An Institutional Robotics Approach to the Design of Socially Aware Multi-Robot Behaviors

We propose an institutional robotics approachto the design of socially-aware multi-robot systems, wherecooperation among the robots and their social interactions withhumans are guided using institutions. Inspired by the conceptsstemming from economical sciences, robot institutions serve ascoordination artifacts, which specify behavioral rules that areacceptable or desirable given the situation and which can bereplaced by other rules to enforce new acceptable or desirablebehaviors without changing the robot’s core code. In this paperwe propose a formal methodology for consistent design ofcoordinated multi-robot behaviors intended for use in human-populated environments. We illustrate theoretical concepts withpractical examples. Graph-based formations serve as a basisfor coordinated multi-robot behaviors and concepts from theliterature on human-aware navigation provide social rules thatare enforced by the institutions. Experiments are carried outin a high-fidelity robotic simulator to illustrate the applicationof the theoretical concepts

A Robust Relative Positioning System for Multi-Robot Formations Leveraging an Extended GM-PHD Filter

We propose a multi-robot tracking method to provide state estimates that allow a group of robots to maintain a formation even when the communication fails. We extend a Gaussian Mixture Probability Hypothesis Density filter to incorporate, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. Sensory detections, information about the formation, and communicated data are all combined in the extended Gaussian Mixture Probability Hypothesis Density filter. Our method is capable of maintaining the state estimates even when long-duration occlusions occur, and improves awareness of the situation when the communication rate is slow or sporadic. The method is evaluated using a high-fidelity simulator in scenarios with a formation of up to five robots. Experiments confirm the ability of the filter to deal with occlusions and refinement of the state estimate even when poses are exchanged at a low frequency, resulting in drastic reduction of the chance of collisions compared to a tracking-free implementation

Towards Institutions for Mixed Human-Robot Societies

We report an exploration into normative reasoning for robots in human societies using the concept of institutions

Towards Norm Realization in Institutions Mediating Human-Robot Societies

Social norms are the understandings that govern the behavior of members of a society. As such, they regulate communication, cooperation and other social interactions. Robots capable of reasoning about social norms are more likely to be recognized as an extension of our human society. However, norms stated in a form of the human language are inherently vague and abstract. This allows for applying norms in a variety of situations, but if the robots are to adhere to social norms, they must be capable of translating abstract norms to the robotic language. In this paper we use a notion of institution to realize social norms in real robotic systems. We illustrate our approach in a case study, where we translate abstract norms into concrete constraints on cooperative behaviors of humans and robots. We investigate the feasibility of our approach and quantitatively evaluate the performance of our framework in 30 real experiments with user-based evaluation with 40 participants

Graph-Based Distributed Control for Adaptive Multi-Robot Patrolling through Local Formation Transformation

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real- world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity