Semantic Local Planning for Mobile Robots through Path Optimization Services on the Edge: a Scenario-based Evaluation

Abstract

Autonomous mobile transport systems are a key enabler for flexible production organization. In order to enhance the software life cycle management and the overall function range, service-based offloading of software function towards cloud and edge is a valid alternative to monolithic onboard software architectures. We introduce an approach towards an edge-computing-based improvement of the classic autonomous navigation stack in terms of considering semantic policies using visual object detection and localization. Thus, context-aware navigation in regards to safety and efficiency can be implemented, e.g. to fulfill directions of standards and guidelines for production, hospital or public domains. Our semantic path planning is implemented as an addition or as a replacement of common local planning services following a microservice approach. The semantic optimization of the initial trajectory is successfully shown in regards to the following policies: keeping to the right side of the surrounding environment, avoiding to drive near to closed doors and passing humans on the right side. In addition, we reveal implementation of further policies by adapting the optimization policies and show within a scenario-based evaluation that the usage of edge computing in comparison to onboard computing yields performance gains.