Dejanira Araiza-Illan

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This paper presents the deductive formal verification of high-level properties of control systems with theorem proving, using the Why3 tool. Properties that can be verified with this approach include stability, feedback gain, and robustness, among others. For the systems, modelled in Simulink, we propose three main steps to achieve the verification:(More)
Collaborative robots could transform several industries, such as manufacturing and healthcare, but they present a significant challenge to verification. The complex nature of their working environment necessitates testing in realistic detail under a broad range of circumstances. We propose the use of Coverage-Driven Verification (CDV) to meet this(More)
Industries such as flexible manufacturing and home care will be transformed by the presence of robotic assistants. Assurance of safety and functional soundness for these robotic systems will require rigorous verification and validation. We propose testing in simulation using Coverage-Driven Verification (CDV) to guide the testing process in an automatic and(More)
The challenges of robotic software testing extend beyond conventional software testing. Valid, realistic and interesting tests need to be generated for multiple programs and hardware running concurrently, deployed into dynamic environments with people. We investigate the use of Belief-Desire-Intention (BDI) agents as models for test generation, in the(More)
The widespread adoption of autonomous adaptive systems depends on provided guarantees of safety and functional correctness, at both design time and runtime. Specifying adaptive systems is cognitively difficult when their aspects are in a large number and have complicated dependencies. We present a technique to construct and automatically explore a(More)
This paper presents the verification of control systems implemented in Simulink. The goal is to ensure that high-level requirements on control performance, like stability, are satisfied by the Simulink diagram. A two stage process is proposed. First, the high-level requirements are decomposed into specific parametrized sub-requirements and implemented as(More)
The emergent global behaviours of robotic swarms are important for them to achieve their navigation task goals. These emergent behaviours can be verified to assess their correctness, through techniques like model checking. Model checking exhaustively explores all possible behaviours, based on a discrete model of the system, such as a swarm in a grid. A(More)
—A novel biologically inspired controller for the autonomous navigation of a mobile robot in an evasion task is proposed. The controller takes advantage of the environment by calculating a measure of danger and subsequently choosing the parameters of a reinforcement learning based decision process. Two different reinforcement learning algorithms were used:(More)
Robotic code needs to be verified to ensure its safety and functional correctness, especially when the robot is interacting with people. Testing the real code in simulation is a viable option. It reduces the costs of experiments and provides detail that is lost when using formal methods. However, generating tests that cover interesting scenarios, while(More)
Self-adaptive systems change their operational behaviour for instance to accommodate variations in their environment, while preserving functional requirements and maintaining acceptable conformance to non-functional requirements (NFRs). While conformance with functional requirements is clear-cut, it is more challenging to specify acceptable behaviours when(More)