Research Overview

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.


Distributed Rendezvous Control in Cluttered Environments

   We consider the rendezvous problem as robots exploring the unknown environment with minimum communication and arrive at the selected rendezvous location. The problem of rendezvous is ubiquitous in nature. Animals in migration are able to share information about food and water thus the whole group rendezvous at those locations. Human also have same issue as we need to meet specific people in specific place, which is applied still in multi-agent robotic systems. With emerging technologies such as localization, ubiquitous wireless communication, and advanced computation capability, enhanced rendezvous control shall bring wider application scenarios like intelligent warehouse and urban search and rescue. The purpose of this research is to develop a bounded distributed rendezvous control mechanism in cluttered environment. The robots within this environment have basically none knowledge of the environment, but can rendezvous at the destination while conquering the limitations such as communication being blocked by large obstacles, and path blocked by small obstacles, with proper decision making mechanism and obstacle avoidance algorithms. Meanwhile, the efficiency in rendezvous is also considered, we try to figure out robotic rendezvous control which not only handles communication unavailable occasions and obstacle avoidance, but also maintain an efficiency-prior trajectory.

Social behavior in multi-robot systems

   Individuals can benefit in a social group by looking out for one another for support and survival. It is a proven phenomenon in nature and in this research our goal is to apply the same principles in a multi-robot system to improve robot survivability robustness.
   Traditionally, research on multi-robot systems has focused on developing application specific control algorithms while adapting individual robots in the group to operational environments and specific tasks without explicitly considering the advantages of being in a social group. However, given the unpredictable nature of various operational environments and autonomous mission requirements, designing individual robots that can take into account all possible scenarios is unfeasible, expensive and still lack robustness in survivability. In contrast, we believe introducing a social group aspect to the multi-robot system may provide a unique and robust way of dealing with such cases.
   For our initial work, social behavioral inspiration was taken from the Huddling behavior of Emperor Penguins in the Antarctic where they share body heat and take turns being in the huddle centers to survive conditions as severe as Antarctic winters as a group.
   Potential research on the topic include energy sharing between heterogeneous robotic agents, application of machine learning techniques for distributed position shuffling within the group to survive damaging external stimuli, distributed control techniques for cooperative object transportation specifically focusing on minimal individual health loss for long term survival of the multi-robot system.


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.