85 research outputs found

    Target-directed navigation using wireless sensor networks and implicit surface interpolation

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    pre-printAbstract-This paper extends the novel research for event localization and target-directed navigation using a deployed wireless sensor network (WSN) [4]. The goal is to have an autonomous mobile robot (AMR) navigate to a target-location by: (i) producing an artificial magnitude distribution within the WSN-covered region, and (ii) having the AMR use the pseudo-gradient from the interpolated distribution in its neighborhood, as it moves towards the target location. Implicit surfaces are used to interpolate the artificial distribution. This scheme only uses the topology of the WSN and received signal strength (RSS) to estimate an efficient navigation path for the AMR. Here, the AMR does not require global coordinates for the region, as it relies on local, neighborhood information alone to navigate. The performance of the scheme is analyzed with hardware experiments and in simulation, using a variety of node-densities and with increasing levels of noise to ensure robustness. Index Terms-Target-directed navigation, pseudo-gradient, spline-interpolated distribution, received signal strength

    Received signal strength based bearing-only robot navigation in a sensor network field

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    pre-printThis paper presents a low-complexity, novel approach to wireless sensor network (WSN) assisted autonomous mobile robot (AMR) navigation. The goal is to have an AMR navigate to a target location using only the information inherent to WSNs, i.e., topology of the WSN and received signal strength (RSS) information, while executing an efficient navigation path. Here, the AMR has neither the location information for the WSN, nor any sophisticated ranging equipment for prior mapping. Two schemes are proposed utilizing particle filtering based bearing estimation with RSS values obtained from directional antennas. Real-world experiments demonstrate the effectiveness of the proposed schemes. In the basic node-to-node navigation scheme, the bearing-only particle filtering reduces trajectory length by 11.7% (indoors) and 15% (outdoors), when compared to using raw bearing measurements. The advanced scheme further reduces the trajectory length by 22.8% (indoors) and 19.8% (outdoors), as compared to the basic scheme. The mechanisms exploit the low-cost, low-complexity advantages of the WSNs to provide an effective method for map-less and ranging-less navigation

    Target localization and autonomous navigation using wireless sensor networks -a pseudogradient algorithm approach

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    pre-printAutonomous mobile robots (AMRs) operating in unknown environments face twin challenges: 1) localization and 2) efficient directed navigation. This paper describes a two-tiered approach to solving these challenges: 1) by developing novel wireless-sensor-network (WSN)-based localization methods and 2) by using WSN-AMR interaction for navigation. The goal is to have an AMR travel from any point within a WSN-covered region to an identified target location without the aid of global sensing and position information. In this research, the target is reached as follows: 1) by producing a magnitude distribution within the WSN region that has a target-directed pseudogradient (PG) and 2) by having the WSN efficiently navigate the AMRs using the PG. This approach utilizes only the topology of the network and the received signal strength (RSS) among the sensor nodes to create the PG. This research shows that, even in the absence of global positioning information, AMRs can successfully navigate toward a target location using only the RSS in their local neighborhood to compute an optimal path. The utility of the proposed scheme is proved through extensive simulation and hardware experiments

    Target Localization and Autonomous Navigation Using Wireless Sensor Networks-A Pseudogradient Algorithm Approach

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    Abstract-Autonomous mobile robots (AMRs) operating in unknown environments face twin challenges: 1) localization and 2) efficient directed navigation. This paper describes a two-tiered approach to solving these challenges: 1) by developing novel wireless-sensor-network (WSN)-based localization methods and 2) by using WSN-AMR interaction for navigation. The goal is to have an AMR travel from any point within a WSN-covered region to an identified target location without the aid of global sensing and position information. In this research, the target is reached as follows: 1) by producing a magnitude distribution within the WSN region that has a target-directed pseudogradient (PG) and 2) by having the WSN efficiently navigate the AMRs using the PG. This approach utilizes only the topology of the network and the received signal strength (RSS) among the sensor nodes to create the PG. This research shows that, even in the absence of global positioning information, AMRs can successfully navigate toward a target location using only the RSS in their local neighborhood to compute an optimal path. The utility of the proposed scheme is proved through extensive simulation and hardware experiments. Index Terms-Goal-directed navigation, pseudo topological gradient, wireless received signal strength (RSS), wireless-sensornetwork (WSN)-assisted target localization

    Odor Localization Sub Tasks: A Survey

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    This paper discusses about the sub tasks of odor localization research. Three steps of odor localization, i.e. Plume finding, plume tracking/tracing, and source declaration are explained. The difficulty of plume finding is discussed. Farrell’s Filamentous and Pseudo-Gaussian plume models that have been analyzed by previous researcher are presented. Some approaches used in plume tracking/tracing based on advection/turbulent and the estimation of odors’ distribution are provided. The advantages of source declaration are showed. Some problems occur in plume finding become a great consideration for the future research

    On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

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    Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

    Distributed Robotic Vision for Calibration, Localisation, and Mapping

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    This dissertation explores distributed algorithms for calibration, localisation, and mapping in the context of a multi-robot network equipped with cameras and onboard processing, comparing against centralised alternatives where all data is transmitted to a singular external node on which processing occurs. With the rise of large-scale camera networks, and as low-cost on-board processing becomes increasingly feasible in robotics networks, distributed algorithms are becoming important for robustness and scalability. Standard solutions to multi-camera computer vision require the data from all nodes to be processed at a central node which represents a significant single point of failure and incurs infeasible communication costs. Distributed solutions solve these issues by spreading the work over the entire network, operating only on local calculations and direct communication with nearby neighbours. This research considers a framework for a distributed robotic vision platform for calibration, localisation, mapping tasks where three main stages are identified: an initialisation stage where calibration and localisation are performed in a distributed manner, a local tracking stage where visual odometry is performed without inter-robot communication, and a global mapping stage where global alignment and optimisation strategies are applied. In consideration of this framework, this research investigates how algorithms can be developed to produce fundamentally distributed solutions, designed to minimise computational complexity whilst maintaining excellent performance, and designed to operate effectively in the long term. Therefore, three primary objectives are sought aligning with these three stages

    Acoustically driven control of mobile robots for source localization in complex ocean environments

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    Ocean based robotic systems are an opportunity to combine the power of acoustic sensing in the water with sophisticated control schemes. Together these bodies of knowledge could create autonomous systems for mapping acoustic fields and localizing underwater sources. However, existing control schemes have often been designed for land and air robots. This creates challenges for applying these algorithms to complex ocean environments. Acoustic fields are strongly frequency dependent, can rarely be realistically modeled analytically, have complex contours where the feature of interest is not always located at the peak pressure, and include many sources of background noise. This work addresses these challenges for control schemes from three categories: feedback and observer control, gradient ascent control and optimal control. In each case the challenges of applying the control scheme to an acoustic field are enumerated and addressed to create a suite of acoustically driven control schemes. For many of these algorithms, the largest issue is the processing and collection of acoustic data, particularly in the face of noise. Two new methods are developed to solve this issue. The first is the use of Principal Component Analysis as a noise filter for acoustic signals, which is shown to address particularly high levels of noise, while providing the frequency dependent sound pressure levels necessary for subsequent processing. The second method addresses the challenge that an analytical expression of the pressure field is often lacking, due to uncertainties and complexities in the environmental parameters. Basis functions are used to address this. Several candidates are considered, but Legendre polynomials are selected for their low error and reasonable processing time. Additionally, a method of intermediate points is used to approximate high frequency pressure fields with low numbers of collected data points. Following this work, the individual control schemes are explored. A method of observer feedback control is proposed to localize sources by linearizing the acoustic fields. A gradient ascent method for localizing sources in real time is proposed which uses Matched Field Processing and Bayesian filters. These modifications allow the gradient ascent algorithm to be compatible with complex acoustic fields. Finally, an optimal control method is proposed using Pontryagin's Maximum Principle to derive trajectories in real time that balance information gain with control energy. This method is shown to efficiently map an acoustic field, either for optimal sensor placement or to localize sources. The contribution of this work is a new collection of control schemes that use acoustic data to localize acoustically complex sources in a realistic noisy environment, and an understanding of the tradeoffs inherent in applying each of these to the acoustic domain
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