Enabling Topological Planning with Monocular Vision

Topological strategies for navigation meaningfully reduce the space of possible actions available to a robot, allowing use of heuristic priors or learning to enable computationally efficient, intelligent planning. The challenges in estimating structure with monocular SLAM in low texture or highly cluttered environments have precluded its use for topological planning in the past. We propose a robust sparse map representation that can be built with monocular vision and overcomes these shortcomings. Using a learned sensor, we estimate high-level structure of an environment from streaming images by detecting sparse vertices (e.g., boundaries of walls) and reasoning about the structure between them. We also estimate the known free space in our map, a necessary feature for planning through previously unknown environments. We show that our mapping technique can be used on real data and is sufficient for planning and exploration in simulated multi-agent search and learned subgoal planning applications.Comment: 7 pages (6 for content + 1 for references), 5 figures. Accepted to the 2020 IEEE International Conference on Robotics and Automatio

Topological mapping for limited sensing mobile robots using the Probabilistic Gap Navigation Tree

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 75-78).This thesis proposes a novel structure for robotic navigation with minimal sensing abilities called the Probabilistic Gap Navigation Tree (PGNT). In this navigation approach, we create a topological map of the environment based on a previously created Gap Navigation Tree (GNT) [40]. The "gap" in the gap navigation algorithm represents a discontinuity in the robotic field of vision. The robot is able to use the gaps to represent its world as a tree structure (GNT), in which each vertex corresponds to a gap. Ideally, the robot navigates in the world by following the tree branches to its desired goal. However, due to the sensor uncertainty, the robot may detect discontinuities when there are none present, and vice versa. The Probabilistic Gap Navigation Tree compensates for the measurement noise by sampling from a distribution of the gap navigation trees to obtain the most likely tree given the sensor measurements, similar to the particle filtering algorithm used in Monte Carlo localization. Therefore, the PGNT allows navigation in an unknown environment using a realistic range finder, as opposed to the ideal sensor model assumed previously. We demonstrate the ability to build a PGNT in a simulated environment.by Valerie Gordeski.M.Eng

Mobile robots and vehicles motion systems: a unifying framework

Robots perform many different activities in order to accomplish their tasks. The robot motion capability is one of the most important ones for an autonomous be- havior in a typical indoor-outdoor mission (without it other tasks can not be done), since it drastically determines the global success of a robotic mission. In this thesis, we focus on the main methods for mobile robot and vehicle motion systems and we build a common framework, where similar components can be interchanged or even used together in order to increase the whole system performance

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics

Learning cognitive maps: Finding useful structure in an uncertain world

In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

Spatial subgoal learning in the mouse: behavioral and computational mechanisms

Here we aim to better understand how animals navigate structured environments. The prevailing wisdom is that they can select among two distinct approaches: querying a mental map of the environment or repeating previously successful trajectories to a goal. However, this dichotomy has been built around data from rodents trained to solve mazes, and it is unclear how it applies to more naturalistic scenarios such as self-motivated navigation in open environments with obstacles. In this project, we leveraged instinctive escape behavior in mice to investigate how rodents use a period of exploration to learn about goals and obstacles in an unfamiliar environment. In our most basic assay, mice explore an environment with a shelter and an obstacle for 5-20 minutes and then we present threat stimuli to trigger escapes to shelter. After 5-10 minutes of exploration, mice took inefficient paths to the shelter, often nearly running into the obstacle and then relying on visual and tactile cues to avoid it. Within twenty minutes, however, they spontaneously developed an efficient subgoal strategy, escaping directly to the obstacle edge before heading to the shelter. Mice escaped in this manner even if the obstacle was removed, suggesting that they had memorized a mental map of subgoals. Unlike typical models of map-based planning, however, we found that investigating the obstacle was not important for updating the map. Instead, learning resembled trajectory repetition: mice had to execute `practice runs' toward an obstacle edge in order to memorize subgoals. To test this hypothesis directly, we developed a closed-loop neural manipulation, interrupting spontaneous practice runs by stimulating premotor cortex. This manipulation successfully prevented subgoal learning, whereas several control manipulations did not. We modelled these results using a panel of reinforcement learning approaches and found that mice behavior is best matched by systems that explore in a non-uniform manner and possess a high-level spatial representation of regions in the arena. We conclude that mice use practice runs to learn useful subgoals and integrate them into a hierarchical cognitive map of their surroundings. These results broaden our understanding of the cognitive toolkit that mammals use to acquire spatial knowledge