A Framework for Cooperative Human-Aware Navigation and Coordination of Multi-Robot Systems in Social Environments

Continual developments in robotic technology have enabled the use of robots in everyday applications in domestic, office and public spaces. Although single robot problems have been the main focus of social robotics research, applications of robots in social environments will not be limited to a single robot due to the increasing demand for robotic assistants and multi-robot operations. Multi-robot systems can achieve performances exceeding the sum of the individual robot contributions by exploiting the full potential of the team through information sharing, coordination, and joint decision-making.

Robots operating in human-populated environments either directly interact with people or have to share the space with the humans. It is of utmost importance that people co-existing with robots feel safe and comfortable around them. This makes human-awareness essential for long-term sustainable deployment of robots in such environments. Furthermore, for cooperative robots, the presence of humans and their actions can directly affect the robot and team plans, making human-awareness more essential for ensuring high performance as well as social acceptability. Research in the area of socially-aware navigation has received substantial attention in recent years. However, despite their great potential, human-aware teams of robots considering social factors at both individual navigation and collective coordination and planning levels, are currently largely unexplored.

In this thesis, we address the problem of human-aware cooperative navigation and coordination for multi-robot systems in realistic social environments. We focus on a class of multi-robot coordination problems known as multi-robot task allocation using a market-based approach. We explicitly consider the challenges of noisy, dynamic and stochastic human-populated environments by means of accounting for perception and prediction limitations and uncertainties in social cost modeling, bid estimation, coordination, and replanning. We construct an end-to-end framework comprising three main components of (i) human-aware navigation, (ii) human-aware coordination and planning for multi-robot systems, and (iii) human-robot interaction in the presence of multiple cooperative robots.

We opt for an incremental approach to this problem starting from single robot human-aware navigation with expectation-based social costmaps. Subsequently, we move to multi-robot cooperative navigation in highly stochastic social environments. We propose human-aware coordination strategies based on social costs and social risks. The concept of risk introduced in this thesis incorporates perception and prediction uncertainties as well as social costs for estimating the stochastic costs of tasks that the robots should bid on in the market. Additionally, we introduce an adaptive risk-based replanning method for dealing with the limitations of local perception and unpredicted human behavior in the social environment. Finally, we demonstrate the interactive potential of the team of robots for social multi-robot task allocation by integrating an interaction that actively requests human collaboration and assistance in socially costly and blocking situations, into our adaptive replanning strategy. Extensive experiments with up to four robots and 12 humans in simulation, and up to two robots and two humans in reality have been carried out for evaluating the performance of the proposed methods in this thesis.