Environmental monitoring of spatially-distributed geo-physical processes (e.g., temperature, pressure, or humidity) requires efficient sampling schemes, particularly, when employing an autonomous mobile agent to execute the sampling task. Many approaches have considered optimal sampling strategies which specialize in minimizing estimation error, while others emphasize reducing resource usage, yet rarely are both of these performance parameters used concurrently to influence the navigation. This work discusses how a spatial estimation process and resource awareness are integrated to generate an informed navigation policy for collecting useful measurement information. We also enable a direct comparison between this informed navigation method and more common approaches using two performance metrics. We show that our informed navigation outperforms these approaches based on performance evaluation as a function of estimation error and resource usage for a useful range of coverage within the sampling area.

With the advent of new technologies, robotic surveying systems are being developed to facilitate the collection of ground-based information to validate and complement data collected by traditional and satellite-based instruments. The development of such systems necessitates an accurate set of reference data. Given the limitations of current in situ measurement methods to aid remote sensing, this paper outlines a method for the creation of three-dimensional data from the most common public data source, 2D contour maps. Using image processing and interpolation techniques, this method was first tested against data collected by a robotic survey system and against methods that a human expert would use. Comparatively, our method yielded vertical RMSE in the range of (0.006066 - 0.39) [m] for different horizontal spatial resolutions. Twenty additional sample contour maps were identified to further vet our method against that of a human expert as a function of the 3D interpolation method selected. These tests provided errors in the centimeter range and also revealed that the linear triangular mesh interpolator is the best choice for this type of image input data.

Efficiently measuring environmental phenomena (e.g., elevation, chemical composition, and mineral density) is a task typically reserved for the geoscience community. Recent robotic systems with the potential for addressing the task of sampling currently exist, yet their navigation strategies (and subsequently sampling strategies) are seldom a function of the spatial change in the measured phenomena of interest. Solutions are especially void for intelligent systems to which resource constraints are applied (i.e., battery power and experimentation time) while complete coverage of an area is expected. In this paper, we discuss the implementation of a custom navigation strategy based on immediately-sensed data that, when combined with spatial interpolation techniques, yields a re-creation of the surveyed space with root mean squared error that meets accepted mapping standards. Our methodology employs an adaptive coverage algorithm which succeeds in lowering the RMS error when compared to other navigation techniques. Our results are validated in simulation by considering: 1) randomly-generated terrains and 2) realistic digital elevation map (DEM) data transposed from publically available terrain contour maps.

This paper describes our successful implementation of a robot that autonomously and strategically removes multiple blocks from an unstable Jenga tower. We present an integrated strategy for perception, planning and control that achieves repeatable performance in this challenging physical domain. In contrast to previous implementations, we rely only on low-cost, readily available system components and use strategic algorithms to resolve system uncertainty. We present a three-stage planner for block extraction which considers block selection, extraction order, and physics-based simulation that evaluates removability. Existing vision techniques are combined in a novel sequence for the identification and tracking of blocks within the tower. Discussion of our approach is presented following experimental results on a 5-DOF robot manipulator.

In ground-based military maneuvers, group formations require flexibility when traversing from one point to the next. For a human-led team of semi-autonomous agents, a certain level of awareness demonstrated by the agents regarding the quality of the formation is preferable. Through the use of a Multi-Robot System (MRS), this work combines leader-follower principles augmented by an assistive formation maintenance (AFM) method to improve formation keeping and demonstrate a formation-in-motion concept. This is achieved using the Robot Mean Task Allocation method (RTMA), a strategy used to allocate formation positions to each unit within a continuously mobile MRS. The end goal is to provide a military application that allows a soldier to efficiently tele-operate a semi-autonomous MRS capable of holding formation amidst a cluttered environment. Baseline simulation is performed in Player/Stage to show the applicability of our developed model and its potential for expansive research.

In this paper, we present a hierarchical methodology that learns new walking gaits autonomously while operating in an uncharted environment, such as on the Mars planetary surface or in the remote Antarctica environment. The focus is to maintain persistent forward locomotion along the body axis, while navigating in natural terrain environments. The hierarchical strategy consists of a finite state machine that models the state of leg orientations coupled with a modified evolutionary algorithm to learn necessary leg movement sequences. Locomotion behavior is assessed by monitoring the robot's progress toward a specified goal location. Details of the methodology are discussed, and experimental results with a six-legged robot are presented.