Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour

stanford-gates1

This is a 30-minute tour through the 1st floor of Stanford's Gates Computer Science Building. The robot is a Pioneer 2DX with a forward-pointing SICK LMS 200 mounted at or about the robot's center of rotation. The laser was running at high speed (75Hz scans) in the 10 mm, 1 degree mode

Publish/subscribe task allocation for heterogeneous agents

Introduction In this paper, we describe a novel approach to the problem of dynamic task allocation among groups of heterogeneous agents. Specifically, we advocate the use of publish/subscribe messaging, a well-researched ((S ¡ 98)) and commercially proven ((TIB97)) message brokerin

Multi-Robot Task Allocation: Analyzing the Complexity and Optimality of Key Architectures

Important theoretical aspects of multi-robot coordination mechanisms have, to date, been largely ignored. To address part of this negligence, we focus on the problem of multi-robot task allocation. We give a formal, domainindependent, statement of the problem and show it to be an instance of another, well-studied, optimization problem. In this light, we analyze several recently proposed approaches to multi-robot task allocation, describing their fundamental characteristics in such a way that they can be objectively studied, compared, and evaluated

A formal analysis and taxonomy of task allocation in multi-robot systems

Despite more than a decade of experimental work in multi-robot systems, important theoretical aspects of multi-robot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multi-robot task allocation (MRTA). Most work on MRTA has been ad hoc and empirical, with many coordination architectures having been proposed and validated in a proof-of-concept fashion, but infrequently analyzed. With the goal of bringing objective grounding to this important area of research, we present a formal study of MRTA problems. A domain-independent taxonomy of MRTA problems is given, and it is shown how many such problems can be viewed as instances of other, well-studied, optimization problems. We demonstrate how relevant theory from operations research and combinatorial optimization can be used for analysis and greater understanding of existing approaches to task allocation, and to show how the same theory can be used in the synthesis of new approaches