A Distributed Strategy for Human-in-the-loop Task Servicing using Multiple Robots with Stationary Base Station Connectivity Constraint

Mobile robots are increasingly being used for tasks like remote surveillance, sensing and maintenance. Some of these tasks are critical and require intelligent decision making for successful completion. It is not always possible to rely exclusively on robot level intelligence to make high impact decisions and hence human supervision is needed during task execution. To facilitate human-in-the-loop task servicing, the task executing robot is required to remain connected to a remotely located human operator. However, robot communication range is typically limited and hence multiple mobile robots might be deployed to perform the tasks. These robots must coordinate with each other to dynamically form and maintain a communication link such that network connectivity exists between the robot servicing the task and the human operator positioned at a stationary base station. The development of connectivity aware coordination algorithms is complex due to limited communication range and presence of obstacles in the search region. In this thesis, we present a distributed multi-robot algorithm for task servicing with human-in-the-loop constraint. Robot control and mission execution is independent of the human operator and is fully autonomous. The algorithm facilitates indirect collaboration amongst the robotic agents and uses a combination of graph theoretic and gradient descent based approaches for path planning. Robots exercise independent decision making on task and role assignment by following a self allocation strategy. This allows dynamic task reassignments and role exchanges amongst the agents based on increased situational awareness. Our solution successfully implements obstacle avoidance and deadlock resolution while being scalable and robust to network and robot failures. To substantiate the claims, we present results from extensive simulations.