Conservative collision prediction and avoidance for stochastic trajectories in continuous time and space

Existing work in multi-agent collision prediction and avoidance typically assumes discrete-time trajectories with Gaussian uncertainty or that are completely deterministic. We propose an approach that allows detection of collisions even between continuous, stochastic trajectories with the only restriction that means and variances can be computed. To this end, we employ probabilistic bounds to derive criterion functions whose negative sign provably is indicative of probable collisions. For criterion functions that are Lipschitz, an algorithm is provided to rapidly find negative values or prove their absence. We propose an iterative policy-search approach that avoids prior discretisations and yields collision-free trajectories with adjustably high certainty. We test our method with both fixed-priority and auction-based protocols for coordinating the iterative planning process. Results are provided in collision-avoidance simulations of feedback controlled plants.Comment: This preprint is an extended version of a conference paper that is to appear in \textit{Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014)

Collision analysis for an UAV

International audienceThe Sense and Avoid capacity of Unmanned Aerial Vehicles (UAV) is one of the key elements to open the access to airspace for UAVs. In order to replace a pilot's See and Avoid capacity such a system has to be certified "as safe as a human pilot on-board". The problem is to prove that an unmanned aircraft equipped with a S and A system can comply with the actual air transportation regulations. This paper aims to provide mathematical and numerical tools to link together the safety objectives and sensors specifications. Our approach starts with the natural idea of a specified "safety volume" around the aircraft: the safety objective is to guarantee that no other aircraft can penetrate this volume. We use a general reachability and viability concepts to define nested sets which are meaningful to allocate sensor performances and manoeuvring capabilities necessary to protect the safety volume. Using the general framework of HJB equations for the optimal control and differential games, we give a rigorous mathematical characterization of these sets. Our approach allows also to take into account some uncertainties in the measures of the parameters of the incoming traffic. We also provide numerical tools to compute the defined sets, so that the technical specifications of a S and A system can be derived in accordance with a small set of intuitive parameters. We consider several dynamical models corresponding to the different choices of maneuvers (lateral, longitudinal and mixed). Our numerical simulations show clearly that the nature of used maneuvers is an important factor in the specifications of sensor's performances

Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships

Mobile Transport and Computing Platform for 5G Verticals: resource abstraction and implementation

This paper has been presented at: 2018 IEEE Conference on Network Function Virtualization and Software Define Networks (IEEE NFV-SDN)The 5G-TRANSFORMER project aims at transforming today's mobile transport networks into an SDN/NFV-based platform, which offers slices tailored to the needs of vertical industries. The paper describes the 5G-TRANSFORMER resource management layer, namely MTP, and its main functionalities such as resource abstraction, resource information modeling and orchestration, and service instantiation. Then, it focuses on the ETSI-based interfaces exploited for the interaction between the control and management plane elements. Finally, the paper reports an MTP implementation including messages reporting resource abstraction.This work has been partially funded by the EU H2020 5G-TRANSFORMER Project (grant no. 761536)

Communication Algorithms for Wireless Ad Hoc Networks

In this dissertation we present deterministic algorithms for reliable and efficient communication in ad hoc networks. In the first part of this dissertation we give a specification for a reliable neighbor discovery layer for mobile ad hoc networks. We present two different algorithms that implement this layer with varying progress guarantees. In the second part of this dissertation we give an algorithm which allows nodes in a mobile wireless ad hoc network to communicate reliably and at the same time maintain local neighborhood information. In the last part of this dissertation we look at the distributed trigger counting problem in the wireless ad hoc network setting. We present a deterministic algorithm for this problem which is communication efficient in terms of the the maximum number of messages received by any processor in the system

Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications

For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

Automated Feature Extraction from Large Cardiac Electrophysiological Data Sets, and a Population Dynamics Approach to the Distribution of Space Debris in Low-Earth Orbit

We present two applications of mathematics to relevant real-world situations. In the first chapter, we discuss an automated method for the extraction of useful data from large file-size readings of cardiac data. We begin by describing the history of electrophysiology and the background of the work\u27s setting, wherein a new multi-electrode array-based application for the long-term recording of action potentials from electrogenic cells makes large-scale readings of relevant data possible, opening the way for exciting cardiac electrophysiology studies in health and disease. With hundreds of simultaneous electrode recordings being acquired over a period of days, the main challenge becomes achieving reliable signal identification and quantification. In the context of this method of data collection, we set out to develop an algorithm capable of automatically extracting regions of high-quality action potentials from terabyte size experimental results and to map the trains of action potentials into a low-dimensional feature space for analysis. We establish that our automatic segmentation algorithm finds regions of acceptable action potentials in large data sets of electrophysiological readings. We use spectral methods and support vector machines to classify our readings and to extract relevant features. We are able to show that action potentials from the same cell site can be recorded over days without detrimental effects to the cell membrane. The variability between measurements 24 h apart is comparable to the natural variability of the features at a single time point. this work contributes towards a non-invasive approach for cardiomyocyte functional maturation, as well as developmental, pathological and pharmacological studies. As the human-derived cardiac model tissue has the genetic makeup of its donor, a powerful tool for individual drug toxicity screening emerges. In the second chapter we consider the population of objects, largely considered debris, in the region of outer space close to Earth. the presence of this debris in Earth\u27s orbit poses a significant risk to human activity in outer space. This debris population continues to grow due to ground launches, loss of external parts from space ships, and uncontrollable collisions between objects. We examine the background of human space launch, the current methods of tracking objects, and modelling work done to date. We propose a diffusion-collision model for the evolution of debris density in Low-Earth Orbit (LEO) and its dependence on ground-launch policy, to arrive at a computationally feasible continuum-based model. We parametrize this model and test it against data from publicly available object catalogs to examine timescales for uncontrolled growth. Finally, we consider sensible launch policies and cleanup strategies and how they reduce the future risk of collisions with active satellites or space ships, along with considering extensions of the model to directly account for launch policy determination through minimization of certain functionals

MODELLING AND SYSTEMATIC EVALUATION OF MARITIME TRAFFIC SITUATION IN COMPLEX WATERS

Maritime Situational Awareness (MSA) plays a vital role in the development of intelligent transportation support systems. The surge in maritime traffic, combined with increasing vessel sizes and speeds, has intensified the complexity and risk of maritime traffic. This escalation presents a considerable challenge to the current systems and tools dedicated to maritime traffic monitoring and management. Meanwhile, the existing literature on advanced MSA methods and techniques is relatively limited, especially when it comes to addressing multi-ship interactions that may involve hybrid traffic of manned ships and emerging autonomous ships in complex and restricted waters in the future. The primary research question revolves around the challenge faced by current collision risk models in incorporating the impact of traffic characteristics in complex waters. This limitation hampers their effectiveness in managing complex maritime traffic situations. In view of this, the research aims to investigate and analyse the traffic characteristics in complex port waters and develop a set of advanced MSA methods and models in a holistic manner, so as to enhance maritime traffic situation perception capabilities and strengthen decision-making on anti-collision risk control. This study starts with probabilistic conflict detection by incorporating the dynamics and uncertainty that may be involved in ship movements. Then, the conflict criticality and spatial distance indicators are used together to partition the regional ship traffic into several compact, scalable, and interpretable clusters from both static and dynamic perspectives. On this basis, a systematic multi-scale collision risk approach is newly proposed to estimate the collision risk of a given traffic scenario from different spatial scales. The novelty of this research lies not only in the development of new modelling techniques on MSA that have never been done by using various advanced techniques (e.g., Monte Carlo simulation, image processing techniques, graph-based clustering techniques, complex network theory, and fuzzy clustering iterative method) but also in the consideration of the impact of traffic characteristics in complex waters, such as multi-dependent conflicts, restricted water topography, and dynamic and uncertain ship motion behaviours. Extensive numerical experiments based on real AIS data in the world's busiest and most complex water area (i.e., Ningbo_Zhoushan Port, China) are carried out to evaluate the models’ performance. The research results show that the proposed models have rational and reliable performance in detecting potential collision danger under an uncertain environment, identifying high-risk traffic clusters, offering a complete comprehension of a traffic situation, and supporting strategic maritime safety management. These developed techniques and models provide useful insights and valuable implications for maritime practitioners on traffic surveillance and management, benefiting the safety and efficiency enhancement of maritime transportation. The research can also be tailored for a wide range of applications given its generalization ability in tackling various traffic scenarios in complex waters. It is believed that this work would make significant contributions in terms of 1) improving traffic safety management from an operational perspective without high financial requirements on infrastructure updating and 2) effectively supporting intelligent maritime surveillance and serving as a theoretical basis of promoting maritime safety management for the complex traffic of mixed manned and autonomous ships