Towards explainability in robotics: A performance analysis of a cloud accountability system
This research study examines the performance impact of a custom log engine and NoSQL database approach—both locally and in cloud environments—to support a log-based accountability and explainability system for autonomous robots. The authors propose a three-layer accountability architecture mapping software components, events, and robot behaviors to ensure traceability without breaking system performance. Through seven assessment scenarios using ROS and a simulated environment, the study evaluates CPU, network, disk, and message metrics to determine the most effective logging and storage strategies.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and developers in robotics who want to understand the performance impacts of implementing logging and accountability frameworks for autonomous agents.
What the document contains
This document addresses the design and performance evaluation of a log-based accountability system for autonomous robots using ROS. It covers a three-layer model integrating logging, events, and behaviors, and analyzes system metrics across local file storage, local NoSQL databases, and cloud-based database solutions.
The text does not provide binding legal regulations, commercial compliance certifications, or formal regulatory mandates for robot manufacturing.
- Achieving perfect mapping between robot actions and logging data is essential for transparency and explainability in autonomous robotics.
- Verbose logging engines incur heavy computational overhead and negatively impact robot system performance.
- Using a NoSQL database like MongoDB for log storage offers extended flexibility and normalization compared to traditional flat file storage.
- Cloud storage alternatives and multi-threading can optimize log management but require careful handling to avoid message lags and dropped packets.
- Systematic trade-offs between storage strategy, network usage, and processing overhead must be balanced depending on hardware constraints.
· 1 INTRODUCTION · 2 MATERIALS AND METHODS - 2.1 Accountability architecture - 2.2 Experimentation · 3 RESULTS - 3.1 Log benchmarking - 3.2 System benchmarking - 3.3 Rosout component benchmarking · 4 DISCUSSION · 5 CONCLUSIONS
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-24
Privacy
1/5Protection of personal data and private life
Finally, there is a legal perspective facing the problems with logs such as privacy (Miller, 2017) or cybersecurity to log files
1 INTRODUCTION
screening score · 2026-10-24
Transparency
4/5Openness about how AI systems work and are used
Understanding why a robot's behaviour was triggered is a growing concern to get human-acceptable social robots. Every action, expected and unexpected, should be able to be explained and audited.
Abstract
screening score · 2026-10-24
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-24