Reject or Embrace Electric Cargo Bikes? An investigation of the Sustainability Effects of a Transition from Electric Vans to Electric Cargo Bikes in Last-Mile Delivery

In the recent period, there has been a massive increase in e-commerce. This has vastly changed Norwegian customers' shopping behavior and provided an increase in last-mile delivery. Simultaneously, the focus on sustainability has become more significant, and the Bergen Municipality is looking for ways to reduce traffic in the city center. This thesis investigates how a transition from e-vans to e-cargo bikes will affect sustainability in a city center. Both the Vehicle Routing Problem (VRP) and the Traveling Salesman Problem have been used to solve the route optimization problem, where it is assumed that all deliveries are made with either e-vans or e-cargo bikes. A mathematical model was developed from the beginning, but due to the large number of addresses, each address was assigned to a cluster. Then, Google OR-Tools was used to solve the VRP. The mathematical model and the Google OR-Tools model proved to perform equally well when compared using a small number of addresses. The main findings show that sustainability in a city center will be positively affected by a transition from e-vans to e-cargo bikes in last-mile delivery. This is because the social, environmental, and economic sustainability pillar will be positively affected due to lower noise levels, lower environmentally harmful emissions, and the potential for lower costs. The sensitivity analysis shows, however, that the number of meters traveled with e-cargo bikes increases faster than for e-vans when the demand for parcels increases. We believe that our findings will apply to cities with the same characteristics as Bergen, such as population and the size of the city center.nhhma

Minimum-Energy Exploration and Coverage for Robotic Systems

This dissertation is concerned with the question of autonomously and efficiently exploring three-dimensional environments. Hence, three robotics problems are studied in this work: the motion planning problem, the coverage problem and the exploration problem. The work provides a better understanding of motion and exploration problems with regard to their mathematical formulation and computational complexity, and proposes solutions in the form of algorithms capable of being implemented on a wide range of robotic systems.Because robots generally operate on a limited power source, the primary focus is on minimizing energy while moving or navigating in the environment. Many approaches address motion planning in the literature, however few attempt to provide a motion that aims at reducing the amount of energy expended during that process. We present a new approach, we call integral-squared torque approximation, that can be integrated with existing motion planners to find low-energy and collision-free paths in the robot\u27s configuration space.The robotics coverage problem has many real-world applications such as removing landmines or surveilling an area. We prove that this problem is inherently difficult to solve in its general case, and we provide an approach that is shown to be probabilistically complete, and that aims at minimizing a cost function (such as energy.) The remainder of the dissertation focuses on minimum-energy exploration, and offers a novel formulation for the problem. The formulation can be directly applied to compare exploration algorithms. In addition, an approach that aims at reducing energy during the exploration process is presented, and is shown through simulation to perform better than existing algorithms

UNMANNED AERIAL VEHICLES: TRAJECTORY PLANNING AND ROUTING IN THE ERA OF ADVANCED AIR MOBILITY

Advanced air mobility (AAM) is a revolutionary concept that enables on-demand air mobility, cargo delivery, and emergency services via an integrated and connected multimodal transportation network. In the era of AAM, unmanned aerial vehicles are envisioned as the primary tool for transporting people and cargo from point A to point B. This thesis focuses on the development of a core decision-making engine for strategic vehicle routing and trajectory planning of autonomous vehicles (AVs) with the goal of enhancing the system-wide safety, efficiency, and scalability. Part I of the thesis addresses the routing and coordination of a drone-truck pairing, where the drone travels to multiple locations to perform specified observation tasks and rendezvous periodically with the truck to swap its batteries. Drones, as an alternative mode of transportation, have advantages in terms of lower costs, better service, or the potential to provide new services that were previously not possible. Typically, those services involve routing a fleet of drones to meet specific demands. Despite the potential benefits, the drone has a natural limitation on the flight range due to its battery capacity. As a result, enabling the combination of a drone with a ground vehicle, which can serve as a mobile charging platform for the drone, is an important opportunity for practical impact and research challenges. We first propose a Mixed Integer Quadratically-constrained Programming driven by critical operational constraints. Given the NP-hard nature of the so called Nested-VRP, we analyze the complexity of the MIQCP model and propose both enhanced exact approach and efficient heuristic for solving the Nested-VRP model. We envision that this framework will facilitate the planning and operations of combined drone-truck missions and further improve the scalability and efficiency of the AAM system. Part II of the thesis focuses on the survivability reasoning and trajectory planning of UAVs under uncertainty. Maintaining the survivability of an UAV requires that it precisely perceives and transitions between safe states in the airspace. We first propose a methodology to construct a survivability map for an UAV as a function of the vehicle's maneuverability, remaining lifetime, availability of landing sites, and the volume of air traffic. The issue of trajectory planning under uncertainty has received a lot of attention in the robotics and control communities. Traditional trajectory planning approaches rely primarily on the premise that the uncertainty of dynamic obstacles is either bounded or can be statistically modeled. This is not the case in the urban environment, where the sources of uncertainty are diverse, and their uncertain behavior is typically unpredictable, making precise modeling impossible. Motivated by this, we present a receding horizon control method with innovative trajectory planning policies that enable dynamic updating of planned trajectories in the presence of partially known and unknown uncertainty. The findings of this study have significant implications for achieving safe aviation autonomy within the AAM system.Ph.D

Exploration autonome et efficiente de chantiers miniers souterrains inconnus avec un drone filaire

Abstract: Underground mining stopes are often mapped using a sensor located at the end of a pole that the operator introduces into the stope from a secure area. The sensor emits laser beams that provide the distance to a detected wall, thus creating a 3D map. This produces shadow zones and a low point density on the distant walls. To address these challenges, a research team from the Université de Sherbrooke is designing a tethered drone equipped with a rotating LiDAR for this mission, thus benefiting from several points of view. The wired transmission allows for unlimited flight time, shared computing, and real-time communication. For compatibility with the movement of the drone after tether entanglements, the excess length is integrated into an onboard spool, contributing to the drone payload. During manual piloting, the human factor causes problems in the perception and comprehension of a virtual 3D environment, as well as the execution of an optimal mission. This thesis focuses on autonomous navigation in two aspects: path planning and exploration. The system must compute a trajectory that maps the entire environment, minimizing the mission time and respecting the maximum onboard tether length. Path planning using a Rapidly-exploring Random Tree (RRT) quickly finds a feasible path, but the optimization is computationally expensive and the performance is variable and unpredictable. Exploration by the frontier method is representative of the space to be explored and the path can be optimized by solving a Traveling Salesman Problem (TSP) but existing techniques for a tethered drone only consider the 2D case and do not optimize the global path. To meet these challenges, this thesis presents two new algorithms. The first one, RRT-Rope, produces an equal or shorter path than existing algorithms in a significantly shorter computation time, up to 70% faster than the next best algorithm in a representative environment. A modified version of RRT-connect computes a feasible path, shortened with a deterministic technique that takes advantage of previously added intermediate nodes. The second algorithm, TAPE, is the first 3D cavity exploration method that focuses on minimizing mission time and unwound tether length. On average, the overall path is 4% longer than the method that solves the TSP, but the tether remains under the allowed length in 100% of the simulated cases, compared to 53% with the initial method. The approach uses a 2-level hierarchical architecture: global planning solves a TSP after frontier extraction, and local planning minimizes the path cost and tether length via a decision function. The integration of these two tools in the NetherDrone produces an intelligent system for autonomous exploration, with semi-autonomous features for operator interaction. This work opens the door to new navigation approaches in the field of inspection, mapping, and Search and Rescue missions.La cartographie des chantiers miniers souterrains est souvent réalisée à l’aide d’un capteur situé au bout d’une perche que l’opérateur introduit dans le chantier, depuis une zone sécurisée. Le capteur émet des faisceaux laser qui fournissent la distance à un mur détecté, créant ainsi une carte en 3D. Ceci produit des zones d’ombres et une faible densité de points sur les parois éloignées. Pour relever ces défis, une équipe de recherche de l’Université de Sherbrooke conçoit un drone filaire équipé d’un LiDAR rotatif pour cette mission, bénéficiant ainsi de plusieurs points de vue. La transmission filaire permet un temps de vol illimité, un partage de calcul et une communication en temps réel. Pour une compatibilité avec le mouvement du drone lors des coincements du fil, la longueur excédante est intégrée dans une bobine embarquée, qui contribue à la charge utile du drone. Lors d’un pilotage manuel, le facteur humain entraîne des problèmes de perception et compréhension d’un environnement 3D virtuel, et d’exécution d’une mission optimale. Cette thèse se concentre sur la navigation autonome sous deux aspects : la planification de trajectoire et l’exploration. Le système doit calculer une trajectoire qui cartographie l’environnement complet, en minimisant le temps de mission et en respectant la longueur maximale de fil embarquée. La planification de trajectoire à l’aide d’un Rapidly-exploring Random Tree (RRT) trouve rapidement un chemin réalisable, mais l’optimisation est coûteuse en calcul et la performance est variable et imprévisible. L’exploration par la méthode des frontières est représentative de l’espace à explorer et le chemin peut être optimisé en résolvant un Traveling Salesman Problem (TSP), mais les techniques existantes pour un drone filaire ne considèrent que le cas 2D et n’optimisent pas le chemin global. Pour relever ces défis, cette thèse présente deux nouveaux algorithmes. Le premier, RRT-Rope, produit un chemin égal ou plus court que les algorithmes existants en un temps de calcul jusqu’à 70% plus court que le deuxième meilleur algorithme dans un environnement représentatif. Une version modifiée de RRT-connect calcule un chemin réalisable, raccourci avec une technique déterministe qui tire profit des noeuds intermédiaires préalablement ajoutés. Le deuxième algorithme, TAPE, est la première méthode d’exploration de cavités en 3D qui minimise le temps de mission et la longueur du fil déroulé. En moyenne, le trajet global est 4% plus long que la méthode qui résout le TSP, mais le fil reste sous la longueur autorisée dans 100% des cas simulés, contre 53% avec la méthode initiale. L’approche utilise une architecture hiérarchique à 2 niveaux : la planification globale résout un TSP après extraction des frontières, et la planification locale minimise le coût du chemin et la longueur de fil via une fonction de décision. L’intégration de ces deux outils dans le NetherDrone produit un système intelligent pour l’exploration autonome, doté de fonctionnalités semi-autonomes pour une interaction avec l’opérateur. Les travaux réalisés ouvrent la porte à de nouvelles approches de navigation dans le domaine des missions d’inspection, de cartographie et de recherche et sauvetage

Assessment of genetic algorithm based assignment strategies for unmanned systems using the multiple traveling salesman problem with moving targets

Title from PDF of title page, viewed March 1, 2023Thesis advisor: Travis FieldsVitaIncludes bibliographical references (pages 100-106)Thesis (M.S.)--Department of Civil and Mechanical Engineering. University of Missouri--Kansas City, 2021The continuous and rapid advancements in autonomous unmanned systems technologies presents increasingly sophisticated threats to military operations. These threats necessitate the prioritization of improved strategies for military resources and air base defense. In many scenarios, it is necessary to combat hostile unmanned systems before they reach the defensible perimeters of existing fixed-base defense systems. One solution to this problem is weaponizing friendly unmanned systems to hunt and kill hostile unmanned systems. However, the assignment and path planning of these “Hunter-Killer” systems to incoming hostile unmanned systems, in a multiple friendly versus multiple enemy scenario, presents a major challenge and can be represented by the Multiple Traveling Salesmen Problem with Moving Targets (MTSPMT). The MTSPMT is a combinatorial optimization problem and an extension of the classical Traveling Salesman Problem whereby the number of salesmen is increased and targets (cities) move with respect to time. The objective of the MTSPMT, for the application of military defense using a squadron of Hunter-Killer unmanned systems, is to determine a path that minimizes the cost required for multiple Hunter-Killer unmanned systems to successfully intercept all incoming threats. In this study, an assessment of genetic algorithm based assignment strategies for unmanned systems using the MTSPMT is performed. A number of scenarios were constructed using up to 50 hostile unmanned systems and the generated solutions were compared based on their resulting time to converge, solution fitness, and number of generations required. Findings indicate that under certain conditions genetic based algorithms provide better results on average and converge more rapidly than brute force searching and existing assignment and path planning solutions.Introduction -- Literature review -- Problem formulation and model design -- Methodology -- Results and Discussion -- Conclusio

Robot Navigation in Human Environments

For the near future, we envision service robots that will help us with everyday chores in home, office, and urban environments. These robots need to work in environments that were designed for humans and they have to collaborate with humans to fulfill their tasks. In this thesis, we propose new methods for communicating, transferring knowledge, and collaborating between humans and robots in four different navigation tasks. In the first application, we investigate how automated services for giving wayfinding directions can be improved to better address the needs of the human recipients. We propose a novel method based on inverse reinforcement learning that learns from a corpus of human-written route descriptions what amount and type of information a route description should contain. By imitating the human teachers' description style, our algorithm produces new route descriptions that sound similarly natural and convey similar information content, as we show in a user study. In the second application, we investigate how robots can leverage background information provided by humans for exploring an unknown environment more efficiently. We propose an algorithm for exploiting user-provided information such as sketches or floor plans by combining a global exploration strategy based on the solution of a traveling salesman problem with a local nearest-frontier-first exploration scheme. Our experiments show that the exploration tours are significantly shorter and that our system allows the user to effectively select the areas that the robot should explore. In the second part of this thesis, we focus on humanoid robots in home and office environments. The human-like body plan allows humanoid robots to navigate in environments and operate tools that were designed for humans, making humanoid robots suitable for a wide range of applications. As localization and mapping are prerequisites for all navigation tasks, we first introduce a novel feature descriptor for RGB-D sensor data and integrate this building block into an appearance-based simultaneous localization and mapping system that we adapt and optimize for the usage on humanoid robots. Our optimized system is able to track a real Nao humanoid robot more accurately and more robustly than existing approaches. As the third application, we investigate how humanoid robots can cover known environments efficiently with their camera, for example for inspection or search tasks. We extend an existing next-best-view approach by integrating inverse reachability maps, allowing us to efficiently sample and check collision-free full-body poses. Our approach enables the robot to inspect as much of the environment as possible. In our fourth application, we extend the coverage scenario to environments that also include articulated objects that the robot has to actively manipulate to uncover obstructed regions. We introduce algorithms for navigation subtasks that run highly parallelized on graphics processing units for embedded devices. Together with a novel heuristic for estimating utility maps, our system allows to find high-utility camera poses for efficiently covering environments with articulated objects. All techniques presented in this thesis were implemented in software and thoroughly evaluated in user studies, simulations, and experiments in both artificial and real-world environments. Our approaches advance the state of the art towards universally usable robots in everyday environments.Roboternavigation in menschlichen Umgebungen In naher Zukunft erwarten wir Serviceroboter, die uns im Haushalt, im Büro und in der Stadt alltägliche Arbeiten abnehmen. Diese Roboter müssen in für Menschen gebauten Umgebungen zurechtkommen und sie müssen mit Menschen zusammenarbeiten um ihre Aufgaben zu erledigen. In dieser Arbeit schlagen wir neue Methoden für die Kommunikation, Wissenstransfer und Zusammenarbeit zwischen Menschen und Robotern bei Navigationsaufgaben in vier Anwendungen vor. In der ersten Anwendung untersuchen wir, wie automatisierte Dienste zur Generierung von Wegbeschreibungen verbessert werden können, um die Beschreibungen besser an die Bedürfnisse der Empfänger anzupassen. Wir schlagen eine neue Methode vor, die inverses bestärkendes Lernen nutzt, um aus einem Korpus von von Menschen geschriebenen Wegbeschreibungen zu lernen, wie viel und welche Art von Information eine Wegbeschreibung enthalten sollte. Indem unser Algorithmus den Stil der Wegbeschreibungen der menschlichen Lehrer imitiert, kann der Algorithmus neue Wegbeschreibungen erzeugen, die sich ähnlich natürlich anhören und einen ähnlichen Informationsgehalt vermitteln, was wir in einer Benutzerstudie zeigen. In der zweiten Anwendung untersuchen wir, wie Roboter von Menschen bereitgestellte Hintergrundinformationen nutzen können, um eine bisher unbekannte Umgebung schneller zu erkunden. Wir schlagen einen Algorithmus vor, der Hintergrundinformationen wie Gebäudegrundrisse oder Skizzen nutzt, indem er eine globale Explorationsstrategie basierend auf der Lösung eines Problems des Handlungsreisenden kombiniert mit einer lokalen Explorationsstrategie. Unsere Experimente zeigen, dass die Erkundungstouren signifikant kürzer werden und dass der Benutzer mit unserem System effektiv die zu erkundenden Regionen spezifizieren kann. Der zweite Teil dieser Arbeit konzentriert sich auf humanoide Roboter in Umgebungen zu Hause und im Büro. Der menschenähnliche Körperbau ermöglicht es humanoiden Robotern, in Umgebungen zu navigieren und Werkzeuge zu benutzen, die für Menschen gebaut wurden, wodurch humanoide Roboter für vielfältige Aufgaben einsetzbar sind. Da Lokalisierung und Kartierung Grundvoraussetzungen für alle Navigationsaufgaben sind, führen wir zunächst einen neuen Merkmalsdeskriptor für RGB-D-Sensordaten ein und integrieren diesen Baustein in ein erscheinungsbasiertes simultanes Lokalisierungs- und Kartierungsverfahren, das wir an die Besonderheiten von humanoiden Robotern anpassen und optimieren. Unser System kann die Position eines realen humanoiden Roboters genauer und robuster verfolgen, als es mit existierenden Ansätzen möglich ist. Als dritte Anwendung untersuchen wir, wie humanoide Roboter bekannte Umgebungen effizient mit ihrer Kamera abdecken können, beispielsweise zu Inspektionszwecken oder zum Suchen eines Gegenstands. Wir erweitern ein bestehendes Verfahren, das die nächstbeste Beobachtungsposition berechnet, durch inverse Erreichbarkeitskarten, wodurch wir kollisionsfreie Ganzkörperposen effizient generieren und prüfen können. Unser Ansatz ermöglicht es dem Roboter, so viel wie möglich von der Umgebung zu untersuchen. In unserer vierten Anwendung erweitern wir dieses Szenario um Umgebungen, die auch bewegbare Gegenstände enthalten, die der Roboter aktiv bewegen muss um verdeckte Regionen zu sehen. Wir führen Algorithmen für Teilprobleme ein, die hoch parallelisiert auf Grafikkarten von eingebetteten Systemen ausgeführt werden. Zusammen mit einer neuen Heuristik zur Schätzung von Nutzenkarten ermöglicht dies unserem System Beobachtungspunkte mit hohem Nutzen zu finden, um Umgebungen mit bewegbaren Objekten effizient zu inspizieren. Alle vorgestellten Techniken wurden in Software implementiert und sorgfältig evaluiert in Benutzerstudien, Simulationen und Experimenten in künstlichen und realen Umgebungen. Unsere Verfahren bringen den Stand der Forschung voran in Richtung universell einsetzbarer Roboter in alltäglichen Umgebungen

Ant-inspired Interaction Networks For Decentralized Vehicular Traffic Congestion Control

Mimicking the autonomous behaviors of animals and their adaptability to changing or foreign environments lead to the development of swarm intelligence techniques such as ant colony optimization (ACO) and particle swarm optimization (PSO) now widely used to tackle a variety of optimization problems. The aim of this dissertation is to develop an alternative swarm intelligence model geared toward decentralized congestion avoidance and to determine qualities of the model suitable for use in a transportation network. A microscopic multi-agent interaction network inspired by insect foraging behaviors, especially ants, was developed and consequently adapted to prioritize the avoidance of congestion, evaluated as perceived density of other agents in the immediate environment extrapolated from the occurrence of direct interactions between agents, while foraging for food outside the base/nest. The agents eschew pheromone trails or other forms of stigmergic communication in favor of these direct interactions whose rate is the primary motivator for the agents\u27 decision making process. The decision making process at the core of the multi-agent interaction network is consequently transferred to transportation networks utilizing vehicular ad-hoc networks (VANETs) for communication between vehicles. Direct interactions are replaced by dedicated short range communications for wireless access in vehicular environments (DSRC/WAVE) messages used for a variety of applications like left turn assist, intersection collision avoidance, or cooperative adaptive cruise control. Each vehicle correlates the traffic on the wireless network with congestion in the transportation network and consequently decides whether to reroute and, if so, what alternate route to take in a decentralized, non-deterministic manner. The algorithm has been shown to increase throughput and decrease mean travel times significantly while not requiring access to centralized infrastructure or up-to-date traffic information

On Realization of Intelligent Decision-Making in the Real World: A Foundation Decision Model Perspective

Our situated environment is full of uncertainty and highly dynamic, thus hindering the widespread adoption of machine-led Intelligent Decision-Making (IDM) in real world scenarios. This means IDM should have the capability of continuously learning new skills and efficiently generalizing across wider applications. IDM benefits from any new approaches and theoretical breakthroughs that exhibit Artificial General Intelligence (AGI) breaking the barriers between tasks and applications. Recent research has well-examined neural architecture, Transformer, as a backbone foundation model and its generalization to various tasks, including computer vision, natural language processing, and reinforcement learning. We therefore argue that a foundation decision model (FDM) can be established by formulating various decision-making tasks as a sequence decoding task using the Transformer architecture; this would be a promising solution to advance the applications of IDM in more complex real world tasks. In this paper, we elaborate on how a foundation decision model improves the efficiency and generalization of IDM. We also discuss potential applications of a FDM in multi-agent game AI, production scheduling, and robotics tasks. Finally, through a case study, we demonstrate our realization of the FDM, DigitalBrain (DB1) with 1.2 billion parameters, which achieves human-level performance over 453 tasks, including text generation, images caption, video games playing, robotic control, and traveling salesman problems. As a foundation decision model, DB1 would be a baby step towards more autonomous and efficient real world IDM applications.Comment: 26 pages, 4 figure