Modern algorithms allow robots to reach a greater level of autonomy and fulfill more challenging tasks. However, on-board limitations regarding computational and battery resources are hindering factors regarding the deployment of such algorithms particularly on mobile robots. Although offloading a majority of the algorithmic components to the edge or even cloud offers an attractive option to leverage massive computing power in robotics applications, safety and reliability remain critical issues. This paper presents a minimalistic safety fallback mechanism when offloading mobile robot navigation to the edge, that ensures safe and collision-free navigation even in the presence of failures in the connection between the on-board and edge-devices. We show the effectiveness of our approach through extensive testing in three different relevant scenarios in a simulated warehouse environment. Our experiments demonstrate the effects of different fallback strategies and show how our proposed approach is able to ensure safety while allowing the robot to continue its mission during an interrupted connection and thus avoiding unnecessary downtime.

Offloading software modules to the edge/cloud can enhance a robot’s capabilities by leveraging massive computing power. However, determining which software module should be offloaded and scheduled to which robot/edge/cloud node is a challenging task, particularly for robot fleets with diverse tasks. In this paper, we tackle the software scheduling problem and introduce a taxonomy to categorize software modules and classify their applicability and requirements for offloading. Additionally, by using prior measurements, we model the compute cluster and formalize software scheduling as a multi-objective optimization problem which we tackle with a genetic algorithm. To evaluate our approach with a challenging setup, we build a mobile manipulation task using open-source frameworks and libraries in the Robot Operating System (ROS2) community in simulation as well as a mildly simplified real-world variant. Our evaluation shows significant improvements compared to the built-in scheduler of Kubernetes (K8s) regarding robotic specific metrics such as the rate of missed cycle time in both simulated and real-world experiments.