OUR CURRENT RESEARCH
Communication As A Sensor
Multi-robot Sequential Decision Making
Resilience in Multi-robot Coordination
Enabling robots to leverage their local motion and received wireless signals to obtain relative information of their neighboring robots. Applications to Pose Graph Optimization and SLAM, Rendezvous, amongst others.
Decision-making under uncertainty with multiple-robots systems is essential for various applications. Real-world robotic sequential decision problems entail several challenges, including partial observation, dynamic environment, a large state space due to a complex system, and a large control space given by multiple agents, partial communication. We consider infinite-horizon discounted Markov decision problems under partial observation (POMDP) with finite, discrete state and control space.
In order for robots to coordinate effectively, they often must communicate with each other by sharing information. However, this communication can be affected by the presence of malicious robots who can alter the information they send and receive to prevent successful coordination. In this research we develop methods to provide resilience to the multi-robot system, allowing them to still function and coordinate in the presence of malicious robots. We apply the methods to various multi-robot tasks and demonstrate their effectiveness with applications in persistent surveillance, flocking, distributed detection, and consensus among others.