Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming

Zhao, Weiye; He, Shu; Liu, Changliu

doi:10.1109/lcsys.2022.3181123

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…We note that this approach is not guaranteed to be safe with respect to the multimodal system, since this unimodal approach essentially takes k i = 3 for one mode and k i = 0 for all other modes. In a simple bimodal system where P (θ 1 ) = P (θ 2 ) = 0.5, it's easy to see that simply setting k 1 = 3, k 2 = 0 will not satisfy (8). Intuitively, we can see in Fig.…”

Section: Comparison With Unimodal Safe Control (Sea)mentioning

confidence: 99%

“…The general problem of designing ϕ(x) for an arbitrary system is challenging, but is not the focus of this work. There is a breadth of existing literature on writing or synthesizing safety indices [3], [7], [8]. Thus we assume that we are given a safety index ϕ for our system that we must design a safe control law around.…”

Section: A Energy-function-based Safe Controlmentioning

confidence: 99%

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Safe and Efficient Exploration of Human Models During Human-Robot Interaction

Pandya

Liu

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Generating safe behaviors for autonomous systems is important as they continue to be deployed in the real world, especially around people. In this work, we focus on developing a novel safe controller for systems where there are multiple sources of uncertainty. We formulate a novel multimodal safe control method, called the Multimodal Safe Set Algorithm (MMSSA) for the case where the agent has uncertainty over which discrete mode the system is in, and each mode itself contains additional uncertainty. To our knowledge, this is the first energy-function-based safe control method applied to systems with multimodal uncertainty. We apply our controller to a simulated human-robot interaction where the robot is uncertain of the human's true intention and each potential intention has its own additional uncertainty associated with it, since the human is not a perfectly rational actor. We compare our proposed safe controller to existing safe control methods and find that it does not impede the system performance (i.e. efficiency) while also improving the safety of the system.

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Section: Comparison With Unimodal Safe Control (Sea)mentioning

confidence: 99%

Section: A Energy-function-based Safe Controlmentioning

confidence: 99%

Safe and Efficient Exploration of Human Models During Human-Robot Interaction

Pandya

Liu

2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…During initialization, an initial population P(0), which may comprise random individuals, is generated and evaluated, and crossover ( p c ) and mutation ( p m ) probabilities are set [lines 1 -2]. The evolutionary cycle [lines [4][5][6][7][8] is bound to a termination condition, e.g., a fixed number of generations. Therein, the offspring population P O is selected from the current population.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

“…We log the rotations of all angle sensors during every run using the time-series database InfluxDB. 4 We implement two use cases with different scenarios and complexity for a production unit.…”

Section: B Study Setupmentioning

confidence: 99%

“…A particular challenge for effectively performing these activities, especially for successfully identifying runtime states, is the quantification of realization precision and measurement uncertainties [3]. Currently, the main focus of uncertainty quantification is on 3D-CAD models representing physical parameters during the design phase of systems [4], [5], [6]. In contrast, models that depict the logical structure and behavior of the system in operation are often not virtually mapped and analyzed [7].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Optimization of Tolerance Ranges for Model-Driven Runtime State Identification

Sint,

Mazak-Huemer,

Eisenberg

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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For continuously checking and updating the virtual representation of a real system during operation, the continuous sensing and interpretation of raw sensor data is a must. The challenge is to bundle sensor value streams (e.g., from IoT networks) and aggregate them to a higher logical state level to enable process-oriented viewpoints and to handle uncertainties about sensor measurements and state realization precision. To address these uncertainties, so-called "tolerance ranges" must be defined in which logical states are detected during operation with acceptable deviations. Specifying such tolerance ranges manually is a time-consuming, error-prone task and often not feasible due to the huge associated value search space. To tackle this challenge, the problem is turned into an optimization problem in this paper. For this purpose, we present a framework based on meta-heuristic search that enables the automatic configuration of tolerance ranges based on available execution traces of multiple sensor value streams. An exploratory study evaluates the approach. For this purpose, we implemented a lab-sized demonstrator of a five-axis grip arm robot, which we continuously monitored during operation in a simulated environment. The evaluation shows the advantage of using meta-heuristic optimizers such as Harmony Search or Genetic Algorithm to identify stable tolerance ranges automatically for state detection at runtime. Note to Practitioners-Monitoring sensor values streams is nowadays a frequently employed technique in many automation domains. However, combining and mapping single value streams to higher-level state-based representations such as state machines or other design-time related models is a major challenge due to measurement and realization precision uncertainties. Thus, simply mapping monitored raw data to these design descriptions can lead to falsely identified or missed states. To improve this situation, we present an approach that provides a mechanism to continuously analyze data streams during operation by automatically finding appropriate tolerance ranges to detect realized system states. The approach uses a small set of annotated execution traces and meta-heuristic searchers to derive optimal tolerance ranges, which provide high correctness and completeness of the identified system states. This approach represents the basis for building a "vertical bridge" from the operation technology layer considering pure sensor data streams to the IT

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