Symbiotic System Design for Safe and Resilient Autonomous Robotics in
Offshore Wind Farms
- URL: http://arxiv.org/abs/2101.09491v1
- Date: Sat, 23 Jan 2021 11:58:16 GMT
- Title: Symbiotic System Design for Safe and Resilient Autonomous Robotics in
Offshore Wind Farms
- Authors: Daniel Mitchell, Osama Zaki, Jamie Blanche, Joshua Roe, Leo Kong,
Samuel Harper, Valentin Robu, Theodore Lim, David Flynn
- Abstract summary: Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational safety compliance and resilience.
We propose a symbiotic system; reflecting the lifecycle learning and co-evolution with knowledge sharing for mutual gain of robotic platforms and remote human operators.
Our methodology enables the run-time verification of safety, reliability and resilience during autonomous missions.
- Score: 3.5409202655473724
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: To reduce Operation and Maintenance (O&M) costs on offshore wind farms,
wherein 80% of the O&M cost relates to deploying personnel, the offshore wind
sector looks to robotics and Artificial Intelligence (AI) for solutions.
Barriers to Beyond Visual Line of Sight (BVLOS) robotics include operational
safety compliance and resilience, inhibiting the commercialization of
autonomous services offshore. To address safety and resilience challenges we
propose a symbiotic system; reflecting the lifecycle learning and co-evolution
with knowledge sharing for mutual gain of robotic platforms and remote human
operators. Our methodology enables the run-time verification of safety,
reliability and resilience during autonomous missions. We synchronize digital
models of the robot, environment and infrastructure and integrate front-end
analytics and bidirectional communication for autonomous adaptive mission
planning and situation reporting to a remote operator. A reliability ontology
for the deployed robot, based on our holistic hierarchical-relational model,
supports computationally efficient platform data analysis. We analyze the
mission status and diagnostics of critical sub-systems within the robot to
provide automatic updates to our run-time reliability ontology, enabling faults
to be translated into failure modes for decision making during the mission. We
demonstrate an asset inspection mission within a confined space and employ
millimeter-wave sensing to enhance situational awareness to detect the presence
of obscured personnel to mitigate risk. Our results demonstrate a symbiotic
system provides an enhanced resilience capability to BVLOS missions. A
symbiotic system addresses the operational challenges and reprioritization of
autonomous mission objectives. This advances the technology required to achieve
fully trustworthy autonomous systems.
Related papers
- SafeEmbodAI: a Safety Framework for Mobile Robots in Embodied AI Systems [5.055705635181593]
Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced.
Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections.
We propose textitSafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems.
arXiv Detail & Related papers (2024-09-03T05:56:50Z) - Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models [81.55156507635286]
Legged robots are physically capable of navigating a diverse variety of environments and overcoming a wide range of obstructions.
Current learning methods often struggle with generalization to the long tail of unexpected situations without heavy human supervision.
We propose a system, VLM-Predictive Control (VLM-PC), combining two key components that we find to be crucial for eliciting on-the-fly, adaptive behavior selection.
arXiv Detail & Related papers (2024-07-02T21:00:30Z) - Highlighting the Safety Concerns of Deploying LLMs/VLMs in Robotics [54.57914943017522]
We highlight the critical issues of robustness and safety associated with integrating large language models (LLMs) and vision-language models (VLMs) into robotics applications.
arXiv Detail & Related papers (2024-02-15T22:01:45Z) - Bridging Active Exploration and Uncertainty-Aware Deployment Using
Probabilistic Ensemble Neural Network Dynamics [11.946807588018595]
This paper presents a unified model-based reinforcement learning framework that bridges active exploration and uncertainty-aware deployment.
The two opposing tasks of exploration and deployment are optimized through state-of-the-art sampling-based MPC.
We conduct experiments on both autonomous vehicles and wheeled robots, showing promising results for both exploration and deployment.
arXiv Detail & Related papers (2023-05-20T17:20:12Z) - Assurance for Autonomy -- JPL's past research, lessons learned, and
future directions [56.32768279109502]
Autonomy is required when a wide variation in circumstances precludes responses being pre-planned.
Mission assurance is a key contributor to providing confidence, yet assurance practices honed over decades of spaceflight have relatively little experience with autonomy.
Researchers in JPL's software assurance group have been involved in the development of techniques specific to the assurance of autonomy.
arXiv Detail & Related papers (2023-05-16T18:24:12Z) - Bayesian Learning for the Robust Verification of Autonomous Robots [7.654864965575541]
We present a Bayesian learning framework that enables runtime verification of autonomous robots.
We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair.
arXiv Detail & Related papers (2023-03-15T09:29:27Z) - AI Maintenance: A Robustness Perspective [91.28724422822003]
We introduce highlighted robustness challenges in the AI lifecycle and motivate AI maintenance by making analogies to car maintenance.
We propose an AI model inspection framework to detect and mitigate robustness risks.
Our proposal for AI maintenance facilitates robustness assessment, status tracking, risk scanning, model hardening, and regulation throughout the AI lifecycle.
arXiv Detail & Related papers (2023-01-08T15:02:38Z) - Autonomous Aerial Robot for High-Speed Search and Intercept Applications [86.72321289033562]
A fully-autonomous aerial robot for high-speed object grasping has been proposed.
As an additional sub-task, our system is able to autonomously pierce balloons located in poles close to the surface.
Our approach has been validated in a challenging international competition and has shown outstanding results.
arXiv Detail & Related papers (2021-12-10T11:49:51Z) - SABER: Data-Driven Motion Planner for Autonomously Navigating
Heterogeneous Robots [112.2491765424719]
We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal.
We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints.
recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution.
A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
arXiv Detail & Related papers (2021-08-03T02:56:21Z) - Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems [16.609594839630883]
Computer vision approaches are widely used by autonomous robotic systems to guide their decision making.
High accuracy is critical, particularly for Human-on-the-loop (HoTL) systems where humans play only a supervisory role.
We propose a solution based upon adaptive autonomy levels, whereby the system detects loss of reliability of these models.
arXiv Detail & Related papers (2021-03-28T05:43:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.