On the applicability of random mobility models for swarm robot movements

Random mobility models have been traditionally used to represent cellular or ad hoc movement patterns for PDA and laptop users. These models are critical for network traffic and protocol analysis. As robotics move to a paradigm where wireless network protocols are being utilized, it is unclear whether the same mobility models are applicable for analyzing these wireless network protocols. This thesis examines the similarity and differences between the traditional random mobility models and the mobility patterns exhibited by a team of randomly moving robots. Though the movements are driven by the same random functions, the robots need to detect and avoid collisions. Results obtained over different scenarios help establish a better understanding of how to develop mobility models representative of robot movements

Analysis of Driver Behavior Modeling in Connected Vehicle Safety Systems Through High Fidelity Simulation

A critical aspect of connected vehicle safety analysis is understanding the impact of human behavior on the overall performance of the safety system. Given the variation in human driving behavior and the expectancy for high levels of performance, it is crucial for these systems to be flexible to various driving characteristics. However, design, testing, and evaluation of these active safety systems remain a challenging task, exacerbated by the lack of behavioral data and practical test platforms. Additionally, the need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly and time-consuming. As an alternative option, researchers attempt to use simulation platforms to study and evaluate their algorithms. In this work, we introduce a high fidelity simulation platform, designed for a hybrid transportation system involving both human-driven and automated vehicles. We decompose the human driving task and offer a modular approach in simulating a large-scale traffic scenario, making it feasible for extensive studying of automated and active safety systems. Furthermore, we propose a human-interpretable driver model represented as a closed-loop feedback controller. For this model, we analyze a large driving dataset to extract expressive parameters that would best describe different driving characteristics. Finally, we recreate a similarly dense traffic scenario within our simulator and conduct a thorough analysis of different human-specific and system-specific factors and study their effect on the performance and safety of the traffic network

Trajectory Planning for Collision Avoidance in Urban Scenarios

In dieser Arbeit werden Ansätze zur Trajektorienplanung vorgestellt, die ein automatisiertes Ausweichen mit einem einzelnen kritischen dynamischen Hindernis in beschränkter Umgebung ermöglichten. Hierzu werden drei Varianten vorgestellt, die eine Kopplung von Längs- und Querdynamik erlauben: Die erste Variante betrachtet die Problemstellung der optimale Trajektorienplanung unter Berücksichtigung eines dynamischen Fahrzeugmodells. Die zweite Variante soll durch eine geeignete Parametrierung der Trajektorien das Verhalten des Fahrzeugmodells approximieren. Die dritte Variante erweitert die Zweite durch einen heuristischen Ansatz zur zeitlichen Abtastung. Das Potenzial der drei vorgestellten Verfahren wird im ersten Schritt simulativ nachgewiesen und im darauffolgenden Schritt wird das dritte Verfahren in einem Versuchsträger zum Nachweis der Echtzeitfähigkeit implementiert. Es wird gezeigt, dass die Ansätze für die Anwendung in Fahrerassistenzsystemen geeignet sind

Path Planning of Industrial Manipulators for Dynamic Obstacles using a New Sensory System

Industrial manipulators perform repetitive and dangerous tasks. They are widely used, however present a source for accidental collisions with human operators. Therefore, they require large isolated spaces heavily taxing factory real-estate. Thus, there exists a need to create a safe cooperative working space shared by both manipulators and humans. The purpose of this research is to provide such an environment by integrating a safety mat-style sensory system, with an implementation of a potential field trajectory planning algorithm. The safety mat sensor has been designed and constructed in a cost effective means acting as a proof of concept for future industrial applications. Both the safety mat and potential field algorithm have been integrated with a CRS F3 manipulator for conducting validation experiments. We have found that our implementation of the potential field algorithm can successfully avoid single, and multiple obstacles detected by the mat. Moreover, collision avoidance is achieved for both static and dynamic obstacles. Finally, our implementation of the potential field algorithm is capable of preventing local minima entrapment of the manipulator, a problem affecting past implementations

Predicting pedestrian trajectories at different densities: A multi-criteria empirical analysis

Predicting human trajectories is a challenging task due to the complexity of pedestrian behavior, which is influenced by external factors such as the scene's topology and interactions with other pedestrians. A special challenge arises from the dependence of the behaviour on the density of the scene. In the literature, deep learning algorithms show the best performance in predicting pedestrian trajectories, but so far just for situations with low densities. In this study, we aim to investigate the suitability of these algorithms for high-density scenarios by evaluating them on different error metrics and comparing their accuracy to that of knowledge-based models that have been used since long time in the literature. The findings indicate that deep learning algorithms provide improved trajectory prediction accuracy in the distance metrics for all tested densities. Nevertheless, we observe a significant number of collisions in the predictions, especially in high-density scenarios. This issue arises partly due to the absence of a collision avoidance mechanism within the algorithms and partly because the distance-based collision metric is inadequate for dense situations. To address these limitations, we propose the introduction of a novel continuous collision metric based on pedestrians' time-to-collision. Subsequently, we outline how this metric can be utilized to enhance the training of the algorithms

Autonomous Road Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds

An Autonomous Road Vehicle (ARV) can navigate various types of road networks using inputs such as throttle (acceleration), braking (deceleration), and steering (change of lateral direction). In most ARV driving scenarios that involve normal vehicle traffic and encounters with vulnerable road users (VRUs), ARVs are not required to take evasive action. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for ARVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM Framework offers usage of the ARV's sensing, perception, control, and actuation system abilities as one cohesive system, to accomplish avoidance of an on-road obstacle, based first on performance feasibility and second on passenger comfort, and is designed to be well-integrated within an ARV high-level system. Co-simulation including the ARV EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and given in the context of an entire ARV system, with implications for future work.Comment: 50 pages, 25 figures, 2 table

Autonomous Collision avoidance for Unmanned aerial systems

Unmanned Aerial System (UAS) applications are growing day by day and this will lead Unmanned Aerial Vehicle (UAV) in the close future to share the same airspace of manned aircraft.This implies the need for UAS to define precise safety standards compatible with operations standards for manned aviation. Among these standards the need for a Sense And Avoid (S&A) system to support and, when necessary, sub¬stitute the pilot in the detection and avoidance of hazardous situations (e.g. midair collision, controlled flight into terrain, flight path obstacles, and clouds). This thesis presents the work come out in the development of a S&A system taking into account collision risks scenarios with multiple moving and fixed threats. The conflict prediction is based on a straight projection of the threats state in the future. The approximations introduced by this approach have the advantage of high update frequency (1 Hz) of the estimated conflict geometry. This solution allows the algorithm to capture the trajectory changes of the threat or ownship. The resolution manoeuvre evaluation is based on a optimisation approach considering step command applied to the heading and altitude autopilots. The optimisation problem takes into account the UAV performances and aims to keep a predefined minimum separation distance between UAV and threats during the resolution manouvre. The Human-Machine Interface (HMI) of this algorithm is then embedded in a partial Ground Control Station (GCS) mock-up with some original concepts for the indication of the flight condition parameters and the indication of the resolution manoeuvre constraints. Simulations of the S&A algorithm in different critical scenarios are moreover in-cluded to show the algorithm capabilities. Finally, methodology and results of the tests and interviews with pilots regarding the proposed GCS partial layout are covered

Developing an advanced collision risk model for autonomous vehicles

Aiming at improving road safety, car manufacturers and researchers are verging upon autonomous vehicles. In recent years, collision prediction methods of autonomous vehicles have begun incorporating contextual information such as information about the traffic environment and the relative motion of other traffic participants but still fail to anticipate traffic scenarios of high complexity. During the past two decades, the problem of real-time collision prediction has also been investigated by traffic engineers. In the traffic engineering approach, a collision occurrence can potentially be predicted in real-time based on available data on traffic dynamics such as the average speed and flow of vehicles on a road segment. This thesis attempts to integrate vehicle-level collision prediction approaches for autonomous vehicles with network-level collision prediction, as studied by traffic engineers. [Continues.

Adaptive Perception, State Estimation, and Navigation Methods for Mobile Robots

In this cumulative habilitation, publications with focus on robotic perception, self-localization, tracking, navigation, and human-machine interfaces have been selected. While some of the publications present research on a PR2 household robot in the Robotics Learning Lab of the University of California Berkeley on vision and machine learning tasks, most of the publications present research results while working at the AutoNOMOS-Labs at Freie Universität Berlin, with focus on control, planning and object tracking for the autonomous vehicles "MadeInGermany" and "e-Instein"