While individual robots are becoming increasingly capable, the complexity of expected missions increases exponentially in comparison. To cope with this complexity, heterogeneous teams of robots have become a significant research interest in recent years. Making effective use of the robots and their unique skills in a team is challenging. Dynamic runtime conditions often make static task allocations infeasible, requiring a dynamic, capability-aware allocation of tasks to team members. To this end, we propose and implement a system that allows a user to specify missions using Behavior Trees (BTs), which can then, at runtime, be dynamically allocated to the current robot team. The system allows to statically model an individual robot’s capabilities within our ros_bt_py BT framework. It offers a runtime auction system to dynamically allocate tasks to the most capable robot in the current team. The system leverages utility values and pre-conditions to ensure that the allocation improves the overall mission execution quality while preventing faulty assignments. To evaluate the system, we simulated a find-and-decontaminate mission with a team of three heterogeneous robots and analyzed the utilization and overall mission times as metrics. Our results show that our system can improve the overall effectiveness of a team while allowing for intuitive mission specification and flexibility in the team composition.

Autonomous Mobile Robots are used with increasing frequency in inspection and maintenance tasks completing fixed goal sequences. The downtime robots experience between goals offers an opportunity to gather additional environment information instead of resting. Uncertainty in the amount of downtime available rules out the definition of a pre-determined schedule set by an external operator. Instead, the robot itself should decide dynamically, what information it should gather before its next task begins. This results in a multi-objective optimization problem trying to maximize information gain while utilizing as much of the available time as possible. We propose a genetic algorithm to solve the presented optimization problem and introduce two different models for artificial curiosity used inside the fitness function for gathering as much information as possible. For planning the genetic algorithm utilizes a multi-map approach using information and obstacle maps. We evaluated our models in a pre-defined and pre-mapped Gazebo environment with a given information map and evaluated their performance against an information-agnostic coverage algorithm. In this work, we show that utilizing artificial curiosity in path planning can result in major information gains by effectively using downtime.