Motion planning for redundant manipulators poses special challenges because the required inverse kinematics are difficult and not complete. This thesis investigates and proposes methods for motion planning for these systems that do not require inverse kinematics and are potentially complete. These methods are also compared in performance to standard inverse kinematics based methods.

In this thesis we study the problem of robotic interaction with objects from a flexible perspective that complements the rigid force-closure approach. In a flexible interaction the object is not firmly bound to the robot (immobilized), which leads to many interesting scenarios. We focus on the secure kind of flexible interactions, commonly referred to as caging grasps. In this context, the adjective secure implies that the object is not able to escape arbitrarily far away from the robot which is caging it. A cage is a secure flexible interaction because it does not immobilize the object, but restricts its motion to a finite set of possible configurations. We study cages in two novel scenarios for objects with holes: caging through multi-agent cooperation and through dual-arm knotting with a rope. From these two case studies, we were able to analyze the caging problem in a broader perspective leading to the definition of a hierarchical classification of flexible interactions and cages. In parallel to the geometric and physical problem of flexible interactions with objects, we study also the problem of discrete action scheduling through a novel control architecture called Behavior Trees (BTs). In this thesis we propose a formulation that unifies the competing BT philosophies into a single framework. We analyze how the mainstream BT formulations differ from each other, as well as their benefits and limitations. We also compare the plan representation capabilities of BTs with respect to the traditional approach of Controlled Hybrid Dynamical Systems (CHDSs). In this regard, we present bidirectional translation algorithms between such representations as well as the necessary and sufficient conditions for translation convergence. Lastly, we demonstrate our action scheduling BT architecture showcasing the aforementioned caging scenarios, as well as other examples that show how BTs can be interfaced with other high level planners. / <p>QC 20170322</p>

Many robotic systems are remotely operated nowadays that require uninterrupted connection and safe mission planning. Such systems are commonly found in military drones, search and rescue operations, mining robotics, agriculture, and environmental monitoring. Different robotic systems may employ disparate communication modalities such as radio network, visible light communication, satellite, infrared, Wi-Fi. However, in an autonomous mission where the robots are expected to be interconnected, communication constrained environment frequently arises due to the out of range problem or unavailability of the signal. Furthermore, several automated projects (building construction, assembly line) do not guarantee uninterrupted communication, and a safe project plan is required that optimizes collision risks, cost, and duration. In this thesis, we propose four pronged approaches to alleviate some of these issues: 1) Communication aware world mapping; 2) Communication preserving using the Line-of-Sight (LoS); 3) Communication aware safe planning; and 4) Multi-Objective motion planning for navigation. First, we focus on developing a communication aware world map that integrates traditional world models with the planning of multi-robot placement. Our proposed communication map selects the optimal placement of a chain of intermediate relay vehicles in order to maximize communication quality to a remote unit. We also vi propose an algorithm to build a min-Arborescence tree when there are multiple remote units to be served. Second, in communication denied environments, we use Line-of-Sight (LoS) to establish communication between mobile robots, control their movements and relay information to other autonomous units. We formulate and study the complexity of a multi-robot relay network positioning problem and propose approximation algorithms that restore visibility based connectivity through the relocation of one or more robots. Third, we develop a framework to quantify the safety score of a fully automated robotic mission where the coexistence of human and robot may pose a collision risk. A number of alternate mission plans are analyzed using motion planning algorithms to select the safest one. Finally, an efficient multi-objective optimization based path planning for the robots is developed to deal with several Pareto optimal cost attributes.

Metody plánování pohybu jsou významnou součástí robotiky, resp. mobilních robotických platforem. Technicky je realizace plánování pohybu z globální úrovně převedena do posloupnosti akcí na úrovni specifické robotické platformy a definovaného prostředí, včetně omezení. V rámci této práce byla provedena recenze mnoha metod určených pro plánování cest, přičemž hlavním těžištěm byly metody založené na tzv. rychle rostoucích stromech (RRT), prostorovém rozkladu (CD) a využití fuzzy expertních systémů (FES). Dosažené výsledky, resp. prezentované algoritmy, využívají dostupné informace z pracovního prostoru mobilního robotu a jsou aplikovatelné na řešení globální pohybové trajektorie mobilních robotů, resp. k řešení specifických problémů plánování cest s omezením typu úzké koridory či překážky s proměnnou polohou v čase. V práci jsou představeny nové plánovací postupy využívající výhod algoritmů RRT a CD. Navržené metody jsou navíc efektivně rozšířeny s využitím fuzzy expertního systému, který zlepšuje jejich chování. Práce rovněž prezentuje řešení pro plánovací problémy typu identifikace úzkých koridorů, či významných oblastí prostoru řešení s využitím přístupů na bázi dekompozice prostoru. V řešeních jsou částečně zahrnuty sub-optimalizace nalezených cest založené na zkracování nalezené cesty a vyhlazování cesty, resp. nahrazení trajektorie hladkou křivkou, respektující lépe předpokládanou dynamiku mobilního zařízení. Všechny prezentované metody byly implementovány v prostředí Matlab, které sloužilo k simulačnímu ověření efektivnosti vlastních i převzatých metod a k návrhu prostoru řešení včetně omezení (překážky). Získané výsledky byly vyhodnoceny s využitím statistických přístupů v prostředí Minitab a Matlab.

This Master's Thesis deals with the controlling of robotic car by Raspberry Pi and the ca- mera. Theoretical part describes individual steps of image processing and probabilistic plan- ning for searching path in the work space. In particular, algorithm RRT (Rapidly-exploring Random Tree) is discussed and the balanced bidirectional RRT is further introduced and used for nonholonomic planning in configuration space. Next chapter speaks about propo- sed solution and there is the accurate description of connection Raspberry Pi to the robotic car. Rest of the work provides look at implemetation details and evaluation. In the end, conclusion was given and some improvements were suggested.

This work deals with the path finding for nonholonomic robots using probabilistic algorithms. The theoretical part analyzes the general problem of finding routes. Subsequently, the work will focus on probabilistic algorithms. The practical part describes design of the applet and web sites that demonstrate probabilistic algorithms to user-specified objects.

One of the main focus of robotics is to integraterobotic tasks and motion planning, which has an increasedsignificance due to their growing number of application fieldsin transportation, navigation, warehouse management and muchmore. A crucial step towards this direction is to have robotsautomatically plan its trajectory to accomplish the given task.In this project a multi-layered approach was implemented toaccomplish it. Our framework consists of a discrete high-levelplanning layer that is designed for planning, and a continuouslow-level search layer that uses a sampling-based method for thetrajectory searching. The layers will interact with each otherduring the search for a solution. In order to coordinate formulti-agent system, velocity tuning is used to avoid collisions, anddifferent priority are assigned to each robot to avoid deadlocks.As a result, the framework trades off completeness for efficiency.The main aim of this project is to study and learn about high-level motion planning and multi-agent system, as an introductionto robotics and computer science. / En viktig aspekt inom robotik är att integrera robotuppgifter med rörelseplanering, som har en ökande be- tydelse för samhället på grund av dess applikationsområde inom t.ex. transport, navigering och lagerhantering. Ett avgörande steg till detta är att få robotarna automatiskt planerar sin bana för att utföra de givna uppgifterna. I detta projekt implementerades “Multi-layered” metod för att uppnå detta. Metoden består av ett hög-nivå diskret planeringslager som är designad för planering, och ett kontinuerligt låg-nivå sökningslager som använder ”sampling-based” algoritmer för sökning av bana. Lagerna interageras med varandra under den tiden där metoden söker efter en önskvärd bana som satisfiera uppdraget. För att koordinera samtliga robotar används den frikopplat approachen där hastigheter för olika robotar justeras till att undvika kollisioner, samt olika prioriteringar tilldelas för varje robot för att undvika ett blockerat låsläge. ”Sampling-based” algoritmer och den frikopplat approachen är oftast mer effektivt tidsmässigt men garantera inte att lösning kommer att hittas även om den existerar. Syftet med detta projekt är att studera och lära sig om rörelseplanering på högt-nivå och multi-agentsystem, som en introduktion till robotik och datavetenskap. / Kandidatexjobb i elektroteknik 2020, KTH, Stockholm

Les techniques de planification de mouvement actuelles sont maintenant capables de résoudre des problèmes mettant en jeu des mécanismes complexes plongés au sein d'environnements encombrés. Néanmoins, l'adaptation de ces planificateurs à des scènes comprenant à la fois des obstacles statiques et des obstacles mobiles s'est avérée limitée jusqu'ici. Une des raisons en est le coût associé à la mise à jour des structures de données qui sont précalculées pour capturer la connexité de l'espace libre. Notre contribution principale concerne la proposition d'un nouveau planificateur capable de traiter des problèmes comprenant à la fois obstacles statiques et obstacles mobiles. Ce planificateur hybride combine deux grandes familles de techniques. D'une part les techniques dites PRM, initialement conçues pour résoudre des problèmes à requêtes multiples et que nous avons étendu à des problèmes de scènes dynamiques. D'autre part, de nouvelles techniques de diffusion, alors que celles-ci sont généralement dédiées aux problèmes simple requête ne nécessitant aucune opération de prétraitement. Les principaux développements accompagnant la construction de ce planificateur sont les suivants : - La proposition d'une architecture originale pour le planificateur dédié aux environnements changeants. Cette architecture inclut notamment plusieurs mécanismes dits d' "évaluation paresseuse" qui permettent de minimiser les test de collision et ainsi d'assurer de bonnes performances. - Le développement d'une nouvelle méthode de diffusion permettant de reconnecter localement certaines portions du réseau invalidées par la présence des obstacles mobiles. Cette méthode, appelée RRT à Domaine Dynamique correspond en fait une extension des planificateur bien connus à bases de RRTs. Un des intérêt propre à notre approche est d'équilibrer automatiquement deux comportements propres au planificateur : l'exploration vers des régions encore inconnues et l'affinage du modèle des régions de l'espac e déjà explorées. - Deux méthodes originales de création de réseaux cycliques qui servent à initialiser notre planificateur. La première assume que les obstacles mobiles sont confinés dans une région donnée, pour construire un réseau adapté aux différents types de changements de position possibles. La seconde est une méthode qui construit des réseaux appelés "réseaux de rétraction". A l'aide d'une structure de donnée de faible taille, cette structure parvient à capturer les différentes variétés de chemins de l'espace, à travers notamment chacune des classes d'homotopie de l'espace libre. Toutes ces méthodes sont implémentées au sein de la plate-forme de travail Move3D développée au LAAS-CNRS et sont évaluées sur différents types de systèmes mécaniques plongés au sein d'environnements 3D.

Small UAVs are used for low altitude surveillance flights where unknown obstacles can be encountered. These UAVs can be given the capability to navigate in uncertain environments if obstacles are identified. This research presents an obstacle avoidance system for small UAVs. First, a mission waypoint path is created that avoids all known obstacles using a genetic algorithm. Then, while the UAV is in flight, obstacles are detected using a forward looking, onboard camera. Image features are found using the Harris Corner Detector and tracked through multiple video frames which provides three dimensional localization of the features. A sparse three dimensional map of features provides a rough estimate of obstacle locations. The features are grouped into potentially hazardous areas. The small UAV then employs a sliding mode control law on the autopilot to avoid obstacles. This research compares rapidly-exploring random trees to genetic algorithms for UAV pre-mission path planning. It also presents two methods for using image feature movement and UAV telemetry to calculate depth and detect obstacles. The first method uses pixel ray intersection and the second calculates depth from image feature movement. Obstacles are avoided with a success rate of 96%.

Unmanned vehicle systems, specifically unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs), have become a popular research topic. This thesis discusses the potential of a UAV-UGV system used to track a human moving through complex urban terrain. This research focuses on path planning problems for both a UAV and a UGV, and presents effective solutions for both problems. In the UAV path planning problem, we desire to plan a path for a miniature fixed-wing UAV to fly through known urban terrain without colliding with any buildings. We present the Waypoint RRT (WRRT) algorithm, which accounts for UAV dynamics while planning a flyable, collision-free waypoint path for a UAV in urban terrain. Results show that this method is fast and robust, and is able to plan paths in difficult urban environments and other terrain maps as well. Simulation and hardware tests demonstrate that these paths are indeed flyable by a UAV. The UGV path planning problem focuses on planning a path to capture a moving target in an urban grid. We discuss using a target motion model based on Markov chains to predict future target locations. We then introduce the Capture and Propagate algorithm, which uses this target motion model to determine the probabilities of capturing the target in various numbers of steps and with various initial UGV moves. By applying some different cost functions, the result of this algorithm is used to choose an optimal first step for the UGV. Results demonstrate that this algorithm is at least as effective as planning a path directly to the current location of the target, and that in many cases, this algorithm performs better. We discuss these cases and verify them with simulation results.