Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

Safety and Reliability for Autonomous Robots in Dynamic Environments

Rizwan, Momina LU orcid (2025)
Abstract
Autonomous robots must operate reliably and safely under uncertain, dynamic conditions over extended periods. To ensure such operational robustness, it is vital that both developers and operators can clearly and verifiably specify functional requirements and safety constraints throughout the robot software lifecycle. My research targets different layers of robot operational safety: early error detection, real-time safety enforcement, and adaptive failure recovery. First, we extend the DeROS language to develop ROSSMARie, a DSL to generate a runtime safety monitor for enforcing safety rules and enabling autonomous recovery. ROSSMARie ensures functional safety through real-time rule monitoring and resume-capable interventions, validated on... (More)
Autonomous robots must operate reliably and safely under uncertain, dynamic conditions over extended periods. To ensure such operational robustness, it is vital that both developers and operators can clearly and verifiably specify functional requirements and safety constraints throughout the robot software lifecycle. My research targets different layers of robot operational safety: early error detection, real-time safety enforcement, and adaptive failure recovery. First, we extend the DeROS language to develop ROSSMARie, a DSL to generate a runtime safety monitor for enforcing safety rules and enabling autonomous recovery. ROSSMARie ensures functional safety through real-time rule monitoring and resume-capable interventions, validated on an industrial robot control platform in scenarios involving human proximity, terrain hazards, and contact instability.
Second, we present EzSkiROS: an embedded DSL framework in Python that supports early fault detection during the pre-deployment (launch) phase of robotic skills. This DSL checks the consistency between Behavior Tree (BT) implementations, high-level symbolic skill contracts, and ontology-based world models. By performing symbolic and dynamic checks before execution, this approach identifies latent faults that would otherwise manifest at runtime.
Third, we introduce a safety monitoring architecture Reflex-Plan, which enables communication between the runtime safety monitor and the deliberate high-level planner. This dual-layer design enables ``fast thinking" for immediate hazard response and ``slow thinking" for recovery planning.
Reflex-Plan is validated in a mock hospital environment using a mobile manipulator, demonstrating measurable improvements in task continuity, response latency, and hazard mitigation.
Together, these contributions form a safety pipeline that uses DSL-based robotic programming. Our results demonstrate significant improvements in operational safety and code maintainability, enabling autonomous robots to handle failures proactively and recover adaptively in complex, real-world settings. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Prof. Hochgeschwender, Nico, University of Bremen, Germany.
organization
publishing date
type
Thesis
publication status
published
subject
keywords
robot safety, domain-specific language, runtime monitor, error detection
pages
200 pages
publisher
Computer Science, Lund University
defense location
Lecture Hall E:B, building E, Klas Anshelms väg 10, Faculty of Engineering LTH, Lund University, Lund.
defense date
2025-10-09 13:15:00
ISBN
978-91-8104-687-8
978-91-8104-688-5
language
English
LU publication?
yes
id
a5203a03-3c8f-483b-a0f9-bbe89b912d39
date added to LUP
2025-09-16 09:17:03
date last changed
2025-09-18 09:22:12
@phdthesis{a5203a03-3c8f-483b-a0f9-bbe89b912d39,
  abstract     = {{Autonomous robots must operate reliably and safely under uncertain, dynamic conditions over extended periods. To ensure such operational robustness, it is vital that both developers and operators can clearly and verifiably specify functional requirements and safety constraints throughout the robot software lifecycle. My research targets different layers of robot operational safety: early error detection, real-time safety enforcement, and adaptive failure recovery. First, we extend the DeROS language to develop ROSSMARie, a DSL to generate a runtime safety monitor for enforcing safety rules and enabling autonomous recovery. ROSSMARie ensures functional safety through real-time rule monitoring and resume-capable interventions, validated on an industrial robot control platform in scenarios involving human proximity, terrain hazards, and contact instability.<br/>Second, we present EzSkiROS: an embedded DSL framework in Python that supports early fault detection during the pre-deployment (launch) phase of robotic skills. This DSL checks the consistency between Behavior Tree (BT) implementations, high-level symbolic skill contracts, and ontology-based world models. By performing symbolic and dynamic checks before execution, this approach identifies latent faults that would otherwise manifest at runtime.<br/>Third, we introduce a safety monitoring architecture Reflex-Plan, which enables communication between the runtime safety monitor and the deliberate high-level planner. This dual-layer design enables ``fast thinking" for immediate hazard response and ``slow thinking" for recovery planning. <br/>Reflex-Plan is validated in a mock hospital environment using a mobile manipulator, demonstrating measurable improvements in task continuity, response latency, and hazard mitigation.<br/>Together, these contributions form a safety pipeline that uses DSL-based robotic programming. Our results demonstrate significant improvements in operational safety and code maintainability, enabling autonomous robots to handle failures proactively and recover adaptively in complex, real-world settings.}},
  author       = {{Rizwan, Momina}},
  isbn         = {{978-91-8104-687-8}},
  keywords     = {{robot safety; domain-specific language; runtime monitor; error detection}},
  language     = {{eng}},
  month        = {{09}},
  publisher    = {{Computer Science, Lund University}},
  school       = {{Lund University}},
  title        = {{Safety and Reliability for Autonomous Robots in Dynamic Environments}},
  year         = {{2025}},
}