Multi-Goal Path Planning for Spray Writing with Unmanned Aerial Vehicle

Tato práce se zabývá plánováním přes více cílů pro bezpilotní vzdušné prostředky v úloze psaní textu. Motivací je použití bezpilotní helikoptéry k preciznímu sprejování nápisů například na střechy průmyslových budov. Problém psaní textu bezpilotní helikoptérou formulujeme jako plánování přes více cílů a navrhujeme nový font vhodný pro tuto aplikaci. Helikoptéra poté musí při psaní nápisu letět podél zadaného textu s využitím navrhovaného fontu. Problém hledání cesty podél textu lze formulovat jako zobecnění problému obchodního cestujícího, kde trajektorie spojující jednotlivé segmenty písmen musí respektovat dynamická omezení helikoptéry. Na spojení segmentů písmen je použit model Dubinsova vozítka, který umožňuje průlet nalezené trajektorie konstantní rychlostí bez brzdících manévrů. Navržená metoda plánování byla otestována v realistickém simulátoru a experimenty ukazují její použitelnost pro vícerotorovou helikoptéru v úloze psaní textu.This thesis describes the multi-goal path planning method for an Unmanned Aerial Vehicle (UAV) feasible for the spray writing task. The motivation is to use an autonomous UAV for precise spray writing on, e.g., roofs of industrial buildings. We formulate the writing with the UAV as a multi-goal path planning problem, and therefore, a new font suitable for the multi-goal path planning has been designed. In order to perform writing, the UAV has to travel along the input text characters. The problem can be formulated as the generalized traveling salesman problem, in which trajectories between input text segments respect the UAV constraints. We employed the Dubins vehicle to connect input text segments that allow us to traverse the final trajectory on constant speed without sharp and braking maneuvers. The implemented method has been tested in a realistic simulation environment. The experiments showed that the proposed method is feasible for the considered multirotor UAV

Global path planning and waypoint following for heterogeneous unmanned surface vehicles assisting inland water monitoring

The idea of dispatching multiple unmanned surface vehicles (USVs) to undertake marine missions has ignited a burgeoning enthusiasm on a global scale. Embarking on a quest to facilitate inland water monitoring, this paper presents a systematical approach concerning global path planning and path following for heterogeneous USVs. Specifically, by capturing the heterogeneous nature, an extended multiple travelling salesman problem (EMTSP) model, which seamlessly bridges the gap between various disparate constraints and optimization objectives, is formulated for the first time. Then, a novel Greedy Partheno Genetic Algorithm (GPGA) is devised to consistently address the problem from two aspects: (1) Incorporating the greedy randomized initialization and local exploration strategy, GPGA merits strong global and local searching ability, providing high-quality solutions for EMTSP. (2) A novel mutation strategy which not only inherits all advantages of PGA but also maintains the best individual in the offspring is devised, contributing to the local escaping efficiently. Finally, to track the waypoint permutations generated by GPGA, control input is generated by the nonlinear model predictive controller (NMPC), ensuring the USV corresponds with the reference path and smoothen the motion under constrained dynamics. Simulations and comparisons in various scenarios demonstrated the effectiveness and superiority of the proposed scheme

Planning Algorithms for Multi-Robot Active Perception

A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms