FairFly: A Fair Motion Planner for Fleets of Autonomous UAVs in Urban Airspace

We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses the fair planning question for general autonomous systems, including UAV fleets, subject to complex missions typical of urban applications. FairFly formalizes each mission in temporal logic. An offline search finds the fairest paths that satisfy the missions and can be flown by the UAVs, leading to lighter online control load. It allows explicit negotiation between UAVs to enable imbalanced path durations if desired. We present three fairness notions, including one that reduces energy consumption. We validate our results in simulation, and demonstrate a lighter computational load and less UAV energy consumption as a result of flying fair trajectories.Comment: 6 pages, conference, itsc, iee

Development of inhalable microparticles for drug delivery to deep lung tissues

Lung cancer is the deadliest solid tumor, leading to the deaths of more individuals than the combination of the three next most lethal cancers which are colon, prostate and breast cancer. According to the IARC, in 2012 lung cancer accounted for 13% (1.83 million) of cancer cases and caused 19% (1.56 million) of cancer deaths worldwide. Despite advances in surgery and drug discovery, lung cancer remains difficult to treat. This is a result of unavoidable exposure to carcinogens, poor diagnosis and the lack of targeted drug delivery platforms. The aim of this study was to develop a non-invasive, patient convenient platform for the targeted delivery of chemotherapeutic drugs to cancer in deeper lung tissue. The formulation consisted of inhalable maltodextrin (MD)-based microparticles (MPs) encapsulating chitosan (CS) nanoparticles (NPs) loaded with magnetic nanoparticles (MNPs) and a chemotherapeutic drug. Ionotropic gelation was used for CS NPs synthesis. MNPs were synthesized via hydrothermal method and they were superparamagnetic with magnetic saturation (Ms), coercivity (Hc) and remanence (Mr) of 48.4 Am2/Kg, 9.9x10-4 T and 0.5 Am2/Kg emu/g; respectively. CS NPs provided a sustained release of drug, whereas MNPs encapsulated in CS NPs were able to increase the NP drug release in response to an external magnetic field by 1.7 fold. Cell uptake studies conducted using lung cancer cells (A549) indicated that the CS NPs are rapidly uptaken, and show preferential toxicity to tumor cells in comparison to cultured fibroblasts. NPs were modified with anti-epidermal growth factor receptor antibodies and this modification showed to hinder cellular uptake of NPs. Afterwards, the prepared CS NPs and CS-MNPs were co-spray freeze dried (SFD) with MD. The prepared SFD powders had fine particle fraction (FPF ≤ 5.2 μm) of 40-42 % w/w and mass median aerodynamic diameter (MMAD) of 5-6 μm as determined by the next generation impactor (NGI). A mixture of CS NPs and CS-MNPs could be able to provide a continuous sustained release of drug, with intermittent blouses of drug in response to external stimuli; a drug profile desirable in cancer therapy. In conclusion, the targeted delivery to the lung cancer using the developed formulation seems to be a promising approach

Benchmark: Nonlinear Hybrid Automata Model of Excitable Cardiac Tissue

Implantable cardiac devices like pacemakers and defibrillators are life-saving medical devices. To verify their functionality, there is a need for heart models that can simulate interesting phenomena and are relatively computationally tractable. In this benchmark we implement a model of the electrical activity in excitable cardiac tissue as a network of nonlinear hybrid automata. The model has previously been shown to simulate fast arrhythmias. The hybrid automata are arranged in a square n-by-n grid and communicate via their voltages. Our Matlab implementation allows the user to specify any size of model $n$, thus rendering it ideal for benchmarking purposes since we can study tool efficiency as a function of size. We expect the model to be used to analyze parameter ranges and network connectivity that lead to dangerous heart conditions. It can also be connected to device models for device verification

Relaxed decidability and the robust semantics of Metric Temporal Logic

Relaxed notions of decidability widen the scope of automatic verification of hybrid systems. In quasi-decidability and $\delta$-decidability, the fundamental compromise is that if we are willing to accept a slight error in the algorithm\u27s answer, or a slight restriction on the class of problems we verify, then it is possible to obtain practically useful answers. This paper explores the connections between relaxed decidability and the robust semantics of Metric Temporal Logic formulas. It establishes a formal equivalence between the robustness degree of MTL specifications, and the imprecision parameter $\delta$ used in $\delta$-decidability when it is used to verify MTL properties. We present an application of this result in the form of an algorithm that generates new constraints to the $\delta$-decision procedure from falsification runs, which speeds up the verification run. We then establish new conditions under which robust testing, based on the robust semantics of MTL, is in fact a quasi-semidecision procedure. These results allow us to delimit what is possible with fast, robustness-based methods, accelerate (near-)exhaustive verification, and further bridge the gap between verification and simulation

Conformance Testing as Falsification for Cyber-Physical Systems

In Model-Based Design of Cyber-Physical Systems (CPS), it is often desirable to develop several models of varying fidelity. Models of different fidelity levels can enable mathematical analysis of the model, control synthesis, faster simulation etc. Furthermore, when (automatically or manually) transitioning from a model to its implementation on an actual computational platform, then again two different versions of the same system are being developed. In all previous cases, it is necessary to define a rigorous notion of conformance between different models and between models and their implementations. This paper argues that conformance should be a measure of distance between systems. Albeit a range of theoretical distance notions exists, a way to compute such distances for industrial size systems and models has not been proposed yet. This paper addresses exactly this problem. A universal notion of conformance as closeness between systems is rigorously defined, and evidence is presented that this implies a number of other application-dependent conformance notions. An algorithm for detecting that two systems are not conformant is then proposed, which uses existing proven tools. A method is also proposed to measure the degree of conformance between two systems. The results are demonstrated on a range of models

Technical Report: Control Using the Smooth Robustness of Temporal Logic

Cyber-Physical Systems must withstand a wide range of errors, from bugs in their software to attacks on their physical sensors. Given a formal specification of their desired behavior in Metric Temporal Logic (MTL), the robust semantics of the specification provides a notion of system robustness that can be calculated directly on the output behavior of the system, without explicit reference to the various sources or models of the errors. The robustness of the MTL specification has been used both to verify the system offline (via robustness minimization) and to control the system online (to maximize its robustness over some horizon). Unfortunately, the robustness objective function is difficult to work with: it is recursively defined, non-convex and non-differentiable. In this paper, we propose smooth approximations of the robustness. Such approximations are differentiable, thus enabling us to use powerful off-the- shelf gradient descent algorithms for optimizing it. By using them we can also offer guarantees on the performance of the optimization in terms of convergence to minima. We show that the approximation error is bounded to any desired level, and that the approximation can be tuned to the specification. We demonstrate the use of the smooth robustness to control two quad-rotors in an autonomous air traffic control scenario, and for temperature control of a building for comfort

Smooth Operator: Control using the Smooth Robustness of Temporal Logic

Modern control systems, like controllers for swarms of quadrotors, must satisfy complex control objectives while withstanding a wide range of disturbances, from bugs in their software to attacks on their sensors and changes in their environments. These requirements go beyond stability and tracking, and involve temporal and sequencing constraints on system response to various events. This work formalizes the requirements as formulas in Metric Temporal Logic (MTL), and designs a controller that maximizes the robustness of the MTL formula. Formally, if the system satisfies the formula with robustness r, then any disturbance of size less than r cannot cause it to violate the formula. Because robustness is not differentiable, this work provides arbitrarily precise, infinitely differentiable, approximations of it, thus enabling the use of powerful gradient descent optimizers. Experiments on a temperature control example and a two-quadrotor system demonstrate that this approach to controller design outper- forms existing approaches to robustness maximization based on Mixed Integer Linear Programming and stochastic heuristics. Moreover, it is not constrained to linear systems