This thesis presents a methodology, architecture, hardware implementation, and results of a system capable of controlling and guiding a hovering vehicle in unknown environments, emphasizing cluttered indoor spaces. Six-axis inertial data and a lowresolution onboard camera yield sufficient

. If GNSS is not available, the previously and/or newly generated map is now used to estimate the INS errors. Simulation results will be presented which shows that the system can provide reliable and accurate navigation solutions in GNSS denied environments for an extended period of time.

We consider exploration problems where a robot has to construct a complete map of an unknown environment. We assume that the environment is modeled by a directed, strongly connected graph. The robot's task is to visit all nodes and edges of the graph using the minimum number R of edge

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot

Operating in an unknown environment is inherent to autonomous systems. This paper addresses two problems concerning efficient navigation in unknown environments.

We study exploration problems where a robot has to construct a complete map of an unknown environment using a path that is as short as possible. In the first problem setting we consider, a robot has to explore n rectangles. We show that no deterministic or randomized online algorithm can be better

Summary. We examine how the choice of the movement algorithm can affect the success of a swarm of simple mobile robots attempting to disperse themselves in an unknown environment. We assume there is no central control, and the robots have limited processing power, simple sensors, and no active

This paper provides an overview of the issues that arise when designing a system that is capable of autonomous navigation in an unknown environment. In addition to presenting the necessary components for such a system and ways to model the environment, dynamic path planning algorithms are treated

We introduce a new learning problem: learning a graph by piecemeal search, in which the learner must return every so often to its starting point (for refueling, say). We present two linear-time piecemeal-search algorithms for learning city-block graphs: grid graphs with rectangular obstacles.

Navigation in mobile robotics involves two tasks, keeping track of the robot’s position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings