The underlying motive of "follow the water" in the search for evidence of past or present life on Mars has led NASA to deploy increasingly sophisticated robotic missions to the planetary surface. Opportunity and Spirit, the current pair of MER (Mars Exploration Rovers) on Mars for over a year, have both discovered evidence of past surface water. Optimal use of mission resources, such as ground planning time and surface operation duration, for increased science data return becomes critical with each advancement in the capabilities of the onboard instrument suites. This paper presents a novel, end-to-end, fully integrated system being developed at JPL (Jet Propulsion Laboratory) that is called SCAIP (Single Command Approach and Instrument Placement). SCAIP enables a rover to autonomously travel to a designated science target from an extended distance away, and precisely place an instrument on that target with a single command without additional human interaction. The results of some experimental studies with a rover in terrestrial settings and using imagery returned from MER are also described.

The robot construction crew (RCC) is a heterogeneous multi-robot system for autonomous construction of a structure through assembly of long components. The two-robot team demonstrates component placement into an existing structure in a realistic environment. The task requires component acquisition, cooperative transport, and cooperative precision manipulation. A behavior-based architecture provides adaptability. The RCC approach minimizes computation, power, communication, and sensing for applicability to space-related construction efforts, but the techniques are applicable to terrestrial construction tasks.

The U.S. National Vision for Space Exploration calls for a sustained and affordable human and robotic program to explore the solar system, starting with a human return to the Moon by 2020. Key to this vision is the development of robotic systems for site preparation, habitat construction, deploying infrastructure, and repair in space and on Lunar and planetary surfaces. This paper discusses a system of systems approach to the development of these capabilities, and includes some preliminary experimental studies of multi-robot surface construction operations with the JPL robotic construction crew (RCC).

Move Value Estimation for Robot Teams (MVERT) is a robot action selection algorithm for teams performing multiple competing tasks. The goal of MVERT is to select actions for robot team members to maximize the team's joint utility toward overall mission progress in a computationally efficient manner. MVERT is fully distributed, with each robot using information about other teammates to select its action with the greatest value. MVERT selects actions for a robot team to perform multi-task exploration and dynamic target observation. Successful action selection is demonstrated in simulation for exploration and in simulation and on robots for dynamic target observation.

We show how to recover 2D structure and motion linearly in order to initialize simultaneous mapping and localization (SLAM) for bearings-only measurements and planar motion. The method supplies a good initial estimate of the geometry, even without odometry or in multiple robot scenarios. Hence, it substantially enlarges the scope in which non-linear batch-type SLAM algorithms can be applied. The method is applicable when at least seven landmarks are seen from three different vantage points, whether by one robot that moves over time or by multiple robots that observe a set of common landmarks.

We present an efficient probabilistic method for localization using landmarks that supports individual robot and multi-robot collaborative localization. The approach, based on the Kalman-Bucy filter, reduces computation by treating different types of landmark measurements (for example, range and bearing) separately. Our algorithm has been extended to perform two types of collaborative localization for robot teams. Results illustrating the utility of the approach in simulation and on a real robot are presented.

We present a method for representing, communicating and fusing distributed, noisy and uncertain observations of an object by multiple robots. The approach relies on re-parameterization of the canonical two-dimensional Gaussian distribution that corresponds more naturally to the observation space of a robot. The approach enables two or more observers to achieve greater effective sensor coverage of the environment and improved accuracy in object position estimation. We demonstrate empirically that, when using our approach, more observers achieve more accurate estimations of an object's position. The method is tested in three application areas, including object location, object tracking, and ball position estimation for robotic soccer. Quantitative evaluations of the technique in use on mobile robots are provided.