresses the multi-agent path planning (MPP) of mobile agents with multiple goals taking into consideration the kinematic constraints of each agent. The "Swing and Dock" (SaD) robotic system being discussed uses discrete locomotion, where agents swing around fixed pins and dock with their mounting legs to realize displacement from one point to another. The system was developed as a subsystem for mobile robotic fixture (SwarmItFix). Previous work dealt with MPP for SaD agents using the concept of extended temporal graph with Integer Linear Programming (ILP) based formulations. The approach discretized time into unit steps, whereas in reality, the agents are constrained by velocity limits. Hence, a real-time schedule is required to accurately plan the agent movement in a working scenario. We utilize the concept of simple temporal network and extend our ILP formulations to model the velocity kinematic constraints. The mathematical formulations are implemented and tested using a GUROBI solver. Computational results display the effectiveness of the approach
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