The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

Time Window Temporal Logic

This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are typically seen in robotics and control applications. This paper also discusses the relaxation of TWTL formulae with respect to deadlines of tasks. Efficient automata-based frameworks to solve synthesis, verification and learning problems are also presented. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the specification. Case studies illustrating the expressivity of the logic and the proposed algorithms are included

Time window temporal logic

This paper introduces time window temporal logic (TWTL), a rich expressive language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms

LTL Control in Uncertain Environments with Probabilistic Satisfaction Guarantees

We present a method to generate a robot control strategy that maximizes the probability to accomplish a task. The task is given as a Linear Temporal Logic (LTL) formula over a set of properties that can be satisfied at the regions of a partitioned environment. We assume that the probabilities with which the properties are satisfied at the regions are known, and the robot can determine the truth value of a proposition only at the current region. Motivated by several results on partitioned-based abstractions, we assume that the motion is performed on a graph. To account for noisy sensors and actuators, we assume that a control action enables several transitions with known probabilities. We show that this problem can be reduced to the problem of generating a control policy for a Markov Decision Process (MDP) such that the probability of satisfying an LTL formula over its states is maximized. We provide a complete solution for the latter problem that builds on existing results from probabilistic model checking. We include an illustrative case study.Comment: Technical Report accompanying IFAC 201

Review of best management practices for aquatic vegetation control in stormwater ponds, wetlands, and lakes

Auckland Council (AC) is responsible for the development and operation of a stormwater network across the region to avert risks to citizens and the environment. Within this stormwater network, aquatic vegetation (including plants, unicellular and filamentous algae) can have both a positive and negative role in stormwater management and water quality treatment. The situations where management is needed to control aquatic vegetation are not always clear, and an inability to identify effective, feasible and economical control options may constrain management initiatives. AC (Infrastructure and Technical Services, Stormwater) commissioned this technical report to provide information for decision- making on aquatic vegetation management with in stormwater systems that are likely to experience vegetation-related issues. Information was collated from a comprehensive literature review, augmented by knowledge held by the authors. This review identified a wide range of management practices that could be potentially employed. It also demonstrated complexities and uncertainties relating to these options that makes the identification of a best management practice difficult. Hence, the focus of this report was to enable users to screen for potential options, and use reference material provided on each option to confirm the best practice to employ for each situation. The report identifies factors to define whether there is an aquatic vegetation problem (Section 3.0), and emphasises the need for agreed management goals for control (e.g. reduction, mitigation, containment, eradication). Resources to screen which management option(s) to employ are provided (Section 4.0), relating to the target aquatic vegetation, likely applicability of options to the system being managed, indicative cost, and ease of implementation. Initial screening allows users to shortlist potential control options for further reference (Section 5.0). Thirty-five control options are described (Section 5.0) in sufficient detail to consider applicability to individual sites and species. These options are grouped under categories of biological, chemical or physical control. Biological control options involve the use of organisms to predate, infect or control vegetation growth (e.g. classical biological control) or manipulate conditions to control algal growth (e.g. pest fish removal, microbial products). Chemical control options involve the use of pesticides and chemicals (e.g. glyphosate, diquat), or the use of flocculants and nutrient inactivation products that are used to reduce nutrient loading, thereby decreasing algal growth. Physical control options involve removing vegetation or algal biomass (e.g. mechanical or manual harvesting), or setting up barriers to their growth (e.g. shading, bottom lining, sediment capping). Preventative management options are usually the most cost effective, and these are also briefly described (Section 6.0). For example, the use of hygiene or quarantine protocols can reduce weed introductions or spread. Catchment- based practices to reduce sediment and nutrient sources to stormwater are likely to assist in the avoidance of algal and possibly aquatic plant problems. Nutrient removal may be a co-benefit where harvesting of submerged weed biomass is undertaken in stormwater systems. It should also be considered that removal of substantial amounts of submerged vegetation may result in a sudden and difficult-to-reverse s witch to a turbid, phytoplankton dominated state. Another possible solution is the conversion of systems that experience aquatic vegetation issues, to systems that are less likely to experience issues. The focus of this report is on systems that receive significant stormwater inputs, i.e. constructed bodies, including ponds, amenity lakes, wetlands, and highly-modified receiving bodies. However, some information will have application to other natural water bodies