Ronny Salim Lim

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.

Model-Based Plant Design and Hierarchical Control of a Prototype Lighter-Than-Air Wind Energy System, With Experimental Flight Test Results

This paper presents the modeling, control system design, and experimental results for a prototype lighter-than-air wind energy system being pioneered by Altaeros Energies. This unique design features a horizontal-axis turbine that is elevated to high altitudes through a buoyant shroud, which is tethered to a ground-based platform. The systems altitude can be adjusted to maximize power production, and because the system is both functional and economical in a stationary position, it circumvents many of the controls challenges faced by kite-based wind energy systems. However, the need for generation of energy introduces pointing, efficiency, and autonomy requirements, which are not faced by conventional aerostats, thereby requiring a careful model-based control design. In this paper, we provide a dynamic model of the Altaeros system, then show how this model is leveraged in the plant design and in the design of the control system, which provides full autonomy, from takeoff, through power production, to autonomous landing. We provide simulation and experimental results that demonstrate the performance of the prototype and point to important areas where Altaeros will focus its efforts moving forward.

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.

Autonomous robotic system for high-efficiency non-destructive bridge deck inspection and evaluation

Bridges are one of the critical civil infrastructure for safety of traveling public. The conditions of bridges deteriorate with time as a result of material aging, excessive loading, and inadequate maintenance, etc. In this paper, the development of an autonomous robotic system is presented for highly-efficient bridge deck inspection and evaluation. An autonomous mobile robot is used as a platform to carry various non-destructive evaluation (NDE) sensing systems for simultaneous and fast data collection. Besides the NDE sensors, the robot is also equipped with various onboard navigation sensors. A sensing integration scheme is presented for high-accuracy robot localization and navigation. The effectiveness of the autonomous robotic NDE system is demonstrated through extensive experiments and field deployments.

Development of a Small-Scale Research Platform for Intelligent Transportation Systems

In this paper, we propose and develop a small-scale research platform for intelligent transportation systems (ITSs). Our platform has four main parts, i.e., an arena, an indoor localization system, automated radio-controlled (RC) cars, and roadside monitoring facilities. First, to mimic traffic environments, we build an arena with a wooden floor, mock buildings, and streets. Second, to facilitate feedback control for trajectory following, an indoor localization system is set up to track the RC cars. Third, both autonomous driving RC cars and human driving RC cars are developed, based on an automated RC car design. The automated RC cars can receive control signals from a computer through an Xbee RF module and control the front and rear wheels through motors. A new control algorithm is developed to allow the RC cars to track predefined trajectories. Finally, we implement an example of roadside monitoring, which uses a fish-eye camera associated with advanced video processing for image segmentation, object identification, and tracking. Experiments are performed to demonstrate the effectiveness of the designed platform. We also discuss possible ITS research problems that can be studied in this testbed.

Decentralized flocking control with a minority of informed agents

In this paper we study the flocking control in the case of a small subset of informed agents. In nature, only few agents in a group have the information of the target, such as knowledge about the location of a food source, or the migration route. However, they can still flock together in a group based on local information. Inspired by this natural phenomenon, a flocking control algorithm is designed to coordinate the motion of multiple agents. Based on our algorithm, all agents can form a network, maintain connectivity and track the target even only very few of them know the information of the target. The experiments and simulations are performed to demonstrate the effectiveness of the proposed algorithm.

A small-scale research platform for intelligent transportation systems

In this paper, we propose and develop a small-scale research platform for intelligent transportation systems. Our platform has four main parts: an arena; an indoor localization system; automated radio controlled (RC) cars; and roadside monitoring facilities. First, to mimic the traffic environments we build an arena with a wooden floor, mock buildings and streets. Second, for the indoor localization system, a motion tracking system (Opti-Track) is set up to track the RC cars for control purpose. Third, for the automated RC cars, both manually and automatically controlled RC cars are developed. The automatic one is equipped with a micro controller, an Xbee RF module, a microphone and a speaker. The manual RC car is similar to the automatic one but equipped with a small wireless camera. The designed circuit inside the RC cars allow them to: (1) receive control signal from the computer through Xbee, and (2) control the front and rear wheels through motors. The control algorithm is developed to allow the RC car to track predefined trajectories. Fourth, we develop the roadside monitoring facilities, which consists of an IP-based fish-eye camera and the associated video processing modules including image segmentation, object identification and tracking. Several experiments are conducted to demonstrate the effectiveness of the designed platform.