Safety vs. Efficiency: AI-Based Risk Mitigation in Collaborative Robotics

Terra, Ahmad; Riaz, Hassam; Raizer, Klaus; Hata, Alberto Yukinobu; Inam, Rafia

doi:10.1109/iccar49639.2020.9108037

Cited by 16 publications

(4 citation statements)

References 12 publications

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“…As a start, we consider the safety system as a five-step risk management process based on [16,33] and modified for handling run-time applications using different risk policies, as shown in Figure 2. This figure summarizes the approach followed in the present research, highlighting the importance and advantages of mapping the potential risks into the design step.…”

Section: Risk Managementmentioning

confidence: 99%

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

2023

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Safe navigation is a key objective for autonomous applications, particularly those involving mobile tasks, to avoid dangerous situations and prevent harm to humans. However, the integration of a risk management process is not yet mandatory in robotics development. Ensuring safety using mobile robots is critical for many real-world applications, especially those in which contact with the robot could result in fatal consequences, such as agricultural environments where a mobile device with an industrial cutter is used for grass-mowing. In this paper, we propose an explicit integration of a risk management process into the design of the software for an autonomous grass mower, with the aim of enhancing safety. Our approach is tested and validated in simulated scenarios that assess the effectiveness of different custom safety functionalities in terms of collision prevention, execution time, and the number of required human interventions.

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Section: Risk Managementmentioning

confidence: 99%

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

2023

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“…In fact, the current work is now focusing on deploying AI into ensuring a highly secure robotic environment with the high accuracy and less overhead. Terra et al presented the implementation of Fuzzy Logic System (FLS) and Reinforcement Learning (RL) to build risk mitigation modules for human-robot collaboration scenarios [324]. The testing results revealed that the presented risk mitigation strategies improve the safety aspect and the efficiency by 26% from the default setup.…”

Section: Artificial Intelligence-based Solutionsmentioning

confidence: 99%

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Yaacoub

Noura

Salman

et al. 2021

Int. J. Inf. Secur.

145

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The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

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“…Fig. 6: The risk profile R Im ε,C (blue circle), assuming failures occur at pairs (23,24,25,26,27). The red dash represents the basic risk of collision R C,j ε as in [19].…”

Section: Case Studiesmentioning

confidence: 99%

“…The risk analysis in robotic applications can be traced from when [11] built the algorithmic risk measure for surgical robots to when [24] showed how the involvement of riskmitigation in robot operations could increase safety. A more systemic and analytical approach that adopts the widely used financial tool value-at-risk and the conditional-value-at-risk measure [16] has been proposed by both [13] and [22].…”

Section: Introductionmentioning

confidence: 99%

Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles

Somarakis¹,

Motee²

2021

Preprint

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We develop a framework to assess the risk of cascading failures (as collisions) in a platoon of vehicles in the presence of exogenous noise and communication time-delay. The notion of value-at-risk (VaR) is adopted to quantify the risk of collision between vehicles conditioned to the knowledge of multiple previously occurred failures in the platoon. We show that the risk of cascading failures depends on the Laplacian spectrum of the underlying communication graph, time-delay, and noise statistics. Furthermore, we exploit the structure of several standard graphs to show how the risk profile depends on the spatial location of the systemic events. Our theoretical findings are significant as they can be applied to design safe platoons. Finally, our results are validated with extensive simulations.

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Safety vs. Efficiency: AI-Based Risk Mitigation in Collaborative Robotics

Cited by 16 publications

References 12 publications

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

Towards Safe Robotic Agricultural Applications: Safe Navigation System Design for a Robotic Grass-Mowing Application through the Risk Management Method

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations

Emergence of Cascading Risk and Role of Spatial Locations of Collisions in Time-Delayed Platoon of Vehicles

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