A tesselated probabilistic representation for spatial robot perception and navigation

The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations

Planning Flight Paths of Autonomous Aerobots

Algorithms for planning flight paths of autonomous aerobots (robotic blimps) to be deployed in scientific exploration of remote planets are undergoing development. These algorithms are also adaptable to terrestrial applications involving robotic submarines as well as aerobots and other autonomous aircraft used to acquire scientific data or to perform surveying or monitoring functions

Graph-Based Path-Planning for Titan Balloons

A document describes a graph-based path-planning algorithm for balloons with vertical control authority and little or no horizontal control authority. The balloons are designed to explore celestial bodies with atmospheres, such as Titan, a moon of Saturn. The algorithm discussed enables the balloon to achieve horizontal motion using the local horizontal winds. The approach is novel because it enables the balloons to use arbitrary wind field models. This is in contrast to prior approaches that used highly simplified wind field models, such as linear, or binary, winds. This new approach works by discretizing the space in which the balloon operates, and representing the possible states of the balloon as a graph whose arcs represent the time taken to move from one node to another. The approach works with arbitrary wind fields, by looking up the wind strength and direction at every node in the graph from an arbitrary wind model. Having generated the graph, search techniques such as Dijkstra s algorithm are then used to find the set of vertical actuation commands that takes the balloon from the start to the goal in minimum time. In addition, the set of reachable locations on the moon or planet can be determined

Parameterized Linear Longitudinal Airship Model

A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets. Heretofore, the development of linearized models of the longitudinal dynamics of airships has been costly in that it has been necessary to perform extensive flight testing and to use system-identification techniques to construct models that fit the flight-test data. The present model is a generic one that can be relatively easily specialized to approximate the dynamics of specific airships at specific operating points, without need for further system identification, and with significantly less flight testing. The approach taken in the present development is to merge the linearized dynamical equations of an airship with techniques for estimation of aircraft stability derivatives, and to thereby make it possible to construct a linearized dynamical model of the longitudinal dynamics of a specific airship from geometric and aerodynamic data pertaining to that airship. (It is also planned to develop a model of the lateral dynamics by use of the same methods.) All of the aerodynamic data needed to construct the model of a specific airship can be obtained from wind-tunnel testing and computational fluid dynamic

Linearization in Motion Planning under Uncertainty

Motion planning under uncertainty is essential to autonomous robots. Over the past decade, the scalability of such planners have advanced substantially. Despite these advances, the problem remains difficult for systems with non-linear dynamics. Most successful methods for planning perform forward search that relies heavily on a large number of simulation runs. Each simulation run generally requires more costly integration for systems with non-linear dynamics. Therefore, for such problems, the entire planning process remains relatively slow. Not surprisingly, linearization-based methods for planning under uncertainty have been proposed. However, it is not clear how linearization affects the quality of the generated motion strategy, and more importantly where to and where not to use such a simplification. This paper presents our preliminary work towards answering such questions. In particular, we propose a measure, called Statistical-distance-based Non-linearity Measure (SNM), to identify where linearization can and where it should not be performed. The measure is based on the distance between the distributions that represent the original motion-sensing models and their linearized version. We show that when the planning problem is framed as the Partially Observable Markov Decision Process (POMDP), the difference between the value of the optimal strategy generated if we plan using the original model and if we plan using the linearized model, can be upper bounded by a function linear in SNM. We test the applicability of this measure in simulation via two venues. First, we compare SNM with a negentropy-based Measure of Non-Gaussianity (MoNG) —a measure that has recently been shown to be a suitable measure of non-linearity for stochastic systems [1]. We compare their performance in measuring the difference between a general POMDP solver [2] that computes motion strategies using the original model and a solver that uses the linearized model (adapted from [3]) on various scenarios. Our results indicate that SNM is more suitable in taking into account the effect that obstacles have on the effectiveness of linearization. In the second set of tests, we use a local estimate of SNM to develop a simple on-line planner that switches between using the original and the linearized model. Simulation results on a car-like robot with second order dynamics and a 4-DOFs and 6-DOFs manipulator with torque control indicate that our simple planner appropriately decides if and when linearization should be use

Autonomous surveillance for biosecurity

The global movement of people and goods has increased the risk of biosecurity threats and their potential to incur large economic, social, and environmental costs. Conventional manual biosecurity surveillance methods are limited by their scalability in space and time. This article focuses on autonomous surveillance systems, comprising sensor networks, robots, and intelligent algorithms, and their applicability to biosecurity threats. We discuss the spatial and temporal attributes of autonomous surveillance technologies and map them to three broad categories of biosecurity threat: (i) vector-borne diseases; (ii) plant pests; and (iii) aquatic pests. Our discussion reveals a broad range of opportunities to serve biosecurity needs through autonomous surveillance.Comment: 26 pages, Trends in Biotechnology, 3 March 2015, ISSN 0167-7799, http://dx.doi.org/10.1016/j.tibtech.2015.01.003. (http://www.sciencedirect.com/science/article/pii/S0167779915000190

Coverage-based next best view selection

Generating 3D scans of environments and objects is an invaluable tool, which can be used in many industries. A small handheld device known as HeatWave can create a 3D environment map containing visible and thermal information. This paper discusses a system which uses a HeatWave scanner together with a robot mobile manipulator, allowing these scans to be collected without user operation. An algorithm for this system is also presented, obtaining the positions that the robot mobile manipulator must move to, to find the best view of the given object. A prior model is built up with no knowledge of the scene, and is then improved with a next best view algorithm. Results are presented from experiments undertaken in visually different scenes