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Exploring an unknown space and building maps is a fundamental capability for mobile robots. For fully autonomous systems, the robot would further need to actively plan its paths during exploration. The problem of designing robot trajectories to actively explore an unknown environment and minimize the map error is referred to as active simultaneous localization and mapping (active SLAM). Existing work...
This paper assesses the predictability of individual vehicle's mobility in Beijing expressway system using Electronic Toll Collection (ETC) data records. By examining the uncertainties of movements using entropy, considering both the frequencies and sequential correlations of vehicles' trajectories, we draw to the conclusion that the average limit of predictability of expressway vehicles mobility...
We are interested in inferring the set of waypoints (or intermediate destinations) of a mobility trajectory in the absence of timing information. We find that, by mining a dataset of real mobility traces, computing the entropy of conditional Markov trajectory enables us to uncover waypoints, even though no timing information nor absolute geographic location is provided. We build on this observation...
Cross-situational learning, the ability to learn word meanings across multiple scenes consisting of multiple words and referents, is thought to be an important tool for language acquisition. The ability has been studied in infants, children, and adults, and yet there is much debate about the basic storage and retrieval mechanisms that operate during cross-situational word learning. It has been difficult...
Physical exploration refers to the challenge of autonomously discovering and learning how to manipulate the environment's degrees of freedom (DOF)—by identifying promising points of interaction and pushing or pulling object parts to reveal DOF and their properties. Recent existing work focused on sub-problems like estimating DOF parameters from given data. Here, we address the integrated problem,...
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...
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...
Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however,...
We present an active exploration strategy that complements Pose SLAM [1] and optimal navigation in Pose SLAM [2]. The method evaluates the utility of exploratory and place revisiting sequences and chooses the one that minimizes overall map and path entropies. The technique considers trajectories of similar path length taking marginal pose uncertainties into account. An advantage of the proposed strategy...
This paper is a contribution to the prognosis of complex systems based expert's knowledge. The aim, is to propose a new probabilistic approach, which allows the exploitation of the expert's knowledge to estimate the damage trajectory for a considered process.
Planning under uncertainty in perception and action requires the robot to be able to use active strategies for trading-off between the contrasting tasks of exploring the scenario and satisfying given constraints on the admissible uncertainty in the estimation process. In this work we compare several state-of-the-art approaches to active SLAM (Simultaneous Localization and Mapping) and exploration...
Autonomous exploration under uncertain robot position requires the robot to plan a suitable motion policy in order to visit unknown areas while minimizing the uncertainty on its pose. The corresponding problem, namely active SLAM (Simultaneous Localization and Mapping) and exploration has received a large attention from the robotic community for its relevance in mobile robotics applications. In this...
An Autonomous Underwater Vehicle (AUV) needs to demonstrate a number of capabilities, in order to carry on autonomous missions with success. One of the key areas is autonomous localisation, i.e. the capability of the AUV to estimate correctly its position and orientation in the environment. However, most of the proposed approaches are “passive”, with no robot motion control involved. The “active”...
The paper considers learning systems as optimisation systems with dynamical information constraints, and general optimality conditions are derived using the duality between the space of utility functions and probability measures. The increasing dynamics of the constraints is used to parametrise the optimal solutions which form a trajectory in the space of probability measures. Stochastic processes...
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