Intrusion Detection in Swarm Robotics
Mentor: Dr. Debbie Perouli
Description: When robots are organized in swarms, each robot coordinates with the rest in order to execute a collaborative task. Potential applications vary significantly and include warehouse cleaners, space explorers and nanorobots in veins and arteries. Although fault tolerance algorithms have been developed to some degree for such swarms, there are important security questions that remain open. This project focuses on detecting whether one or more members of the robotic swarm have been compromised. Building on preliminary work, the student(s) will develop, implement and test an intrusion detection algorithm for specific robotic swarms. The student(s) will be able to work with both simulators and real robots available at Marquette University’s research laboratories.
Several industries, such as healthcare and hospitality, are investigating the benefits of using social and service robots to cover some of their customers' needs. For example, a robot could interact with an older adult to decrease the feeling of loneliness or a robot could deliver items such as food or towels to a hotel guest. Successful deployment of social and service robots will likely lead to the introduction of more robotic units in the facility resulting in the formation of a swarm.
The objective of this project is to develop techniques that detect a hacked robot in the context of a small swarm (three to four robots). The project will focus on two types of abnormal robot behavior:
- The robot's location is different from the location it should have. This type of abnormality includes the robot spending significantly more or less time at a location that it was supposed to visit.
- The robot's psychological impact on the customer is negative.
Milestones and Goals
|Week 1: Orientation||
|Week 2: Study Related Work||
|Week 3: Learn About TurtleBots||
|Week 4: Form Research Hypothesis||
|Week 5: Design Methodology to Support or Disprove Hypothesis||
|Week 6: Start Writing Paper||
|Week 7: Build System||
|Week 8: Run Experiments||
|Week 9: Test hypothesis||
|Week 10: Presenting Research||