SMUG Planner: A Safe Multi-Goal Planner for Mobile Robots in Challenging Environments

Robotic exploration or monitoring missions require mobile robots to autonomously and safely navigate between multiple target locations in potentially challenging environments. Currently, this type of multi-goal mission often relies on humans designing a set of actions for the robot to follow in the form of a path or waypoints. In this work, we consider the multi-goal problem of visiting a set of pre-defined targets, each of which could be visited from multiple potential locations. To increase autonomy in these missions, we propose a safe multi-goal (SMUG) planner that generates an optimal motion path to visit those targets. To increase safety and efficiency, we propose a hierarchical state validity checking scheme, which leverages robot-specific traversability learned in simulation. We use LazyPRM* with an informed sampler to accelerate collision-free path generation. Our iterative dynamic programming algorithm enables the planner to generate a path visiting more than ten targets within seconds. Moreover, the proposed hierarchical state validity checking scheme reduces the planning time by 30% compared to pure volumetric collision checking and increases safety by avoiding high-risk regions. We deploy the SMUG planner on the quadruped robot ANYmal and show its capability to guide the robot in multi-goal missions fully autonomously on rough terrain

Spatial coverage in routing and path planning problems

Routing and path planning problems that involve spatial coverage have received increasing attention in recent years in different application areas. Spatial coverage refers to the possibility of considering nodes that are not directly served by a vehicle as visited for the purpose of the objective function or constraints. Despite similarities between the underlying problems, solution approaches have been developed in different disciplines independently, leading to different terminologies and solution techniques. This paper proposes a unified view of the approaches: Based on a formal introduction of the concept of spatial coverage in vehicle routing, it presents a classification scheme for core problem features and summarizes problem variants and solution concepts developed in the domains of operations research and robotics. The connections between these related problem classes offer insights into common underlying structures and open possibilities for developing new applications and algorithms

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

The Vehicle Routing Problem with Service Level Constraints

We consider a vehicle routing problem which seeks to minimize cost subject to service level constraints on several groups of deliveries. This problem captures some essential challenges faced by a logistics provider which operates transportation services for a limited number of partners and should respect contractual obligations on service levels. The problem also generalizes several important classes of vehicle routing problems with profits. To solve it, we propose a compact mathematical formulation, a branch-and-price algorithm, and a hybrid genetic algorithm with population management, which relies on problem-tailored solution representation, crossover and local search operators, as well as an adaptive penalization mechanism establishing a good balance between service levels and costs. Our computational experiments show that the proposed heuristic returns very high-quality solutions for this difficult problem, matches all optimal solutions found for small and medium-scale benchmark instances, and improves upon existing algorithms for two important special cases: the vehicle routing problem with private fleet and common carrier, and the capacitated profitable tour problem. The branch-and-price algorithm also produces new optimal solutions for all three problems