Limited Sensor Capabilities Research Articles

Event-triggered consensus control method with communication faults for multi-UAV

This paper investigates the event-triggered consensus for a group of unmanned aerial vehicles (UAVs) with communication faults under the assumption that the position sensors of some individuals are damaged. The objective is to make the UAV group reach consensus in urgent tasks such as obstacle avoidance or evasion. Using the Lyapunov stability theory, sufficient conditions to achieve system consensus are given based on different velocity and position interaction topologies. Considering the limited capabilities of sensors and processors, an event-triggered consensus protocol is adopted to reduce the sampling frequency. Finally, simulation results illustrate the effectiveness of our approach.

Unmanned Aerial Vehicle Path Planning Algorithm Based on Deep Reinforcement Learning in Large-Scale and Dynamic Environments

Path planning is one of the key technologies for autonomous flight of Unmanned Aerial Vehicle. Traditional path planning algorithms have some limitations and deficiencies in the complex and dynamic environment. In this article, we propose a deep reinforcement learning approach for three-dimensional path planning by utilizing the local information and relative distance without global information. UAV can obtain the limited environmental information nearby in the actual scenario with limited sensor capabilities. Therefore, path planning can be formulated as a Partially Observable Markov Decision Process. The recurrent neural network with temporal memory is constructed to address the partial observability problem by extracting crucial information from historical state-action sequences. We develop an action selection strategy that combines the current reward value and the state-action value to reduce the meaningless exploration. In addition, we construct two sample memory pools and propose an adaptive experience replay mechanism based on the frequency of failure. The simulation experiment results show that our method has significant improvements over Deep Q-Network and Deep Recurrent Q-Network in terms of stability and learning efficiency. Our approach successfully plans a reasonable three-dimensional path in the large-scale and complex environment, and has the perfect ability to avoid obstacles.in the unknown environment.

Ultra-Reliable and Low-Latency Communications for Connected Vehicles: Challenges and Solutions

With the rapid development of communications and computing, the concept of connected vehicles emerges to improve driving safety, traffic efficiency and the infotainment experience. Due to the limited capabilities of sensors and information processing on a single vehicle, vehicular networks (VNETs) play a vital role for the realization of connected vehicles. To achieve closer cooperation on the road, ultra-reliable and low-latency communications (URLLCs) become the indispensable components of future VNETs. However, traditional wireless networks are designed with the objective of maximizing spectral and energy efficiency regardless of adequate consideration of URLLCs. To this end, this article investigates the challenges and solutions of conceiving URLLC VNETs dedicated for connected vehicles. Specifically, the underlying application scenarios are first presented with various vehicular communication patterns. Then, the URLLC requirements and potential challenges are discussed for VNETs. We also discuss the solutions to establish URLLC VNETs from the aspects of the physical and media access control layers, including the optimization frameworks for latency and reliability, as well as the case studies of employing such frameworks. Finally, several open research directions are pointed out.

A Hybrid Path-Planning Strategy for Mobile Robots with Limited Sensor Capabilities.

This paper introduces a strategy for the path planning problem for platforms with limited sensor and processing capabilities. The proposed algorithm does not require any prior information but assumes that a mapping algorithm is used. If enough information is available, a global path planner finds sub-optimal collision-free paths within the known map. For the real time obstacle avoidance task, a simple and cost-efficient local planner is used, making the algorithm a hybrid global and local planning solution. The strategy was tested in a real, cluttered environment experiment using the Pioneer P3-DX and the Xbox 360 kinect sensor, to validate and evaluate the algorithm efficiency.

Cooperative Automated Driving for Various Traffic Scenarios: Experimental Validation in the GCDC 2016

Cooperative automated driving is a promising technology to improve road safety, fuel consumption, and traffic throughput without the need to expand the current infrastructure. To accelerate the developments in cooperative driving toward deployment in realistic traffic, the second grand cooperative driving challenge (GCDC) took place in Helmond, The Netherlands, in 2016. The aim of this implementation oriented challenge is to validate the practical feasibility and benefits of cooperative automated driving in the context of several advanced traffic scenarios, including cooperative merging on a highway and cooperative intersection crossing. Since all scenarios require road participants to cooperate, an interaction protocol was provided for each scenario. Except for this pre-defined interaction protocol, each team had full flexibility in developing the cooperative automated vehicle system. As such, one of the main difficulties of the challenge was to ensure interoperability despite the fact that each road participant might use different vehicle types and vehicle control systems that have been developed independently. In this paper, we provide an overview of the ATeam’s implementation of the cooperative automated driving system for the cooperative merging on a highway and cooperative intersection crossing scenarios. This overview addresses the hardware architecture used during GCDC and, the design and integration of the required software layers. This paper also addresses practical issues that need to be taken into account when developing a cooperative automated driving system such as the limited capabilities of sensors and imperfections induced by the packet-based communication. Moreover, we present experimental results that were obtained during the challenge.

Energy-Based Acoustic Source Localization Methods: A Survey.

Energy-based source localization is an important problem in wireless sensor networks (WSNs), which has been studied actively in the literature. Numerous localization algorithms, e.g., maximum likelihood estimation (MLE) and nonlinear-least-squares (NLS) methods, have been reported. In the literature, there are relevant review papers for localization in WSNs, e.g., for distance-based localization. However, not much work related to energy-based source localization is covered in the existing review papers. Energy-based methods are proposed and specially designed for a WSN due to its limited sensor capabilities. This paper aims to give a comprehensive review of these different algorithms for energy-based single and multiple source localization problems, their merits and demerits and to point out possible future research directions.

Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems

Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs.

Optimizing Coverage Performance of Multiple Random Path-planning Robots

Abstract This paper presents a new approach to the multi-agent coverage path-planning problem. An efficient multi-robot coverage algorithm yields a coverage path for each robot, such that the union of all paths generates an almost full coverage of the terrain and the total coverage time is minimized. The proposed algorithm enables multiple robots with limited sensor capabilities to perform efficient coverage on a shared territory. Each robot is assigned to an exclusive route which enables it to carry out its tasks simultaneously, e.g., cleaning assigned floor area with minimal path overlapping. It is very difficult to cover all free space without visiting some locations more than once, but the occurrence of such events can be minimized with efficient algorithms. The proposed multi-robot coverage strategy directs a number of simple robots to cover an unknown area in a systematic manner. This is based on footprint data left by the randomized path-planning robots previously operated on that area. The developed path-planning algorithm has been applied to a simulated environment and robots to verify its effectiveness and performance in such an application.

Evaluation of the GOME Water Vapor Climatology 1995–2002

An accurate knowledge of the 3‐D water vapor (WV) field is still limited, because of the limited capabilities of sensors in the past to cover the whole Earth’s surface and the lower part of the troposphere, as well as to measure over reasonably long time series. We show here water vapor total column retrieved from seven years of Global Ozone Monitoring Experiment (GOME) measurements collected from August 1995 until August 2002. Our aim is two‐fold: (1) to evaluate the accuracy and the limitations of the GOME water vapor total column and (2) to demonstrate its potential for climate studies. The column retrieval makes use of two innovative techniques operating in tandem, namely the University of Graz empirical air mass factor ratioing technique (IGAM) and the Spectral Structure Parameterization (SSP) retrieval method. The GOME instrument and its follow‐up instruments (SCIAMACHY and GOME‐2), using these algorithms, have the capability to cover nearly the whole globe in cloud‐free situations and collect robust WV total column information over more than 3 decades. In this work we evaluate the results for the first 7 years against independent in situ measurements from the operational WMO radiosonde network, against high spatial resolution water vapor columns from MERIS (the Medium Resolution Imaging Spectrometer on EnviSat) and also compare with ERA40 model results. The GOME water vapor total column exhibits a bias of less than 2.5% with an uncertainty of around 5 mm for collocated measurements against radiosonde and MERIS measurements. Spatial patterns and trends in the global distribution of WV total column fields from GOME against re‐analysis model results are well correlated with temperature in the tropics, and exhibit a lesser degree of correlation in the extra tropics. Cloud‐free total columns from GOME can be systematically lower by up to 5 mm in the sub‐tropics with respect to the all‐sky case. In contrast, the impact of the diurnal cycle on the monthly mean values is found to be very small.