2391025
Preliminary results in range-only localization and mapping
This paper presents methods of localization using cooperating landmarks (beacons) that provide the ability to measure range only. Recent advances in radio frequency technology make it possible to measure range between inexpensive beacons and a transponder Such a method has tremendous benefit since line of sight is not required between the beacons and the transponder and because the data association problem can be completely avoided. If the positions of the beacons are known, measurements from multiple beacons can be combined using probability grids to provide an accurate estimate of robot location. This estimate can be improved by using Monte Carlo techniques and Kalman filters to incorporate odometry data. Similar methods can be used to solve the simultaneous localization and mapping problem (SLAM) when beacon locations are uncertain. Experimental results are presented for robot localization. Tracking and SLAM algorithms are demonstrated in simulation.
668272
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
While many multi-robot systems rely on fortuitous cooperation between agents, some tasks, such as the assembly of large structures, require tighter coordination. We present a general software architecture for coordinating heterogeneous robots that allows for both autonomy of the individual agents as well as explicit coordination. This paper presents recent results with three robots with very different configurations. Working as a team, these robots are able to perform a high-precision docking task that none could achieve individually.
62491573
A Robotic Excavator for Autonomous Truck Loading
Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavator's software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.
1823400
Models for Automated Earthmoving
We present a composite forward model of the mechanics of an excavator backhoe digging in soil. This model is used to predict the trajectories developed by a closed-loop force based control scheme given initial conditions, some of which can be controlled (excavator control parameters), some directly measured (shape of the terrain), and some estimated (soil properties). Since soil conditions can vary significantly, it is necessary that soil properties be estimated online. Our models are used to both estimate soil properties and predict contact forces between the excavator and the terrain. In a large set of experiments we have conducted, we find that these models are accurate to within approximately 20% and run about 10 times faster than real-time. In this paper we motivate the development of these models and discuss experimental data from our testbed.
8708828
Optimal motion estimation from visual and inertial measurements
Cameras and inertial sensors are good candidates to be deployed together for autonomous vehicle motion estimation, since each can be used to resolve the ambiguities in the estimated motion that results from using the other modality alone. We present an algorithm that computes optimal vehicle motion estimates by considering all of the measurements from a camera, rate gyro, and accelerometer simultaneously. Such optimal estimates are useful in their own right, and as a gold standard for the comparison of online algorithms. By comparing the motions estimated using visual and inertial measurements, visual measurements only, and inertial measurements only against ground truth, we show that using image and inertial data together can produce highly accurate estimates even when the results produced by each modality alone are very poor Our test datasets include both conventional and omnidirectional image sequences, and an image sequence with a high percentage of missing data.
570980
An efficient on-line path planner for outdoor mobile robots
Abstract Mobile robots operating in outdoor unstructured environments often have only incomplete maps and must deal with new objects found during traversal. Path planning in these environments must be incremental to accommodate new information and must use efficient representations. This article reports recent results in path planning using an efficient data structure (framed-quadtrees) and an optimal algorithm (D*) to incrementally replan optimal paths. We show how the use of framed-quadtrees leads to paths that are shorter and more direct than when other representations are used. We also show the difference in performance when the robot starts with no information about the world versus when it starts with partial information about the world. Our results indicate that, as would be expected, starting with partial information is better than starting with no information. However, in many cases, partial information results in performance that is almost as good as starting out with complete information about the world, while the computational cost incurred is significantly lower. Our system has been tested in simulation as well on an autonomous jeep equipped with local obstacle avoidance capabilities. Results from both simulation and real experimentation are discussed.
9182507
Improving Policies without Measuring Merits
Performing policy iteration in dynamic programming should only require knowledge of relative rather than absolute measures of the utility of actions (Werbos, 1991) - what Baird (1993) calls the advantages of actions at states. Nevertheless, most existing methods in dynamic programming (including Baird's) compute some form of absolute utility function. For smooth problems, advantages satisfy two differential consistency conditions (including the requirement that they be free of curl), and we show that enforcing these can lead to appropriate policy improvement solely in terms of advantages.
52828064
Smart-Aloha for Multi-Hop Wireless Networks

This paper presents a novel slotted ALOHA-based protocol for use in ad hoc networks where nodes are equipped with adaptive array smart antennas. The protocol relies on the ability of the antenna and DoA (Direction of Arrival) algorithms to identify the direction of transmitters and then beamform appropriately to maximize SINR (Signal to Interference and Noise Ratio) at the receiver. The performance of the protocol is evaluated using analytical modeling as well as detailed simulation in OPNET and Matlab where we demonstrate the benefits of using smart antennas. The impact of using different number of antenna elements is also studied for this environment.
5635288
Indexing Uncertain Categorical Data
Uncertainty in categorical data is commonplace in many applications, including data cleaning, database integration, and biological annotation. In such domains, the correct value of an attribute is often unknown, but may be selected from a reasonable number of alternatives. Current database management systems do not provide a convenient means for representing or manipulating this type of uncertainty. In this paper we extend traditional systems to explicitly handle uncertainty in data values. We propose two index structures for efficiently searching uncertain categorical data, one based on the R-tree and another based on an inverted index structure. Using these structures, we provide a detailed description of the probabilistic equality queries they support. Experimental results using real and synthetic datasets demonstrate how these index structures can effectively improve the performance of queries through the use of internal probabilistic information.
206599449
Downloading vs Purchase: Music Industry vs Consumers
The music industry argues that unauthorised downloading of music is detrimentally affecting the industry; a breach of copyright that needs to be stopped. The industry has taken many actions to stamp out unauthorised music downloading, including prosecution of peer-to-peer software users for breach of copyright and against peer-to-peer software suppliers for contributory copyright infringement. Industry commentators have questioned this line of reasoning particularly as there is now significant revenue from legal music downloads in the United States and Europe. This paper draws on a qualitative study of music consumers in Australia to show there is not a clear dichotomy between down- loading and purchase. It is more of a continuum. From the users' perspective, downloading is transformed to the activity of accessing and exploring music. The industry could more fruitfully focus on satisfying this basic aspect of the listening experience in DRM systems in order to work together with their customers.
6543198
Reinforcement Learning for Spoken Dialogue Systems
Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application of MDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.