Mobile robot navigation

About: Mobile robot navigation is a research topic. Over the lifetime, 14713 publications have been published within this topic receiving 263092 citations.

Seven principles of efficient human robot interaction

Abstract: Advances in robot technology and artificial intelligence have increased the range of robot applications as well as the importance of supporting human interaction with robots and robot teams. Previous work by the authors has highlighted the importance of creating neglect tolerant autonomy and efficient interfaces. In this paper, lessons learned from evaluating neglect tolerance and interface efficiency are compiled into a set of principles for efficient interaction. Emphasis is placed on designing efficient interfaces, but many of the principles require autonomy levels that support the principles. Each principle is illustrated by an example and motivated by citing relevant factors from cognitive information processing.

Method and apparatus for optical odometry

Abstract: A method and apparatus for optical odometry are disclosed which inexpensively facilitate diverse applications including indoor/outdoor vehicle tracking in secure areas, industrial and home robot navigation, automated steering and navigation of autonomous farm vehicles, shopping cart navigation and tracking, and automotive anti-lock braking systems. In a preferred low-cost embodiment, a telecentric lens is used with an optical computer mouse chip and a microprocessor. In a two-sensor embodiment, both rotation and translation are accurately measured.

Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

Abstract: Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.

Roball, the Rolling Robot

Abstract: Designing a mobile robotic toy is challenging work The robot must be appealing to children and create interesting interactions while facing the wide variety of situations that can be experienced while playing with a child, and all at a reasonable cost In this paper we present Roball, a ball-shaped robot that moves by making its external spherical shell rotate Such design for a mobile robotic toy shows robustness in handling unstructured environments and unconstrained interactions with children Results show that purposeful movements of the robot, its physical structure and locomotion dynamics generate interesting new games influenced by the environment and the child

Deep Reinforcement Learning with Successor Features for Navigation across Similar Environments

Abstract: In this paper we consider the problem of robot navigation in simple maze-like environments where the robot has to rely on its onboard sensors to perform the navigation task. In particular, we are interested in solutions to this problem that do not require localization, mapping or planning. Additionally, we require that our solution can quickly adapt to new situations (e.g., changing navigation goals and environments). To meet these criteria we frame this problem as a sequence of related reinforcement learning tasks. We propose a successor feature based deep reinforcement learning algorithm that can learn to transfer knowledge from previously mastered navigation tasks to new problem instances. Our algorithm substantially decreases the required learning time after the first task instance has been solved, which makes it easily adaptable to changing environments. We validate our method in both simulated and real robot experiments with a Robotino and compare it to a set of baseline methods including classical planning-based navigation.