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Charles Pippin is a senior research scientist at the Georgia Tech Research Institute (GTRI). His research interests include collaborative autonomy algorithms, machine learning, multi-robot systems and unmanned systems. Pippin's research includes investigation on both indoor robot platforms performing patrolling tasks and on unmanned aerial vehicles (UAVs). He has led a team of researchers performing autonomous collaboration using GTRI’s research fleet of UAVs, resulting in multiple successful field demonstrations of collaborative autonomy with unmanned aerial and ground vehicles. Pippin was also a member of the DARPA program for Learning Applied to Ground Robotics (LAGR). In his current work, he is studying issues related to task sharing, as well as performance and trust on cooperative, multi-robot teams.
Trust on Multi-Robot Teams: Agents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. Yet, on current multi-robot teams, robots are typically expected to cooperate and perform as designed. There are many situations in which robots may not be interested in full cooperation, or may not be capable of performing as expected. In addition, the control strategy for robots may be fixed with no mechanism for modifying the team structure if teammate performance deteriorates. This research investigates the application of trust and reputation models for use on multi-robot teams and addresses the problem of how cooperation can be enabled through the use of incentive mechanisms. In this context, robots can reason about which of their peers are both capable and trustworthy partners.
Collaborative Unmanned Systems: GTRI’s fleet of five tactical UAVs perform on-board signal processing and execute collaborative autonomous behaviors. Multi-UAV formation flying has been demonstrated and cooperative ISR missions have been conducted. GTRI’s UAVs collaborate with UGVs for air-to-ground teaming in support of target detection, surveillance and distributed task allocation. The heterogeneous team of vehicles autonomously collaborates to execute complex missions.
Institute for Robotics & Intelligent Machines
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