Using occupancy grids for mobile robot perception and navigation

An approach to robot perception and world modeling that uses a probabilistic tesselated representation of spatial information called the occupancy grid is reviewed. The occupancy grid is a multidimensional random field that maintains stochastic estimates of the occupancy state of the cells in a spatial lattice. To construct a sensor-derived map of the robot's world, the cell state estimates are obtained by interpreting the incoming range readings using probabilistic sensor models. Bayesian estimation procedures allow the incremental updating of the occupancy grid, using readings taken from several sensors over multiple points of view. The use of occupancy grids from mapping and for navigation is examined. Operations on occupancy grids and extensions of the occupancy grid framework are briefly considered     

Tracking Multiple Moving Objects for Real-Time Robot Navigation

This paper proposes a method for detecting and tracking the motion of a large number of dynamic objects in crowded environments, such as concourses in railway stations or airports, shopping malls, or convention centers. With this motion information, a mobile vehicle is able to navigate autonomously among moving obstacles, operating at higher speeds and using more informed locomotion strategies that perform better than simple reactive manoeuvering strategies. Unlike many of the methods for motion detection and tracking discussed in the literature, our approach is not based on visual imagery but uses 2D range data obtained using a laser rangefinder. The direct availability of range information contributes to the real-time performance of our approach, which is a primary goal of the project, since the purpose of the vehicle is the transport of humans in crowded areas. Motion detection and tracking of dynamic objects is done by constructing a sequence of temporal lattice maps. These capture the time-varying nature of the environment, and are denoted as time-stamp maps. A time-stamp map is a projection of range information obtained over a short interval of time (a scan) onto a two-dimensional grid, where each cell which coincides with a specific range value is assigned a time stamp. Based on this representation, we devised two algorithms for motion detection and motion tracking. The approach is very efficient, with a complete cycle involving both motion detection and tracking taking 6 ms on a Pentium 166 MHz. The system has been demonstrated on an intelligent wheelchair operating in railway stations and convention centers during rush hour.     

Technical Feasibility and Relative Productivity of Alternate NASA Robotic Missions to a Lunar Dark Crater

Two concepts for large-scale, complex, robotic missions to search for frozen water at the lunar south pole are systematically analyzed to determine their relative productivity and investment requirements. A concurrent design team, a technology-assessment tool, and a sensitivity model are integrated to search a large, complex trade space. Performance goals for a broad portfolio of missions comprising NASA's lunar exploration program are optimized subject to budget, workforce, and other nontechnical constraints. Explicit distinction is made between enabling and enhancing technologies. Uncertainties and dependencies are included within the optimization framework. Given the constraints used in this analysis, the study determines that the longer mission [using a radioisotope thermoelectric generator (RTG)] would return 14 times the value of the shorter mission (using a methanol-oxygen fuel cell) for roughly a 17% increase in cost, and would be enabled with the recommended temporal technology portfolio. To assess the robustness of the investment recommendations, other potential fuel-cell chemistries are evaluated along with potential improvements in rover speed and autonomy, and a reduced activity profile. Results indicate that a lithium-oxygen fuel cell would enable the highest level of productivity among the three fuel cells studied, though not as high as that permitted by an RTG. For the shorter duration mission concepts, it was found that productivity could be enhanced by reducing the number of activities from the baseline 15 to 4, thereby permitting time for each activity to be more fully accomplished.     

Robotic Airships for Exploration of Planetary Bodies with an Atmosphere: Autonomy Challenges

Robotic unmanned aerial vehicles have great potential as surveying and instrument deployment platforms in the exploration of planets and moons with an atmosphere. Among the various types of planetary aerovehicles proposed, lighter-than-atmosphere (LTA) systems are of particular interest because of their extended mission duration and long traverse capabilities. In this paper, we argue that the unique characteristics of robotic airships make them ideal candidates for exploration of planetary bodies with an atmosphere. Robotic airships extend the capabilities of balloons through their flight controllability, allowing (1) precise flight path execution for surveying purposes, (2) long-range as well as close-up ground observations, (3) station-keeping for long-term monitoring of high science value sites, (4) transportation and deployment of scientific instruments and in situ laboratory facilities across vast distances, and (5) opportunistic flight path replanning in response to the detection of relevant sensor signatures. Implementation of these capabilities requires achieving a high degree of vehicle autonomy across a broad spectrum of operational scenarios. The paper outlines some of the core autonomy technologies required to implement the capabilities listed above, drawing on work and results obtained in the context of AURORA (Autonomous Unmanned Remote Monitoring Robotic Airship), a research effort that focuses on the development of the technologies required for substantially autonomous robotic airships. We discuss airship modeling and control, autonomous navigation, and sensor-based flight control. We also outline an approach to airborne perception and monitoring which includes mission-specific target acquisition, discrimination and identification, and present experimental results obtained with AURORA.