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Situation information and sensor information are differentiated and a method for computing the situation information expected value (SIEV) is presented for use in Information Based Sensor Management (IBSM). Nine case pairs are evaluated in which the sensor capabilities vary among poor, average, and good sensors, and the goal lattice values vary among attack, defend, and stealth modes showing that...
This study introduces a machine hearing system for robot audition, which enables a robotic agent to pro-actively minimize the uncertainty of sound source location estimates through motion. The proposed system is based on an active exploration approach, providing a means to model and predict effects of the agent's future motions on localization uncertainty in a probabilistic manner. Particle filtering...
In this paper, a new task-oriented active-sensing method is presented. Most active sensing methods choose sensing actions that minimize the uncertainty of the state according to some information-theoretic measure. While this is reasonable for most applications, minimizing state uncertainty may not be most relevant when the state information is used to perform a task. This is because the uncertainty...
One of the major challenges in multi-robot exploration is to fuse the partial maps generated by individual robots into a consistent global map. We address 3D volumetric map fusion by extending the well known iterative closest point(ICP) algorithm to include probabilistic distance and surface information. In addition, the relative transformation is evaluated based on Mahalanobis distance and map dissimilarities...
This letter presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetually changing world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-long learning process. We address the problem by application of information-theoretic exploration methods...
The cardinality balanced multi-target multi- Bernoulli (CB-MeMBer) filter has received a lot of attention as a good approximation to the ideal multi-target filter. Consequently, several schemes have been proposed in order to perform sensor management when a CB-MeMBer filter is used. Most of these criteria are computationally very expensive due to the nature of the CB-MeMBer filter and the integration...
We introduce a particle filter-based approach to representing and actively reducing uncertainty over articulated motion models. The presented method provides a probabilistic model that integrates visual observations with feedback from manipulation actions to best characterize a distribution of possible articulation models. We evaluate several action selection methods to efficiently reduce the uncertainty...
Instrumentation of an environment with sensors can provide an effective and scalable localization solution for robots. Where GPS is not available, beacons that provide position estimates to a robot must be placed effectively in order to maximize a robots navigation accuracy and robustness. Sonar range-based beacons are reasonable candidates for low cost position estimate sensors. In this paper we...
We propose a method for the computation of entropy decrease in C-space. These estimates are then used to evaluate candidate exploratory trajectories in the context of autonomous mobile robot mapping. The method evaluates both map and path entropy reduction and uses such estimates to compute trajectories that maximize coverage whilst minimizing localization uncertainty, hence reducing map error. Very...
Occupancy grids have been a popular mapping technique in mobile robotics for nearly 30 years. Occupancy grids offer a discrete representation of the world and seek to determine the occupancy probability of each cell. Traditional occupancy grid mapping methods make two assumptions for computational efficiency and it has been shown that the full posterior is computationally intractable for real-world...
A frequent method for taking into account the partially observable nature of an environment in which robots interact lies in formulating the problem domain as a Partially Observable Markov Decision Process (POMDP). By having humans demonstrate how to act in this partially observable context we can leverage their prior knowledge, experience and intuition, which is difficult to encode directly in a...
This paper presents a novel semantic categorization method for 3D point cloud data using supervised, multiclass Gaussian Process (GP) classification. In contrast to other approaches, and particularly Support Vector Machines, which probably are the most used method for this task to date, GPs have the major advantage of providing informative uncertainty estimates about the resulting class labels. As...
We treat information gathering as a POMDP in which the goal is to maximize an accumulated intrinsic reward at each time step based on the negative entropy of the agent's beliefs about the world state. We show that such information foraging agents can discover intelligent exploration policies that take into account the long-term effects of sensor and motor actions, and can automatically adapt to variations...
This paper presents a novel approach to data fusion for stochastic processes that model spatial data. It addresses the problem of data fusion in the context of large scale terrain modeling for a mobile robot. Building a model of large scale and complex terrain that can adequately handle uncertainty and incompleteness in a statistically sound manner is a very challenging problem. To obtain a comprehensive...
This work deals with a group of mobile sensors sampling a spatiotemporal random field whose mean is unknown and covariance is known up to a scaling parameter. The Bayesian posterior predictive entropy provides a direct mapping between the locations of a new set of point measurements and the uncertainty of the resulting estimate of the model parameters. Since the posterior predictive entropy and its...
To meet the necessity of handling environmental uncertainties of mobile robots, we proposed an efficient exploration strategy to gather information, called entropy sweeper. To do so, we utilized the entropy distribution and the utility function to determine which positions have more uncertainties. Proposed strategy is divided into two phases: the learning phase and the action phase. In general, uncertainties...
Cameras are popular sensors for robot navigation tasks such as localization as they are inexpensive, lightweight, and provide rich data. However, fast movements of a mobile robot typically reduce the performance of vision-based localization systems due to motion blur. In this paper, we present a reinforcement learning approach to choose appropriate velocity profiles for vision-based navigation. The...
This paper presents two new approaches that enable the use of linear landmarks for planning paths with uncertainty in position in outdoor environments. The first approach uses a combination of forward simulation and entropy to reduce the dimensionality of the search space, while still preserving most of the information required to propagate a full covariance matrix. The second approach adds incremental...
In active perception systems for scene recognition the utility of an observation is determined by the information gain in the probability distribution over the state space. The goal is to find a sequence of actions which maximizes the system knowledge at low resource costs. Most current approaches focus either on optimizing the determination of the payoff neglecting the costs or develop sophisticated...
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