Field and Service Robotics
Autonomous Robotic Teams for a Real-Time Water Monitoring
Water monitoring is an important task to conserve natural resources such as rivers and lakes. For a long period of time water monitoring methods have been based on the human activities. For example, water sampling is the most common human activity. However, this technique entails some inherent disadvantages. In case of a large river with fast flow, it is dangerous to the samplers. Moreover, it is slow and costly because human samplers should travel to sites for conducting a water sampling on their own. Also, it is inefficient for a long-term and real-time monitoring. Recently, autonomous water monitoring systems composed of mobile sensors for real time monitoring and data collection have been introduced. Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vehicle (USV) are proposed to overcome the disadvantages of the human based methods.
The goal of this research is to develop a continuous, real-time autonomous river monitoring system. We are planning to develop the autonomous robotic teams that integrate a variety of technologies including robots, crowdsourcing, advanced region of interest (ROI) selection and path planning. For this research ‘Wabash River’ is the targeted test site because it is the one of the longest rivers in the US and main stream in Indiana state. This research is at the beginning stage. Based on the needs assessment, this system will be iteratively designed to be fully capable of various tasks, such as water sampling, water pollution monitoring, sediment sampling, and early flood warning.
A Directional Antenna-based Leader-follower Robotic Convoy System
In this research, we present a directional antenna-based leader-follower robotic relay system capable of building end-to-end communication in complicated and dynamically changing environments. The proposed system consists of multiple networked robots - one is a mobile end node and the others are leaders or followers acting as radio relays. Every follower uses directional antennas to relay a communication radio and to estimate the location of the leader robot as a sensory device. For bearing estimation, we employ a weight centroid algorithm (WCA) and present a theoretical analysis of the use of WCA for this work. Using a robotic convoy method, we develop online, distributed control strategies that satisfy the scalability requirements of robotic network systems and enable cooperating robots to work independently. The performance of the proposed system is evaluated by conducting extensive real-world experiments that successfully build actual communication between two end nodes.
Reliability and Safety of Autonomous Multi-Agent Systems
Today's autonomous cars, otherwise known as driverless vehicles or self-driving cars, enable the deployment of safety technologies, such as collision warning, automatic emergency braking, and Vehicle-to-Vehicle technologies. In the near future, these systems in all vehicles will help to achieve zero fatalities, zero injuries, and zero accidents. However, behind the potential of these innovations, there is new challenge on autonomous cars that still need to address: cybersecurity.
As the first step, we propose an attack-aware multi-sensor integration algorithm for the navigation system. A Fault Detection and Isolation (FDI) scheme is adopted for the detection of cyberattacks on navigation systems. Particularly, a discrete Extended Kalman Filter (EKF) is employed to construct robust residuals in the presence of noise. The proposed method uses a parametric statistical tool for detecting attacks based on the residuals in properties of discrete time signals and dynamic systems. It is based on a measurement history rather than a single measurement at a time. These approaches enable the proposed multi-sensor integration algorithm to generate a quick detection and low false alarms rate that are suitable to the applications of dynamic systems. Finally, as a case study, INS/GNSS integration for autonomous vehicle navigation systems is considered and tested with software-in-the-loop simulation (SILS).
In addition, we consider attack detection algorithms autonomous multi-vehicle systems with imperfect information. This research addresses how a locally controlled autonomous agent can be identified by other agents if it has been compromised and how to make decisions with the ultimate goal of recovering system functionality and safety.