Robotic Sensor Networks Research Articles

A Sensor Self-aware Distributed Consensus Filter for Simultaneous Localization and Tracking

Simultaneous localization and tracking (SLAT) has become a very hot topic in both academia and industry for its potential wide applications in robotic equipment, sensor networks and smart devices. In order to exploit the advantages supported by state filtering and parameter estimation, researchers have proposed adaptive structures for solving SLAT problems. Existing solutions for SLAT problems that rely on belief propagation often have limited accuracy or high complexity. To adapt the brain decision mechanism for solving SLAT problems, we introduce a specific framework that is suitable for wireless sensor networks. Motivated by the high efficiency and performance of brain decision making built upon partial information and information updating, we propose a cognitively distributed SLAT algorithm based on an adaptive distributed filter, which is composed of two stages for target tracking and sensor localization. The first stage is consensus filtering that updates the target state with respect to each sensor. The second stage employs a recursive parameter estimation that exploits an on-line optimization method for refining the sensor localization. As an integrated framework, each consensus filter is specific to a separate sensor subsystem and gets feedback information from its parameter estimation. The performance comparison in terms of positioning accuracy with respect to RMSE is shown and the simulation results demonstrate that the proposed ICF-RML performs better than the BPF-RML. This is expected since the distributed estimation with sufficient communication mechanism often achieves higher accuracy than that of less sufficient cases. Furthermore, the performance of the ICF-RML is comparable with that of the BPF-RML even if the latter assumes known prior network topology. We also observe from the results of tracking errors that ICF-RML accomplishes a remarkable improvement in the precision of target tracking and achieves more stable convergence than BPF-RML, in the scenario that all sensors are used to calculate the effect from data association errors. We apply this approach to formulate the SLAT problem and propose an effective solution, summarized in the paper. For small-size sensor networks with Gaussian distribution, our algorithm can be implemented through a distributed version of weighted information filter and a consensus protocol. Comparing the existing method, our solution shows a higher accuracy in estimation but with less complexity.

Node Localization in Robotic Sensor Networks for Pipeline Inspection

Robotic sensor networks provide an effective approach for underground pipeline inspection. Such networks are comprised of sensor nodes (SNs) and relay nodes (RNs) carried by robots for information sensing and communication, and are able to perform accurate and realtime inspection, especially in adverse environments. SN localization is critical in such networks because localization results can be used not only for locating and pinpointing leaks, but also for maneuvering mobile SNs in a pipeline of complex configuration. However, both the underground operational environment and the limited resources of the SNs pose significant challenges for SN localization. This paper presents algorithms for SN localization in robotic sensor networks for underground pipeline inspection. Specifically, self-localization of underground in-pipe SNs were investigated by taking into account SN movement dynamics, and using the measurements of the SN’s velocity and the received signal strength (RSS) of the radio signal from aboveground RNs. Depending on the availability of the RSS at the SN, different localization algorithms based on the Kalman filter are proposed for different scenarios. Simulation results show the efficacy of the proposed algorithms. The framework also provides insight into the design of robotic sensor networks for the inspection and maintenance of other types of pipeline systems, such as oil and gas pipelines.

Locally selectable protocol for sparse, highly-volatile, robotic wireless video sensor networks

Wireless video sensor networks WVSNs are a specialisation of wireless sensor networks that focus on high bandwidth transmission with low latency and under low power conditions. This type of network might be applied is in multi-robot exploration for urban search and rescue USAR. We propose a new hybrid routing protocol, called locally selectable protocol LSP, on top of a Bluetooth piconet architecture that provides high-bandwidth, multi-hop routing with reduced latency and very low power consumption for sparse topologies that are highly volatile, yet are continuously streaming video. We take advantage of the hybrid combination of proactive and reactive routing schemes. We simulate and implement the proposed scheme on a small team of resource-constrained USAR robots, called TerminatorBot. We have shown that our protocol on top of Bluetooth maintains higher bandwidth with lower latency to dropped nodes and lower overall power consumption in comparison to existing solutions that support ZigBee and Wi-Fi.

Obstacle Avoidance in Mobile Robotic Sensors and Establishing Connection

The Mobile Robotic Sensor Network (MRSN), require reliable applications and coordination based on strong network connectivity. These sensors are deployed into hazardous environment such as extreme temperature, mudslides, explosion etc. Chances of failure is high in sensor during operation which leads to easy compromise on connectivity due to interference. Based on the survey undertaken on DARA, LeDiR, they faces obstacle collision and sensor collision. The proposed method OCRS avoids the problem of obstacle avoidance and also introduces the concept of gyroscopic force. OCRS is implemented with the help of backup selection algorithm. On the basis of implementation done with the help of system model and control input it proves OCRS is more efficient than the existing system.

Restoring Connectivity of Mobile Robotic Sensor Networks While Avoiding Obstacles

In many mission critical applications of mobile robotic sensor networks (MRSNs), the intersensor collaboration requires reliable application-level coordination based on strong network connectivity with some suggested solutions. In practice, however, the disturbing obstacles and harsh interferences the connectivity of the MRSN can be easily compromised, especially when the failure of some critical sensors results in disintegration of the network into two or more disjoint segments. Existing connectivity restoration schemes fail to perform under such harsh working conditions as they overlook an important fact that sensors may encounter obstacles during the relocation. Our obstacle-avoiding connectivity restoration strategy (OCRS) proposed method addresses this problem using a fully exploring mobility technique avoiding any incoming convex obstacle conditions. For which a backup selection algorithm (BSA) proactively determine the cut-vertex sensors within the network and assigns a backup sensor to each cut-vertex node. Then, a selected backup sensor avoiding obstacles uses a gyroscopic force controller displaced restoring the disturbed connectivity. Extensive simulation experiments verify OCRS capability to restore connectivity with guaranteed collision avoidance, and also to outperform contemporary schemes in terms of message complexity and traveling distance.

A Review on Sensor Network Issues and Robotics

The interaction of distributed robotics and wireless sensor networks has led to the creation of mobile sensor networks. There has been an increasing interest in building mobile sensor networks and they are the favored class of WSNs in which mobility plays a key role in the execution of an application. More and more researches focus on development of mobile wireless sensor networks (MWSNs) due to its favorable advantages and applications. In WSNs robotics can play a crucial role, and integrating static nodes with mobile robots enhances the capabilities of both types of devices and enables new applications. In this paper we present an overview on mobile sensor networks in robotics and vice versa and robotic sensor network applications.

Distributed Task Allocation of Mobile Robotic Sensor Networks with Guaranteed Connectivity

Robotic sensor network (RSN) contains mobile sensors and robots providing feasible solution for many multi-agent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel connectivity preserving hybrid task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Task-oriented Unoccupied Neighbor Algorithm, named TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical to most RSN applications. The correctness, efficiency and scalability of TUNA are proved with both theoretical analysis and experimental simulations. The evaluation results show that TUNA can effectively assign tasks to mobile robots with the requirements of only a few messages and small movements of mobile agents.

Optimal swarm formation for odor plume finding.

This paper presents an analytical approach to the problem of odor plume finding by a network of swarm robotic gas sensors, and finds an optimal configuration for them, given a set of assumptions. Considering cross-wind movement for the swarm, we found that the best spatial formation of robots in finding odor plumes is diagonal line configuration with equal distance between each pair of neighboring robots. We show that the distance between neighboring pairs in the line topology depends mainly on the wind speed and the environmental conditions, whereas, the number of robots and the swarm's crosswind movement distance do not show significant impact on optimal configurations. These solutions were analyzed and verified by simulations and experimentally validated in a reduced scale realistic environment using a set of mobile robots.

Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks

Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal.

Exploiting the use of information to improve coverage performance of robotic sensor networks

A robotic sensor network is advantageous in performing a coverage task compared to the static sensor network because of its ability to self-deploy and self-reconfigure. However, since the sensor has a limited sensing range, when mobile sensors are initially deployed, sensors located far away from the region of interest may not be able to self-deploy themselves, that is, participate in the coverage task. This results in a degradation of coverage performance by the robotic network. This paper proposes a novel algorithm in order to improve the coverage performance by the whole mobile sensor network by guaranteeing the participation of all sensors in the coverage task. The algorithm is a combination of the standard gradient-based coverage algorithm and a leader-following algorithm and is designed to maximise the joint detection probabilities of the events in the region of interest. As a strategy, first a set of leader sensors are selected based on the information which each sensor has gathered. The rest of the sensors will follow their leaders until they have sufficient information on the region of interest and then switch to the standard coverage algorithm. The proposed algorithm can be performed in a distributed manner. The results are validated through several numerical simulations.

An adaptive scheme for robot localization and mapping with dynamically configurable inter-beacon range measurements.

This work is motivated by robot-sensor network cooperation techniques where sensor nodes (beacons) are used as landmarks for range-only (RO) simultaneous localization and mapping (SLAM). This paper presents a RO-SLAM scheme that actuates over the measurement gathering process using mechanisms that dynamically modify the rate and variety of measurements that are integrated in the SLAM filter. It includes a measurement gathering module that can be configured to collect direct robot-beacon and inter-beacon measurements with different inter-beacon depth levels and at different rates. It also includes a supervision module that monitors the SLAM performance and dynamically selects the measurement gathering configuration balancing SLAM accuracy and resource consumption. The proposed scheme has been applied to an extended Kalman filter SLAM with auxiliary particle filters for beacon initialization (PF-EKF SLAM) and validated with experiments performed in the CONET Integrated Testbed. It achieved lower map and robot errors (34% and 14%, respectively) than traditional methods with a lower computational burden (16%) and similar beacon energy consumption.

Striving for sensing: Taming your mobile code to share a robot sensor network

This article presents a general purpose, multi-application mobile node sensor network based on mobile code. This intelligent system can work in delay and disruption tolerant (DTN) scenarios. Mobile nodes host software mobile code with task missions and act as DTN routers following the store-carry-and-forward paradigm. Most similar proposals are unable to simultaneously run different applications, with different routing algorithms, movement models, and information retrieval strategies. The keystone of the approach in this paper is using mobile code at two levels: for the application, and for the definition of the behavior in terms of routing algorithms, movement policies and sensor retrieval preferences. The intelligence of the system lies in its ability to adapt to the environment, dynamically optimizing routing algorithms using local and global information and influencing node movement. Simulations and an implementation of a real scenario have been undertaken to prove the feasibility and usability of the system, and to study its performance. A proposal for a real-world application in the context of refugee camp management is presented.

Recognition of Emotions using Energy Based Bimodal Information Fusion and Correlation

Multi-sensor information fusion is a rapidly developing research area which forms the backbone of numerous essential technologies such as intelligent robotic control, sensor networks, video and image processing and many more. In this paper, we have developed a novel technique to analyze and correlate human emotions expressed in voice tone & facial expression. Audio and video streams captured to populate audio and video bimodal data sets to sense the expressed emotions in voice tone and facial expression respectively. An energy based mapping is being done to overcome the inherent heterogeneity of the recorded bi-modal signal. The fusion process uses sampled and mapped energy signal of both modalities�s data stream and further recognize the overall emotional component using Support Vector Machine (SVM) classifier with the accuracy 93.06%.

A Deployment Method Based on Spring Force in Wireless Robot Sensor Networks

Robotic sensor deployment is fundamental for the effectiveness of wireless robot sensor networks-a good deployment algorithm leads to good coverage and connectivity with low energy consumption for the whole network. Virtual force-based algorithms (VFAs) is one of the most popular approaches to this problem. In VFA, sensors are treated as points subject to repulsive and attractive forces exerted among them-sensors can move according to imaginary force generated in algorithms. In this paper, a virtual spring force-based algorithm with proper damping is proposed for the deployment of sensor nodes in a wireless sensor network (WSN). A new metric called Pair Correlation Diversion (PCD) is introduced to evaluate the uniformity of the sensor distribution. Numerical simulations showed that damping can affect the network coverage, energy consumption, convergence time and general topology in the deployment. Moreover, it was found that damping effect (imaginary friction force) has significant influence on algorithm outcomes. In addition, when working under approximate critical-damping condition, the proposed approach has the advantage of a higher coverage rate, better configurational uniformity and less energy consumption.

Implementing a Trust and Reputation Model for Robotic Sensor Networks

Robotic sensor networks (RSNs) can be described as networks of devices equipped with communication, sensing and actuation capabilities. Successful implementations of RSNs require tackling challenging problems lying at the intersection of robotics, communication and perception. In addition, RSNs resemble human societies and emerging intelligent multi-agent systems in some respects. In these collaborative distributed systems, each node decides which to interact with and forms a network with other nodes in order to improve the quality of the decisions. In order to achieve this goal, trust and reputation models are of practical use. In this paper, a trust and reputation model for RNSs is proposed. Also, performance evaluations of the model in comparison with well-known models in the literature are given to prove its effectiveness. The results of the performance evaluations prove that the proposed model is successful in RSNs comprising of a large number of sensor nodes. In addition, the processing and memory requirements of the proposed model are moderate and the system runs effectively in systems with low processing power and limited main memory. DOI: http://dx.doi.org/10.5755/j01.eee.19.10.5884

Home monitoring system based on indoor service robot and wireless sensor network

In this paper, the design and implementation of a home monitoring system which is based on indoor service robot and wireless sensor network are presented. The wireless sensor node which consists of mote module and sensor board is designed and fabricated. The construction algorithm of the wireless sensor network is based on self-organizing feature map model. The indoor service robot which has the capabilities to follow human commands and handle emergency is designed and implemented. A location algorithm of the robot based on the wireless sensor network is proposed. Stability of the proposed home monitoring system in longtime monitoring tasks is tested. Experimental results also demonstrate the indoor service robot can improve the capabilities of the wireless sensor network and the home monitoring system.

Design of a network of robotic Lagrangian sensors for shallow water environments with case studies for multiple applications

This article describes the design methodology for a network of robotic Lagrangian floating sensors designed to perform real-time monitoring of water flow, environmental parameters, and bathymetry of shallow water environments (bays, estuarine, and riverine environments). Unlike previous Lagrangian sensors which passively monitor water velocity, the sensors described in this article can actively control their trajectory on the surface of the water and are capable of inter-sensor communication. The addition of these functionalities enables Lagrangian sensing in obstacle-encumbered environments, such as rivers. The Ishikawa cause and effect design framework is used to ensure that the final system synthesizes the diverse operational and functional needs of multiple end-user groups to arrive at a broadly applicable system design. A summary of potential applications for the system is given including completed projects performed on behalf of the Department of Homeland Security, Office of Naval Research, and the California Bay-Delta Authority.

Remote Biological and Robotic Sensor Networks for Environmental Monitoring

Environment monitoring of large areas requires for many in-situ pollutant measurements. An efficient approach which integrates biological and robotic systems is presented in this contribution. In this approach bees provide an early detection capacity which is complemented by mobile robots to verify pollution indications by obtaining more detailed in-situ measurements. This contribution reports on the system architecture and the challenging technical components related to detecting the sampling location of the bees by observing their dancing behavior, the autonomous transfer of the rovers to these target positions for detailed measurements, the integration of the obtained data from the different sources into an overall characterization of the area's pollution status and an autonomous flexible alerting system.

Distributed robotic sensor networks: An information-theoretic approach

In this paper we present an information-theoretic approach to distributively control multiple robots equipped with sensors to infer the state of an environment. The robots iteratively estimate the environment state using a sequential Bayesian filter, while continuously moving along the gradient of mutual information to maximize the informativeness of the observations provided by their sensors. The gradient-based controller is proven to be convergent between observations and, in its most general form, locally optimal. However, the computational complexity of the general form is shown to be intractable, and thus non-parametric methods are incorporated to allow the controller to scale with respect to the number of robots. For decentralized operation, both the sequential Bayesian filter and the gradient-based controller use a novel consensus-based algorithm to approximate the robots’ joint measurement probabilities, even when the network diameter, the maximum in/out degree, and the number of robots are unknown. The approach is validated in two separate hardware experiments each using five quadrotor flying robots, and scalability is emphasized in simulations using 100 robots.

Adaptive Information Collection by Robotic Sensor Networks for Spatial Estimation

This work deals with trajectory optimization for a robotic sensor network sampling a spatio-temporal random field. We examine the optimal sampling problem of minimizing the maximum predictive variance of the estimator over the space of network trajectories. This is a high-dimensional, multi-modal, nonsmooth optimization problem, known to be NP-hard even for static fields and discrete design spaces. Under an asymptotic regime of near-independence between distinct sample locations, we show that the solutions to a novel generalized disk-covering problem are solutions to the optimal sampling problem. This result effectively transforms the search for the optimal trajectories into a geometric optimization problem. Constrained versions of the latter are also of interest as they can accommodate trajectories that satisfy a maximum velocity restriction on the robots. We characterize the solution for the unconstrained and constrained versions of the geometric optimization problem as generalized multicircumcenter trajectories, and provide algorithms which enable the network to find them in a distributed fashion. Several simulations illustrate our results.