Optimisation-based verification process of obstacle avoidance systems for unmanned vehicles

This thesis deals with safety verification analysis of collision avoidance systems for unmanned vehicles. The safety of the vehicle is dependent on collision avoidance algorithms and associated control laws, and it must be proven that the collision avoidance algorithms and controllers are functioning correctly in all nominal conditions, various failure conditions and in the presence of possible variations in the vehicle and operational environment. The current widely used exhaustive search based approaches are not suitable for safety analysis of autonomous vehicles due to the large number of possible variations and the complexity of algorithms and the systems. To address this topic, a new optimisation-based verification method is developed to verify the safety of collision avoidance systems. The proposed verification method formulates the worst case analysis problem arising the verification of collision avoidance systems into an optimisation problem and employs optimisation algorithms to automatically search the worst cases. Minimum distance to the obstacle during the collision avoidance manoeuvre is defined as the objective function of the optimisation problem, and realistic simulation consisting of the detailed vehicle dynamics, the operational environment, the collision avoidance algorithm and low level control laws is embedded in the optimisation process. This enables the verification process to take into account the parameters variations in the vehicle, the change of the environment, the uncertainties in sensors, and in particular the mismatching between model used for developing the collision avoidance algorithms and the real vehicle. It is shown that the resultant simulation based optimisation problem is non-convex and there might be many local optima. To illustrate and investigate the proposed optimisation based verification process, the potential field method and decision making collision avoidance method are chosen as an obstacle avoidance candidate technique for verification study. Five benchmark case studies are investigated in this thesis: static obstacle avoidance system of a simple unicycle robot, moving obstacle avoidance system for a Pioneer 3DX robot, and a 6 Degrees of Freedom fixed wing Unmanned Aerial Vehicle with static and moving collision avoidance algorithms. It is proven that although a local optimisation method for nonlinear optimisation is quite efficient, it is not able to find the most dangerous situation. Results in this thesis show that, among all the global optimisation methods that have been investigated, the DIviding RECTangle method provides most promising performance for verification of collision avoidance functions in terms of guaranteed capability in searching worst scenarios

Flexible adaptation of iterative learning control with applications to synthetic bone graft manufacturing

Additive manufacturing processes are powerful tools; they are capable of fabricating structures without expensive structure specific tooling -- therefore structure designs can efficiently change from run-to-run -- and they can integrate multiple distinct materials into a single structure. This work investigates one such additive manufacturing process, micro-Robotic Deposition ($\mu$RD), and its utility in fabricating advanced architecture synthetic bone grafts. These bone grafts, also known as synthetic bone scaffolds, are highly porous three-dimensional structures that provide a matrix to support the natural process of bone remodeling. Ideally, the synthetic scaffold will stimulate complete bone healing in a skeletal defect site and also resorb with time so that only natural tissue remains. The objective of this research is to develop methods to integrate different regions with different porous microstructures into a single scaffold; there is evidence that scaffolds with designed regions of specific microstructures can be used to elicit a strong and directed bone ingrowth response that improves bone ingrowth rate and quality. The key contribution of this work is the development of a control algorithm that precisely places different build materials in specified locations, thereby the fabrication of advanced architecture scaffolds is feasible. Under previous control methods, designs were relegated to be composed of a single material. The control algorithm developed in this work is an adaptation of Iterative Learning Control (ILC), a control method that is typically best suited for mass manufacturing applications. This adaptation reorients the ILC framework such that it is more amenable to additive manufacturing systems, such as $\mu$RD. Control efficacy is demonstrated by the fabrication of advanced architecture scaffolds. Scaffolds with contoured forms, multiple domains with distinct porous microstructures, and hollow cavities are feasible when the developed controller is used in conjunction with a novel manufacturing workflow in which scaffolds are filled within patterned molds that support overhanging features. An additional application demonstrates controller performance on the robot positioning problem; this work has implications for additive manufacturing in general

Failure diagnosis and prognosis in stochastic discrete-event and cyber-physical systems

In this dissertation we study the problem of fault diagnosis in both discrete event systems and cyber physical systems. Discrete event systems (DESs) are event-driven systems with discrete states that evolve in response to abrupt occurrences of discrete changes (called events). The stochastic DESs are used to characterize the quantitative behavior of the system, by modeling the uncertainty on the occurrence of events as random variables with certain distribution. A stochastic DES is similar to the Markov chain models, with the difference being that, in stochastic DESs, the transition is labeled with the event while the event information is omitted in a Markov chain. Many physical systems, such as manufacturing systems, communication protocols, reactive software, telephone networks, traffic systems, robotics and digital hardware, can be modeled as DESs at a certain level of abstraction. Fault diagnosis is to detect the occurrence of a fault so as to enable any fault tolerant actions. It is a crucial and challenging problem that has attracted considerable attentions in the literature of software engineering, automotive systems, power systems and nuclear engineering. In this dissertation, we propose the online detection schemes for stochastic DESs and also introduce the notions of missed detections (MDs) and false alarms (FAs), or equivalently, false-negatives and false-positives, for the schemes. The idea is that given any observation (of partially observed events), the detector recursively computes the conditional probability of the nonoccurrence of a fault and issues a fault decision if the probability of the nonoccurrence of a fault falls below an appropriately chosen threshold, and issues no-decision otherwise. We establish that S-Diagnosability is a necessary and sufficient condition for achieving any desired levels of MD and FA rates, where the notion of S-Diagnosability was proposed by Thorsley, et al. in 2005, requiring that given any tolerable ambiguity level &rho and error bound &tau , there must exist a delay bound n such that for any fault trace, its extensions, longer than n and probability of ambiguity higher than &rho, occur with probability smaller than &tau . Algorithms for determining the detection scheme parameters of detection threshold and detection delay bound for the specified MD and FA rates requirement are also presented, based on the construction of an extended observer, which computes, for each observation sequence, the set of states reached in the system model, along with their probabilities and the number of post-fault transitions executed. This dissertation also studies the fault diagnosis in cyber physical systems, where the dynamics of the physical systems over discrete sample instances are described by stochastic difference equations, and the nonfault behaviors are specified by linear-time temporal logic (LTL) formulas over sequences of requirement variables that are functions of inputs and states (just as the outputs). We first introduce the notion of an input-output stochastic hybrid automaton (I/O-SHA), and then show that it can be used to model the refinement of a given discrete-time stochastic system against its LTL specification so as to identify the system behaviors that satisfy the nonfault specification versus the ones that violate it in form of reachability of a fault location. For this we propose a refinement algorithm that refines the system model in form of discrete-time stochastic equations with respect to its specification model in form of a Buchi acceptor, and the resulting refinement can be modeled as an I/O-SHA. We further show that the fault detection problem then reduces to a state estimation problem for the I/O-SHA. The performance of the detection protocol is evaluated in terms of its FA and MD rates. We additionally propose the notion of S-Diagnosability for I/O-SHA, which can guarantee the existence of detectors that can achieve any desired FA and MD rates. We further consider the fault prognosis problem, where the goal is to predict a fault prior to its occurrence, for stochastic DESs. We introduce m-steps Stochastic-Prognosability, or simply Sm-Prognosability, requiring for any tolerance level &rho and error bound &tau , there exists a reaction bound k &ge m, such that the set of fault traces for which a fault cannot be predicted k steps in advance with tolerance level &rho, occurs with probability smaller than &tau . Similar to the fault diagnosis problem, we formalize the notion of a prognoser that maps observations to decisions by comparing a suitable statistic with a threshold, and show that Sm-Prognosability is a necessary and sufficient condition for the existence of a prognoser with reaction bound at least m (i.e., prediction at least m-steps prior to the occurrence of a fault) that can achieve any specified FA and MD rate requirement. Moreover, we provide a polynomial algorithm for verifying Sm-Prognosability

Probabilistic Testing for Stochastic Hybrid Systems

In this paper we propose a testing based method for safety/reachability analysis of stochastic hybrid systems. Testing based methods are characterized by analysis based on the execution traces of the system or the simulation thereof. Testing based method is very appealing because of the simplicity of its execution, the possibility of having a partial verification, and its highly parallel structure. The key idea in this paper is the construction of a robust neighborhood consisting of states that have the same probabilistic safety/reachability properties. We construct the robust neighborhood using the level sets of a stochastic bisimulation function. We also show how to construct stochastic bisimulation functions for systems whose continuous dynamics is stable and linear. As a case example, we consider the problem of conflict detection of aircraft flight, and show that we can infer some robust probabilistic safety property by using the algorithm that we present in this paper