@conference {1653952,
	title = {Adaptive malicious robot detection in dynamic topologies},
	booktitle = {IEEE Conference on Decisions and Control (CDC)},
	year = {2022},
	address = {Cancun, Mexico},
	abstract = {We consider a class of problems where robotsgather observations of each other to assess the legitimacyof their peers. Previous works propose accurate detection ofmalicious robots when robots are able to extract observations ofeach other for a long enough time. However, they often considerstatic networks where the set of neighbors a robot observesremains the same. Mobile robots experience a dynamic set ofneighbors as they move, making the acquisition of adequateobservations more difficult. We design a stochastic policy thatenables the robots to periodically gather observations of everyother robot, while simultaneously satisfying a desired robotdistribution over an environment modeled by sites. We showthat with this policy, any pre-existing or new malicious robot inthe network will be detected in a finite amount of time, whichwe minimize and also characterize. We derive bounds on thetime needed to obtain the desired number of observations for agiven topological map and validate these bounds in simulation.We also show and verify in a hardware experiment that theteam is able to successfully detect malicious robots, and thusestimate the true distribution of cooperative robots per site, inorder to converge to the desired robot distribution over sites.},
	author = {Matthew Cavorsi and Ninad Jadhav and David Salda{\~n}a and Stephanie Gil}
}
