Hung Manh La

Distributed Sensor Fusion for Scalar Field Mapping Using Mobile Sensor Networks

In this paper, autonomous mobile sensor networks are deployed to measure a scalar field and build its map. We develop a novel method for multiple mobile sensor nodes to build this map using noisy sensor measurements. Our method consists of two parts. First, we develop a distributed sensor fusion algorithm by integrating two different distributed consensus filters to achieve cooperative sensing among sensor nodes. This fusion algorithm has two phases. In the first phase, the weighted average consensus filter is developed, which allows each sensor node to find an estimate of the value of the scalar field at each time step. In the second phase, the average consensus filter is used to allow each sensor node to find a confidence of the estimate at each time step. The final estimate of the value of the scalar field is iteratively updated during the movement of the mobile sensors via weighted average. Second, we develop the distributed flocking-control algorithm to drive the mobile sensors to form a network and track the virtual leader moving along the field when only a small subset of the mobile sensors know the information of the leader. Experimental results are provided to demonstrate our proposed algorithms.

Cooperative and Active Sensing in Mobile Sensor Networks for Scalar Field Mapping

Scalar field mapping has many applications including environmental monitoring, search and rescue, etc. In such applications, there is a need to achieve a certain level of confidence regarding the estimates of the scalar field. In this paper, a cooperative and active sensing framework is developed to enable scalar field mapping using multiple mobile sensor nodes. The cooperative and active controller is designed via the real-time feedback of the sensing performance to steer the mobile sensors to new locations in order to improve the sensing quality. During the movement of the mobile sensors, the measurements from each sensor node and its neighbors are fused with the corresponding confidences using distributed consensus filters. As a result, an online map of the scalar field is built while achieving a certain level of confidence of the estimates. We conducted computer simulations to validate and evaluate our proposed algorithms.

Developing a crack inspection robot for bridge maintenance

One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and finds the location of cracks manually. Thus the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a system that uses a mobile robot to conduct the inspection, where the robot collects bridge deck images with a high resolution camera. In this method, the Laplacian of Gaussian algorithm is used to detect cracks and the global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, we develop a complete coverage path planning algorithm for the mobile robot. We compare it with other path planning strategies. Finally, we validate our proposed system through experiments and simulation.

Mechatronic Systems Design for an Autonomous Robotic System for High-Efficiency Bridge Deck Inspection and Evaluation

The condition of bridges is critical for the safety of the traveling public. Bridges deteriorate with time as a result of material aging, excessive loading, environmental effects, and inadequate maintenance. The current practice of nondestructive evaluation (NDE) of bridge decks cannot meet the increasing demands for highly efficient, cost-effective, and safety-guaranteed inspection and evaluation. In this paper, a mechatronic systems design for an autonomous robotic system for highly efficient bridge deck inspection and evaluation is presented. An autonomous holonomic mobile robot is used as a platform to carry various NDE sensing systems for simultaneous and fast data collection. The robots NDE sensor suite includes ground penetrating radar arrays, acoustic/seismic arrays, electrical resistivity sensors, and video cameras. Besides the NDE sensors, the robot is also equipped with various onboard navigation sensors such as global positioning system (GPS), inertial measurement units (IMU), laser scanner, etc. An integration scheme is presented to fuse the measurements from the GPS, the IMU and the wheel encoders for high-accuracy robot localization. The performance of the robotic NDE system development is demonstrated through extensive testing experiments and field deployments.

A Robotic Crack Inspection and Mapping System for Bridge Deck Maintenance

One of the important tasks for bridge maintenance is bridge deck crack inspection. Traditionally, a human inspector detects cracks using his/her eyes and marks the location of cracks manually. However, the accuracy of the inspection result is low due to the subjective nature of human judgement. We propose a crack inspection system that uses a camera-equipped mobile robot to collect images on the bridge deck. In this method, the Laplacian of Gaussian (LoG) algorithm is used to detect cracks and a global crack map is obtained through camera calibration and robot localization. To ensure that the robot collects all the images on the bridge deck, a path planning algorithm based on the genetic algorithm is developed. The path planning algorithm finds a solution which minimizes the number of turns and the traveling distance. We validate our proposed system through both simulations and experiments.

Automated Crack Detection on Concrete Bridges

Detection of cracks on bridge decks is a vital task for maintaining the structural health and reliability of concrete bridges. Robotic imaging can be used to obtain bridge surface image sets for automated on-site analysis. We present a novel automated crack detection algorithm, the STRUM (spatially tuned robust multifeature) classifier, and demonstrate results on real bridge data using a state-of-the-art robotic bridge scanning system. By using machine learning classification, we eliminate the need for manually tuning threshold parameters. The algorithm uses robust curve fitting to spatially localize potential crack regions even in the presence of noise. Multiple visual features that are spatially tuned to these regions are computed. Feature computation includes examining the scale-space of the local feature in order to represent the information and the unknown salient scale of the crack. The classification results are obtained with real bridge data from hundreds of crack regions over two bridges. This comprehensive analysis shows a peak STRUM classifier performance of 95% compared with 69% accuracy from a more typical image-based approach. In order to create a composite global view of a large bridge span, an image sequence from the robot is aligned computationally to create a continuous mosaic. A crack density map for the bridge mosaic provides a computational description as well as a global view of the spatial patterns of bridge deck cracking. The bridges surveyed for data collection and testing include Long-Term Bridge Performance programs (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.

Dynamic target tracking and observing in a mobile sensor network

This paper presents novel approaches to (1) the problem of flocking control of a mobile sensor network to track and observe a moving target and (2) the problem of sensor splitting/merging to track and observe multiple targets in a dynamic fashion. First, to deal with complex environments when the mobile sensor network has to pass through a narrow space among obstacles, we propose an adaptive flocking control algorithm in which each sensor can cooperatively learn the networks parameters to decide the network size in a decentralized fashion so that the connectivity, tracking performance and formation can be improved. Second, for multiple dynamic target tracking, a seed growing graph partition (SGGP) algorithm is proposed to solve the splitting/merging problem. To validate the adaptive flocking control we tested it and compared it with the regular flocking control algorithm. For multiple dynamic target tracking, to demonstrate the benefit of the SGGP algorithm in terms of total energy and time consumption when sensors split, we compared it with the random selection (RS) algorithm. Several experimental tests validate our theoretical results.

Manipulability Optimization of Redundant Manipulators Using Dynamic Neural Networks

For solving the singularity problem arising in the control of manipulators, an efficient way is to maximize its manipulability. However, it is challenging to optimize manipulability effectively because it is a nonconvex function to the joint angles of a robotic arm. In addition, the involvement of an inversion operation in the expression of manipulability makes it even hard for timely optimization due to the intensively computational burden for matrix inversion. In this paper, we make progress on real-time manipulability optimization by establishing a dynamic neural network for recurrent calculation of manipulability-maximal control actions for redundant manipulators under physical constraints in an inverse-free manner. By expressing position tracking and matrix inversion as equality constraints, physical limits as inequality constraints, and velocity-level manipulability measure, which is affine to the joint velocities, as the objective function, the manipulability optimization scheme is further formulated as a constrained quadratic program. Then, a dynamic neural network with rigorously provable convergence is constructed to solve such a problem online. Computer simulations are conducted and show that, compared to the existing methods, the proposed scheme can raise the manipulability almost 40% on average, which substantiates the efficacy, accuracy, and superiority of the proposed manipulability optimization scheme.

Multirobot Cooperative Learning for Predator Avoidance

Multirobot collaboration has great potentials in tasks, such as reconnaissance and surveillance. In this paper, we propose a multirobot system that integrates reinforcement learning and flocking control to allow robots to learn collaboratively to avoid predator/enemy. Our system can conduct concurrent learning in a distributed fashion as well as generate efficient combination of high-level behaviors (discrete states and actions) and low-level behaviors (continuous states and actions) for multirobot cooperation. In addition, the combination of reinforcement learning and flocking control enables multirobot networks to learn how to avoid predators while maintaining network topology and connectivity. The convergence and scalability of the proposed system are investigated. Simulations and experiments are performed to demonstrate the effectiveness of the proposed system.

Adaptive flocking control for dynamic target tracking in mobile sensor networks

Target tracking is an important task in sensor networks, especially in mobile sensor networks. Flocking control is used to control a mobile sensor network to track a target. However, there are some existing problems in this control method, such as network fragmentation, loss of formation and poor tracking performance. In order to handle these problems we propose a novel approach to flocking control of a mobile sensor network to track a moving target within changing environments. In our approach, each agent can cooperatively learn the networks parameters to decide the size of network in a decentralized fashion so that the connectivity, formation and tracking performance can be improved when avoiding obstacles. In addition, to demonstrate the benefit of our approach a comparison between this approach and the existing method is given. Computer simulations are performed to demonstrate the effectiveness of the proposed approach.