Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature

This short review aims to make the reader familiar with state-of-the-art works relating to planning, scheduling and learning. First, we study state-of-the-art planning algorithms. We give a brief introduction of neural networks. Then we explore in more detail graph neural networks, a recent variant of neural networks suited for processing graph-structured inputs. We describe briefly the concept of reinforcement learning algorithms and some approaches designed to date. Next, we study some successful approaches combining neural networks for path-planning. Lastly, we focus on temporal planning problems with uncertainty.Comment: AAAI-format & update

Automatic Optimisation of Reliable Collaborative Services in OLSR Mobile Ad Hoc Networks

International audienceFuture Network based Operations (NbO) will strongly rely on Mobile Ad hoc Networks (MANET), due to urban area, tactical mobility and assymetric operation management. These networks will support multiple collaborative services, such as sensor to shooters, reachback, maintenance of Common Operational Picture (COP). Both networks and services will have to be managed with no or limited operator intervention, still providing reliable behavior in spite of aggressive environments. At routing level, we present how to preserve 2-connectivity by adapting the Optimised Link State Routing Protocol (OLSR). We also introduce the concept of active subnet management to retrieve maximal operational gain from collaborative services. Following a constraint solving method, the paper shows how to maximise the subnet of actors, while satisfying 2-connectivity, reactivity and communication quality constraints. We demonstrate the approach on simulating real world NbO

Towards automatic robust planning for the discrete commanding of aerospace equipment

Abstract-An increasing requirement for satellites, space probes and (unmanned) aircraft is that they exhibit robust behaviour without direct human intervention. Autonomous operation is required in spite of incomplete knowledge of an uncertain environment. In particular, embedded equipment that processes sensing data must consider uncertain input parameters while managing its own activities. We show how uncertainty may be addressed in constraint-based planning and scheduling functions for aerospace equipment, contrasting with some current practice in Integrated Modular Avionic (IMA) design. We produce a conditional plan that takes account of foreseeable contingencies, so guaranteeing system behaviour in the worst case. Executing a branch of the plan corresponds to synthesising a deterministic finite state automaton capable of discrete event commanding of an avionic sub-system. Experimental results show the feasibility of the approach for realistic aerospace equipment

Constrained Navigation with Mandatory Waypoints in Uncertain Environment

Also available online at http://www.ijisce.org/admin/upload/946980IJISCE-Constrained%20Navigation%20with%20Mandatory%20Waypoints%20in%20Uncertain%20Environment.pdfInternational audienceThis paper presents a hybrid solving method for vehicle path planning problems. As part of the vehicle system architecture (vetronic), planning is dynamic and has to be activated on-line, which requires response times to be compatible with mission execution. The proposed approach combines constraint solving techniques with an Ant Colony Optimization (ACO). The hybridization relies on a static probing technique which builds up a search strategy using a distance information between problem variables and a heuristic solution. Various forms of this approach are compared and evaluated on real world scenarios. Preliminary results exhibit response times close to vehicle control requirements, on realistic problem instances

Solving Disjunctive Temporal Networks with Uncertainty under Restricted Time-Based Controllability using Tree Search and Graph Neural Networks

Planning under uncertainty is an area of interest in artificial intelligence. We present a novel approach based on tree search and graph machine learning for the scheduling problem known as Disjunctive Temporal Networks with Uncertainty (DTNU). Dynamic Controllability (DC) of DTNUs seeks a reactive scheduling strategy to satisfy temporal constraints in response to uncontrollable action durations. We introduce new semantics for reactive scheduling: Time-based Dynamic Controllability (TDC) and a restricted subset of TDC, R-TDC. We design a tree search algorithm to determine whether or not a DTNU is R-TDC. Moreover, we leverage a graph neural network as a heuristic for tree search guidance. Finally, we conduct experiments on a known benchmark on which we show R-TDC to retain significant completeness with regard to DC, while being faster to prove. This results in the tree search processing fifty percent more DTNU problems in R-TDC than the state-of-the-art DC solver does in DC with the same time budget. We also observe that graph neural network search guidance leads to substantial performance gains on benchmarks of more complex DTNUs, with up to eleven times more problems solved than the baseline tree search.Comment: Thirty-Sixth AAAI Conference on Artificial Intelligence. This version includes the technical appendix. arXiv admin note: substantial text overlap with arXiv:2108.0106

SAFEST: A Framework for Early Security Triggers in Public Spaces

International audiencePublic spaces such as airports, railway stations or stadiums bring together large numbers of people on a quite limited space to use a security-sensitive infrastructure. Electronic security systems may help to provide better and faster security, as well as safety for the general public. Application scenarios may include intrusion detection and monitoring of large crowds in order to provide guidance in case of unexpected events (e.g., a mass panic). However, current security systems used within the public infrastructure are typically expensive, non-trivial to deploy, difficult to operate and maintain, prone to malfunction due to individual component failures, and generally lack citizen privacy-friendliness. The advent of novel, large-scale distributed security systems based on wireless, lightweight sensors may enhance security and safety in public spaces. In this realm, SAFEST is a project aiming at analyzing the social context of area surveillance and developing a system that can fulfill this task, both in terms of technology as well as acceptance by the general public. The targeted system will operate in a distributed way, collect anonymized data, securely transfer this data to a central location for evaluation, and - if necessary - notify the operator or issue alerts directly to the general public. Work on the technical aspects of the system is accompanied by social studies investigating the individual perception of risk and the methods for reaching public acceptance of the technical solutions

Area & Perimeter Surveillance in SAFEST using Sensors and the Internet of Things

International audienceSAFEST is a project aiming to provide a comprehensive solution to ensure the safety and security of the general public and critical infrastructures. The approach of the project is to design a lightweight, distributed system using heterogeneous, networked sensors, able to aggregate the input of a wide variety of signals (e.g. camera, PIR, radar, magnetic, seismic, acoustic). The project aims for a proof-of-concept demonstration focusing on a concrete scenario: crowd monitoring, area and perimeter surveillance in an airport, realized with a prototype of the system, which must be deployable and foldable overnight, and leverage autoconfiguration based on wireless communications and Internet of Things. This paper reviews the progress towards reaching this goal, which is planned for 2015

Chemical Imaging on Liver Steatosis Using Synchrotron Infrared and ToF-SIMS Microspectroscopies

Fatty liver or steatosis is a frequent histopathological change. It is a precursor for steatohepatitis that may progress to cirrhosis and in some cases to hepatocellular carcinoma. In this study we addressed the in situ composition and distribution of biochemical compounds on tissue sections of steatotic liver using both synchrotron FTIR (Fourier transform infrared) and ToF-SIMS (time of flight secondary ion mass spectrometry) microspectroscopies. FTIR is a vibrational spectroscopy that allows investigating the global biochemical composition and ToF-SIMS lead to identify molecular species in particular lipids. Synchrotron FTIR microspectroscopy demonstrated that bands linked to lipid contribution such as -CH3 and -CH2 as well as esters were highly intense in steatotic vesicles. Moreover, a careful analysis of the -CH2 symmetric and anti-symmetric stretching modes revealed a slight downward shift in spectra recorded inside steatotic vesicles when compared to spectra recorded outside, suggesting a different lipid environment inside the steatotic vesicles. ToF-SIMS analysis of such steatotic vesicles disclosed a selective enrichment in cholesterol as well as in diacylglycerol (DAG) species carrying long alkyl chains. Indeed, DAG C36 species were selectively localized inside the steatotic vesicles whereas DAG C30 species were detected mostly outside. Furthermore, FTIR detected a signal corresponding to olefin (C = C, 3000-3060 cm−1) and revealed a selective localization of unsaturated lipids inside the steatotic vesicles. ToF-SIMS analysis definitely demonstrated that DAG species C30, C32, C34 and C36 carrying at least one unsaturated alkyl chain were selectively concentrated into the steatotic vesicles. On the other hand, investigations performed on the non-steatotic part of the fatty livers have revealed important changes when compared to the normal liver. Although the non-steatotic regions of fatty livers exhibited normal histological aspect, IR spectra demonstrated an increase in the lipid content and ToF-SIMS detected small lipid droplets corresponding most likely to the first steps of lipid accretion

Procédure administrative contentieuse : une expertise ordonnée par le juge d'appel peut justifier à certaines conditions une réévaluation de l'indemnité demandée en première instance

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Du droit de la responsabilité administrative dans ses rapports avec la notion de risque

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