Enterprise AI Analysis
Edge-Cloud Orchestration of Assertion-Based Monitors for Robotic Applications
By NICOLA BOMBIERI, SAMUELE GERMINIANI, FRANCESCO LUMPP, GRAZIANO PRAVADELLI
Published: March 2026
DOI: 10.1145/3723878
The runtime verification of multi-domain software applications implementing the behaviors of modern robots is a challenging task. On the one hand, assertion-based verification (ABV) has shown great potential to check the correctness of complex systems at runtime. On the other hand, the computational overhead introduced by runtime ABV can be substantial, variable and non-deterministic. As a consequence, applying accurate ABV at runtime to autonomous robots, which are often characterized by resource-constrained computing architectures, can lead to severe slowdowns of the software execution and failures of temporal constraints, thus compromising the overall system's correctness. We address this challenge by proposing a platform for runtime ABV that implements monitor synthesis from signal temporal logic assertions and dynamic monitor migration across edge devices and the cloud. The synthesized monitors are wrapped into ROS-compliant nodes and connected to the system under verification. The overall ABV framework and the related migration mechanism are then containerized with Docker for both edge and cloud computing. To evaluate the proposed platform, we present the results obtained with a set of synthetic benchmarks and with an industrial case study, which implements the mission of a Robotnik RB-Kairos mobile robot in a smart manufacturing production line.
Executive Impact: Key Takeaways for Business Leaders
This research introduces a critical advancement in robotic system reliability and operational efficiency. Here’s why it matters to your enterprise:
This article addresses the inherent difficulties of verifying complex robotic software in real-time, especially in resource-constrained environments. The proposed edge-cloud orchestration platform ensures robust and accurate runtime verification without compromising the robot's performance or violating critical operational constraints. This is achieved through dynamic monitor migration, allowing verification tasks to adapt to available computational resources, thereby preventing system overloads and maintaining continuous, reliable operation.
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Your Path to Intelligent Automation
Our proven methodology ensures a smooth transition and maximum impact for your enterprise.
Phase 1: Discovery & Strategy
We begin with an in-depth analysis of your current robotic applications, infrastructure, and verification challenges. This phase defines key performance indicators and outlines a tailored AI orchestration strategy aligned with your business objectives.
Phase 2: Monitor Synthesis & Integration
Leveraging Signal Temporal Logic (STL), we synthesize assertion-based monitors specific to your robotic behaviors. These monitors are then containerized and seamlessly integrated into your existing ROS-compliant nodes without requiring source code modifications.
Phase 3: Edge-Cloud Orchestration Deployment
Our platform deploys the dynamic orchestration framework across your edge and cloud infrastructure. This includes setting up monitor handlers, the global coordinator, and configuring the MILP solver for intelligent resource allocation and monitor migration.
Phase 4: Optimization & Scalability
Continuous monitoring and fine-tuning ensure optimal performance. The system dynamically adapts to workload changes, migrating monitors to prevent resource saturation and maintain verification accuracy, even as your robotic operations scale.
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