In the rapidly evolving field of robotics, significant progress has been made in the planning, control, and coordination of multi-robot systems, embedding robots into various sectors such as household, manufacturing, healthcare, and surveillance. Despite these advancements, challenges arise, particularly concerning privacy due to robots' potential to access and share more information than necessary, risking sensitive data exposure. Addressing this, our research introduces innovative strategies to ensure collaborative computation among robots while safeguarding privacy, thereby preventing unnecessary information sharing and achieving optimal objectives. We propose lightweight communication protocols for data synchronization, reducing the need for extensive data exchange, and a secure multiparty auction-based algorithm for private task allocation without revealing sensitive data. Additionally, we explore the use of secure multiparty computation with Markov Decision Processes (MDP) for planning, ensuring privacy in multi-agent cooperation. Building on this foundation, we delve into decentralized multi-robot information gathering (DMRIG), presenting the Asynchronous Information Gathering with Bayesian Optimization (AsyncIGBO) and Distributed and Decentralized Robotic Information Gathering (DDRIG) algorithms to improve environmental monitoring data collection efficiency, balancing communication complexity, and privacy. Through practical experimentation, these algorithms' real-world efficacy is demonstrated, emphasizing their role in enhancing environmental monitoring via sophisticated information sharing and task allocation among robots. This dissertation provides a comprehensive approach to addressing privacy and efficiency in heterogeneous robot systems, showcasing the potential of these technologies to advance robotics applications securely and effectively. Together, these components form a comprehensive approach to addressing privacy concerns in heterogeneous robot systems. By interlinking efficient data sharing protocols, secure task allocation, private planning strategies, and optimized multi-robot information gathering, the dissertation lays the groundwork for a new paradigm in robotic collaboration. This synergy ensures that robots can work together effectively, achieving optimal objectives without compromising sensitive information, marking a significant advancement in the field of robotics.

Murtadha Alsayegh is a Ph.D. candidate at the Knight Foundation School of Computing and Information Sciences (KFSCIS) at Florida International University (FIU), specializing in Security in Robotics. His research focuses on Secure Multiparty Computation (SMPC), Privacy Preservation, and Secure Motion Planning, conducted at the Motion, Robotics, and Automation Lab (MORA Lab). Murtadha obtained a Master’s degree in Software Engineering in 2013 from the University of Michigan-Dearborn and a B.S. in Computer Science from Lawrence Technological University in 2010. As a Ph.D. student, he received a scholarship from the Saudi Arabian Cultural Mission (SACM). Prior to his doctoral studies, Murtadha worked as a Senior Automation Engineer at Schneider Electric – Saudi Arabia, in 2013, where he enhanced the security of several supervisory control and data acquisition systems against cyber threats and attacks in major power plants. Murtadha is the first author of several peer-reviewed publications presented at prestigious venues such as the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), European Control Conferences (ECC), and IEEE Conference on Decision and Control (CDC), and has co-authored papers in the IEEE Robotics and Automation Letters (RA-L) journal.