While wearable robots that utilize intrinsically soft materials for actuation offer enhanced safety and biological compatibility, the challenges of sensing and control significantly affect their performance. The control problem in such systems is inherently complex, and the inclusion of 'softness' introduces additional nonlinearities, hysteresis, and uncertainties. Furthermore, the effectiveness of control strategies is highly dependent on sensor selection and integration, which presents its own challenges. Most robotic systems require separate sensors for control purposes. In this study, a new sensing and control scheme are introduced for soft wearable robots, leveraging the intrinsic soft-sensing capability of fluidic filament actuators without adding computational complexity. This method enables simultaneous sensing and actuation with$\mathbf{9 6 \%}$position accuracy, even under physical disturbances. This approach is demonstrated with a soft assistive device for elbow flexion/extension, achieving 70.5% tracking accuracy and a 0.09s response delay to human intention, ensuring the system provides minimal resistance when assistance is not needed, while delivering the required support when necessary.

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

Culverts, essential components of drainage systems, require regular inspection to ensure optimal functionality. However, culvert inspections pose numerous challenges, including accessibility, manpower, defect localization, and reliance on superficial assessments. To address these challenges, we propose a novel Culvert Autonomous Inspection Robotic System (CAIS) equipped with advanced sensing and evaluation capabilities. Our solution integrates an RGBD camera, deep learning, lighting systems, and non-destructive evaluation (NDE) techniques to enable accurate and comprehensive condition assessments. We present a pioneering Partially Observable Markov Decision Process (POMDP) framework to resolve uncertainty in autonomous inspections, especially in confined and unstructured environments like culverts or tunnels. The framework outputs detailed 3D maps highlighting visual defects and NDE condition assessments, demonstrating consistent and reliable performance in both indoor and outdoor scenarios. Additionally, we provide an open-source implementation of our framework on GitHub, contributing to the advancement of autonomous inspection technology and fostering collaboration within the research community. Source codes are available*.

This work presents a comprehensive LiDAR-inertial odometry framework featuring robust smoothing and mapping capabilities, effectively correcting LiDAR feature point skewness using an inertial measurement unit (IMU). While the Extended Kalman Filter (EKF) is a common choice for nonlinear motion estimation, its complexity grows when handling maneuvering targets. To overcome this challenge, a new framework that incorporates the Iterated Interactive Multiple Models of Kalman Filter (IMMKF) is given, providing a solution for reliable navigation in dynamic motion and noisy conditions. To ensure map consistency, an ikd-tree that facilitates continuous updates and adaptive rebalance is employed, preserving the map’s integrity. To guarantee the robustness of our approach, it undergoes extensive testing across diverse scales of indoor and outdoor environments. This testing scenario simulates absolute GPS denial. In terms of estimated motion, the new algorithm demonstrates superior accuracy compared to existing approaches. The implementation is openly accessible on GitHub4for further exploration.

Following biology's lead, soft robotics has emerged as a perfect candidate for actuation within complex environments. While soft actuation has been developed intensively over the last few decades, soft sensing has so far slowed to catch up. A largely unresearched area is the change of the soft material properties through prestress to achieve a degree of mechanical sensitivity tunability within soft sensors. Here, a new 3D force sensor which employs novel hydraulic filament artificial muscles capable of in-situ sensitivity tunability is introduced. Using a neural network (NN) model, the new soft 3D sensor can precisely detect external forces based on the change of the hydraulic pressures with error of$\sim 1.0, \sim 1.3$, and$\sim 0.94$% in the$\text{x, y}$, and z-axis directions, respectively. The sensor is also able to sense large force ranges, comparable to other similar sensors available in the literature. The sensor is then integrated into a soft robotic surgical arm for monitoring the tool-tissue interaction during an ablation process.

Atrial fibrillation (AF) is mostly treated via robotic catheter-based cardiac ablation procedures. Over the last few decades, cables or tendon mechanisms are at the core of available cardiac catheters. Despite advances, the use of cables often results in considerable force loss, nonlinear hysteresis, and control challenges. Most catheters are not equipped with force sensing, which increases the risk of the ablation process and decreases their efficacy in clinical settings. In addition, current catheters have a poor user interface and therefore the ablation process requires skilled or trained surgeons to steer the complex motion of the catheter tip within the heart chambers. To improve the cardiac ablation procedure, a new robotic catheter that has the ability to extend its working space without moving its flexible body and a real-time force sensor for safe operation is highly desired. In this work, a new handheld and soft robotic catheter for AF ablation is introduced. The new device consists of several improved components such as a soft manipulator for navigation and bending motion, an ergonomic handheld controller, and a soft force sensor for monitoring tool-tissue contact. The design, modeling, and fabrication of the device are presented and followed by experimental characterizations and ex-vivo validation.

Although experience sharing (ES) accelerates multiagent reinforcement learning (MARL) in an advisor-advisee framework, attempts to apply ES to decentralized multiagent systems have so far relied on trusted environments and over-looked the possibility of adversarial manipulation and inference. Nevertheless, in a real-world setting, some Byzantine attackers, disguised as advisors, may provide false advice to the advisee and catastrophically degrade the overall learning performance. Also, an inference attacker, disguised as an advisee, may conduct several queries to infer the advisors' private information and make the entire ES process questionable in terms of privacy leakage. To address and tackle these issues, we propose a novel MARL framework (BRNES) that heuristically selects a dynamic neighbor zone for each advisee at each learning step and adopts a weighted experience aggregation technique to reduce Byzantine attack impact. Furthermore, to keep the agent's private information safe from adversarial inference attacks, we leverage the local differential privacy (LDP)-induced noise during the ES process. Our experiments show that our framework outperforms the state-of-the-art in terms of the steps to goal, obtained reward, and time to goal metrics. Particularly, our evaluation shows that the proposed framework is 8.32x faster than the current non-private frameworks and 1.41x faster than the private frameworks in an adversarial setting.

This paper presents a simple but compact design of a bicycle-like robot for inspecting complex-shaped ferromagnetic structures. The design concept for versatile locomotion relies on two independently steered magnetic wheels formed in a bicycle-like configuration, allowing the robot to possess multi-directional mobility. The key feature of a reciprocating mechanism enables the robot to change its shape when passing obstacles. A dynamic joint of the robot configuration makes it naturally adapt to uneven and complex surfaces of steel structures. We demonstrate the usability and practical deployment of the robot for steel thickness measurement using an ultrasonic sensor.

This paper presents a novel design of a multi-directional bicycle robot, which is developed for the inspection of steel structures, in particular, steel-reinforced bridges. The locomotion concept is based on arranging two magnetic wheels in a bicycle-like configuration with two independent steering actuators. This configuration allows the robot to possess multi-directional mobility. An additional free joint helps the robot adapt naturally to non-flat and complex steel structures. The robot's design provides the advantage of being mechanically simple and providing high-level mobility across diverse steel structures. In addition, a visual sensor is equipped that allows the data collection for steel defect detection with offline training and validation. The paper also provides a novel pipeline for Steel Defect Detection, which utilizes multiple datasets (one for training and one for validation) from real bridges. The quantitative results have been reported for three Deep Encoder-Decoder Networks (i.e., LinkNet, UNet, DeepLab) with their corresponding Encoder modules (i.e., ResNet-18, ResNet-34, RegNet-X2, EfficientNet-B0, and EfficientNet-B2). Due to space concerns, the qualitative results have been outlined in Appendix, with a link in Fig. 11 caption to access the result provided.

Assistive robots can be found in hospitals and rehabilitation clinics, where they help patients maintain a positive disposition. Our proposed robotic mobility solution combines state of the art hardware and software to provide a safer, more independent, and more productive lifestyle for people with some of the most severe disabilities. New hardware includes, a retractable roof, manipulator arm, a hard backpack, a number of sensors that collect environmental data and processors that generate 3D maps for a hands-free human-machine interface.The proposed new system receives input from the user via head tracking or voice command, and displays information through augmented reality into the user’s field of view. The software algorithm will use a novel cycle of self-learning artificial intelligence that achieves autonomous navigation while avoiding collisions with stationary and dynamic objects. The prototype will be assembled and tested over the next three years and a publicly available version could be ready two years thereafter.

Recent large wildfires and subsequent damage have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and we thus investigate deploying Unmanned Aerial Vehicles (UAVs) to track wildfires. Specifically, we attack the problem of distributed autonomous control of UAVs using a set of potential fields to track wildfire boundaries. A multiobjective evolutionary algorithm searches through the space of potential field parameters to maximize fire coverage while minimizing energy consumption. Fire spread is modelled by the well known FARSITE fire model. Preliminary simulation results show that our potential fields approach to UAV control leads to 100% coverage of the boundary by UAVs and 78.1% energy remaining on three testing scenarios.

The advanced robotic and automation (ARA) lab has developed and successfully implemented a design inspired by many of the various cutting edge steel inspection robots to date. The combination of these robots concepts into a unified design came with its own set of challenges since the parameters for these features sometimes conflicted. An extensive amount of design and analysis work was performed by the ARA lab in order to find a carefully tuned balance between the implemented features on the ARA robot and general functionality. Having successfully managed to implement this conglomerate of features represents a breakthrough to the industry of steel inspection robots as the ARA lab robot is capable of traversing most complex geometries found on steel structures while still maintaining its ability to efficiently travel along these structures; a feat yet to be done until now.

Autonomous navigation of steel bridge inspection robots are essential for proper maintenance. Majority of existing robotic solutions for bridge inspection require human intervention to assist in the control and navigation. In this paper, a control system framework has been proposed for a previously designed ARA robot [1], which facilitates autonomous real-time navigation and minimizes human involvement. The mechanical design and control framework of ARA robot enables two different configurations, namely the mobile and inch-worm transformation. In addition, a switching control was developed with 3D point clouds of steel surfaces as the input which allow the robot to switch between mobile and inch-worm transformation. The surface availability algorithm (considers plane, area and height) of the switching control enables the robot to perform inch-worm jumps autonomously. The mobile transformation allows the robot to move on continuous steel surfaces and perform visual inspection of steel bridge structures. Practical experiments on actual steel bridge structures highlight the effective performance of ARA robot with the proposed control framework for autonomous navigation during visual inspection of steel bridges.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas that will likely spread next. Lack of information of the fire front causes many accidents. Using unmanned aerial vehicles (UAVs) to cover wildfire is promising because it can replace humans in hazardous fire tracking and significantly reduce operation costs. In this paper, we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

Motivated by a high demand for automated inspection of civil infrastructure, this work presents an efficient design and development of a tank-like robot for structural health monitoring. Unlike most existing magnetic wheeled mobile robot designs, which may be suitable for climbing on flat steel surface, our proposed tank-like robot design uses reciprocating mechanism and roller-chains to make it capable of climbing on different structural shapes (e.g., cylinder, cube) with coated or non-coated steel surfaces. The developed robot is able to pass through the joints and transition from one surface to the other (e.g., from flat to curving surfaces). Taking into account several strict considerations (including tight dimension, efficient adhesion and climbing flexibility) to adapt with various shapes of steel structures, a prototype tank-like robot integrating multiple sensors (hall-effects, sonars, inertial measurement unit, Eddy current and cameras), has been developed. Rigorous analysis of robot kinematics, adhesion force, sliding failure and turn-over failure has been conducted to demonstrate the stability of the proposed design. Mechanical and magnetic force analysis together with sliding/turn-over failure investigation can serve as an useful framework for designing various steel climbing robots in the future. The robot is integrated with cameras and Eddy current sensor for visual and in-depth fatigue crack inspection of steel structures. Experimental results and field deployments confirm the adhesion, climbing, inspection capability of the developed robot.

Unmanned aerial vehicles, commonly known as drones, have many potential uses in real-world applications. Drones require advanced planning and navigation algorithms to enable them to safely move through and interact with the world around them. This paper presents an extended potential field controller (ePFC) which enables an aerial robot, or drone, to safely track a dynamic target location while simultaneously avoiding any obstacles in its path. The ePFC outperforms a traditional potential field controller with smoother tracking paths and shorter settling times. The proposed ePFC's stability is evaluated by Lyapunov approach, and its performance is simulated in a MATLAB environment. Finally, the controller is implemented on an experimental platform in a laboratory environment which demonstrates the effectiveness of the controller.

A genetic algorithm (GA), is used to optimize the many parameters of a convolutional neural network (CNN) that control the structure of the network. CNNs are used in image classification problems where it is necessary to generate feature descriptors to discern between image classes. Because of the deep representation of image data that CNNs are capable of generating, they are increasingly popular in research and industry applications. With the increasing number of use cases for CNNs, more and more time is being spent to come up with optimal CNN structures for different applications. Where one CNN might succeed at classification, another can fail. As a result, it is desirable to more easily find an optimal CNN structure to increase classification accuracy. In the proposed method, a GA is used to evolve the parameters that influence the structure of a CNN. The GA compares CNNs by training them on images of concrete containing cracks. The best CNN after several generations of the GA is then compared to the state-of-the-art CNN for crack detection. This work shows that it is possible to generalize the process of optimizing a CNN for image classification through the use of a GA.

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments.

Satisfactory operation of civil infrastructure is of critical importance to an economy. In order to maintain performance, infrastructure needs to be properly maintained. Inspecting infrastructure is inherently labor-intensive work and costly. In this paper, we propose a solution to cost-effective infrastructure inspection by developing a multi-functional inspection robot. The robot is equipped with several state-of-the-art non-destructive evaluation (NDE) sensors to perform inspection. The robot is able to perform selected inspection methods in certain areas based on multiple sensor data fusion. With this, the overall inspection time is reduced, which in turn reduces maintenance cost. An inspection framework based on multiple NDE data sensor fusion is proposed. Detailed discussions include robot design, robot navigation and sensor data fusion.

Wild-land fire fighting is a hazardous job. A key task for firefighters is to observe the “fire front” to chart the progress of the fire and areas it will likely spread next. Lack of information of the fire front causes many accidents. Using Unmanned Aerial Vehicles (UAV) to cover wildfire is promising because it can replace humans for fire tracking, reducing hazards and saving operation costs. In this paper we propose a distributed control framework designed for a team of UAVs that can closely monitor a wildfire in open space, and precisely track its development. The UAV team, designed for flexible deployment, can effectively avoid in-flight collisions and cooperate well with neighbors. They can maintain a certain height level to the ground for safe flight above fire. Experimental results are conducted to demonstrate the capabilities of the UAV team in covering a spreading wildfire.

It is necessary for a mobile robot to be able to efficiently plan a path from its starting, or current location to a desired goal location. This is a trivial task when the environment is static. However, the operational environment of the robot is rarely static, and it often has many moving obstacles. The robot may encounter one, or many of these unknown and unpredictable moving obstacles. The robot will need to decide how to proceed when one of these obstacles is obstructing it's path. A method of dynamic replanning using RRT* is presented. The robot will modify it's current plan when an unknown random moving obstacle obstructs the path. Various experimental results show the effectiveness of the proposed method.

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this work, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from the Beta component of a user's EEG. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe ‘home’ location. This system has been validated on a commercial UAV under malicious attack conditions.

A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.

This paper presents a computer vision-based method to automatically detect concrete cracks. We focus on images containing the concrete: background and crack, where the background is the major mode of the gray-scale histogram. Therefore, we address the detection problem of potential concrete cracks by dealing with histogram thresholding to extract regions of interests from the background. We first employ line emphasis and moving average filters to remove noise from concrete surface images obtained from an inspection robot. The developed algorithm is then applied for automatic detection of significant peaks from the gray-scale histogram of the smoothed image. The biggest peak and its corresponding valley(s) are consequently identified to calculate the threshold value for image binarization. The effectiveness of our proposed method was successfully evaluated on various test images, where cracks could be identified without the requirement of some heuristic reasoning.

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 program's (LTBP) pilot project bridges at Haymarket, VA, USA, and Sacramento, CA, USA.

One challenging problem for human-machine systems is to accurately estimate the position, velocity, and attitude of human foot motion, using an inertial measurement unit (IMU) sensor. This is particularly so in large environments affected by local magnetic disturbances. In this paper, we propose an algorithm that not only handles this problem, but also works efficiently in real time. The novelty of this paper lies mainly in two contributions: First, we propose a dynamic gait phase detection (GPD) method that can detect human foot gait phase with high accuracy (2.78% errors) in dynamic speeds of human foot motion, such as walking or running; second, we integrate an inertial navigation system, a GPD, a zero velocity update, and an extended Kalman filter in real time. The system can, thus, handle the IMU drift problem, as well as noise, for high-accuracy localization both indoors (0.375% errors) and outdoors (0.55% errors). To validate the proposed algorithm, we apply the motion-tracking system (MTS-ground truth), and the results show that 93.7% of the proposed algorithm's results converge on the MTS's results within a distance of less than 7.5 cm. Hence, the proposed algorithm can be embedded in wearable sensor devices for practical applications.

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.

Bridge deck inspection is conducted to identify bridge condition deterioration and, thus, to facilitate implementation of appropriate maintenance or rehabilitation procedures. In this paper, we report the development of a robotic system for bridge deck data collection and analysis. The robotic system accurately localizes itself and autonomously maneuvers on the bridge deck to collect visual images and conduct nondestructive evaluation (NDE) measurements. The developed robotic system can reduce the cost and time of the bridge deck data collection. Crack detection and mapping algorithm to build the deck crack maps is presented in detail. The electrical resistivity (ER), impact-echo (IE) and ultrasonic surface waves (USW) data collected by the robot are analyzed to generate the corrosion, delamination and concrete elastic modulus maps of the deck. The presented robotic system has been successfully deployed to inspect numerous bridges.

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.

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.

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.

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.

Birds, bees, and fish often flock together in groups to find the source of food (target) based on local information. Inspired by this natural phenomenon, a flocking control algorithm is designed to coordinate the activities of multiple agents in noisy environments. Based on this algorithm, all agents can form a network and maintain connectivity. This is of great advantage for agents to exchange information. In addition, collision avoidance among agents is guaranteed in the whole process of target tracking. We show that even with noisy measurements the flocks can achieve cohesion and follow the moving target. We also investigate the stability and scalability of our algorithm. The numerical simulations are performed to demonstrate the effectiveness of the proposed algorithm.

This paper presents a new approach to flocking control of a mobile sensor network to track a moving target. In our approach, the center of mass (CoM) of positions and velocities of all mobile sensors in the network (Single-CoM) or the center of mass of position and velocity of each sensor and its neighbors (Multi-CoM) is controlled to track and observe a moving target. In addition, we prove that the CoM of position and velocity exponentially converges to the moving target in free space. Based on this approach, the target is kept at the center of the sensor network. This is of great advantage for sensors to track and observe the target for recognition or identification purposes. In addition, collision-free and velocity matching among mobile sensors are guaranteed in the whole process of the target tracking. We also investigate the stability of our algorithms. The numerical simulations are performed to demonstrate the proposed approach.

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 network's 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.

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 the problem of how to design an optimal flocking control algorithm with optimal flocking parameters to allow the network to catch up the target as fast as possible in order to minimize the tracking time and power consumption. This paper presents an optimization problem in flocking control for a mobile sensor network to track a moving target. A non convex optimization method based on genetic algorithms is developed. The overall purpose of this approach is to find out the optimal solutions of flocking parameters that deliver desired swarm behaviors to minimize the cost function. This cost function represents the time it takes all mobile sensors (robots) in the network to catch up the moving target. The experimental tests are obtained to demonstrate our approach.