220 research outputs found
CONGESTION CONTROL FOR A ULTRA-WIDEBAND DYNAMIC SENSOR NETWORK USING AUTONOMIC BASED LEARNING
The physical conditions of the area of interest is being collected at the central
location using a set of dedicated sensors that forms a network is referred to as
Wireless Sensor Network. A dynamic environment is required for a secure multi-hop
communication between nodes of the heterogeneous Wireless Sensor Network. One
such solution is to employ autonomic based learning in a MAC Layer of the UWB
TxRx. Over a time period the autonomic based network learns from the previous
experience and adapts to the environment significantly. Exploring the Autonomicity
would help us in evading the congestion of about 30% in a typical UWB-WSNs.
Simulation results showed an improvement of 5% using Local Automate Collision
Avoidance Scheme (LACAS-UWB) compared to LACAS
Feasible, Robust and Reliable Automation and Control for Autonomous Systems
The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences
Aerospace Cyber-Physical Systems Education
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106495/1/AIAA2013-4809.pd
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Optimization and Control of Cyber-Physical Vehicle Systems
A cyber-physical system (CPS) is composed of tightly-integrated computation, communication and physical elements. Medical devices, buildings, mobile devices, robots, transportation and energy systems can benefit from CPS co-design and optimization techniques. Cyber-physical vehicle systems (CPVSs) are rapidly advancing due to progress in real-time computing, control and artificial intelligence. Multidisciplinary or multi-objective design optimization maximizes CPS efficiency, capability and safety, while online regulation enables the vehicle to be responsive to disturbances, modeling errors and uncertainties. CPVS optimization occurs at design-time and at run-time. This paper surveys the run-time cooperative optimization or co-optimization of cyber and physical systems, which have historically been considered separately. A run-time CPVS is also cooperatively regulated or co-regulated when cyber and physical resources are utilized in a manner that is responsive to both cyber and physical system requirements. This paper surveys research that considers both cyber and physical resources in co-optimization and co-regulation schemes with applications to mobile robotic and vehicle systems. Time-varying sampling patterns, sensor scheduling, anytime control, feedback scheduling, task and motion planning and resource sharing are examined
Dynamic Resource Allocation Model for Distribution Operations using SDN
In vehicular ad-hoc networks, autonomous vehicles generate a large amount of data prior to support in-vehicle applications. So, a big storage and high computation platform is needed. On the other hand, the computation for vehicular networks at the cloud platform requires low latency. Applying edge computation (EC) as a new computing paradigm has potentials to provide computation services while reducing the latency and improving the total utility. We propose a three-tier EC framework to set the elastic calculating processing capacity and dynamic route calculation to suitable edge servers for real-time vehicle monitoring. This framework includes the cloud computation layer, EC layer, and device layer. The formulation of resource allocation approach is similar to an optimization problem. We design a new reinforcement learning (RL) algorithm to deal with resource allocation problem assisted by cloud computation. By integration of EC and software defined networking (SDN), this study provides a new software defined networking edge (SDNE) framework for resource assignment in vehicular networks. The novelty of this work is to design a multi-agent RL-based approach using experience reply. The proposed algorithm stores the users’ communication information and the network tracks’ state in real-time. The results of simulation with various system factors are presented to display the efficiency of the suggested framework. We present results with a real-world case study
Motion Planning of UAV Swarm: Recent Challenges and Approaches
The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work
An Energy-aware, Fault-tolerant, and Robust Deep Reinforcement Learning based approach for Multi-agent Patrolling Problems
Autonomous vehicles are suited for continuous area patrolling problems.
However, finding an optimal patrolling strategy can be challenging for many
reasons. Firstly, patrolling environments are often complex and can include
unknown environmental factors. Secondly, autonomous vehicles can have failures
or hardware constraints, such as limited battery life. Importantly, patrolling
large areas often requires multiple agents that need to collectively coordinate
their actions. In this work, we consider these limitations and propose an
approach based on model-free, deep multi-agent reinforcement learning. In this
approach, the agents are trained to automatically recharge themselves when
required, to support continuous collective patrolling. A distributed
homogeneous multi-agent architecture is proposed, where all patrolling agents
execute identical policies locally based on their local observations and shared
information. This architecture provides a fault-tolerant and robust patrolling
system that can tolerate agent failures and allow supplementary agents to be
added to replace failed agents or to increase the overall patrol performance.
The solution is validated through simulation experiments from multiple
perspectives, including the overall patrol performance, the efficiency of
battery recharging strategies, and the overall fault tolerance and robustness
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