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This paper introduces novel graph exploration based on reasoning procedure. The proposed approach does not need any markers and only a similarity measures of the places and routes are used for decisions and loop closing. The edge selection process is driven by information gain, which is computed for each edge based on the probability of the loop closure and consecutive merges performed if the loop...
We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts. Our key insight is we can formalize the...
Predictive State Representations (PSRs) allow modeling of dynamical systems directly in observables and without relying on latent variable representations. A problem that arises from learning PSRs is that it is often hard to attribute semantic meaning to the learned representation. This makes generalization and planning in PSRs challenging. In this paper, we extend PSRs and introduce the notion of...
To analyze auditory scenes of robots' surrounding environments, not only speeches but also non-speech sounds are important, which are spatially distributed and have different spectral and temporal characteristics. Thus, this paper investigates Acoustic Event Identification (AEI) which includes problems of localization, detection, and identification of sound sources. To achieve AEI by a robot in a...
Realizing accurate robotic manipulation of flexible objects is very challenging. It asks for dexterous manipulation skill, and demands good adaptation to dynamic uncertainties due to deformation. Unlike traditional methods that basically depend on complex modeling, we follow the opposite approach by exploring possible simplifications based on high-speed motion strategy as well as high-speed visual...
In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation...
This paper shows how to carry out batch continuous-time trajectory estimation for bodies translating and rotating in three-dimensional (3D) space, using a very efficient form of Gaussian-process (GP) regression. The method is fast, singularity-free, uses a physically motivated prior (the mean is constant body-centric velocity), and permits trajectory queries at arbitrary times through GP interpolation...
In order to fully exploit the capabilities of a robotic systems, it is necessary to consider the limitations and errors of actuators and sensors already during the motion planning phase. In this paper, a framework for path planning is introduced, that uses heuristic search to build up a search graph in belief space, an extension to the deterministic state space considering the uncertainty associated...
This paper proposes a method for safe navigation based on representative sample inputs. The representative inputs are chosen in safe input sets based on their distance from forbidden input sets. The inputs are not only the safest decisions with respect to various unmodeled sources of uncertainties, but are also representatives of groups of nearby input sets resulting in similar maneuvers. This approach...
This paper presents a method to automatically generate probabilistic graphs for path planning out of stochastic maps. A sample based technique is used to generate a set of paths from which a graph structure of the map is built. The nodes of the graph represent places in the map and the edges the paths between those places, which are labelled with the cost of traversing the edge and the probability...
With the increasing usage of autonomous underwater vehicles in offshore applications, it has been important to have a controller that can satisfy a convincing performance throughout the mission. Due to non-linear and time varying dynamic, controlling this vehicle has some special difficulties. Modeling errors that may exist and Uncertainties which associated with the environment firstly evokes that...
This work focuses on robust nonlinear control design of a robot arm with micro-hand by using operator-based robust right coprirne factorization (RRCF) approach. In detail, to control the precise endpoint position of robot arm and obtain the desired force using micro-hand according to the external environment or task involved, a connected feedback control system based on operator-based RRCF approach...
To address the safety issues in human robot interactions (HRI), a safe set algorithm (SSA) was developed previously. However, during HRI, the uncertainty levels are changing in different phases of the interaction, which is not captured by SSA. A safe exploration algorithm (SEA) is proposed in this paper to address the uncertainty levels in the robot control. To estimate the uncertainty levels online,...
In this paper, we apply inverse optimal control approaches in order to recover the cost function that can explain given observations, for a class of constrained optimization problems. The inverse optimal control was recently solved in an approximately optimal framework, meaning that the interest is in finding the proper criteria suitable for the system for which the decisions are approximately optimal...
Iterative learning control was developed for systems that repeat the same task over a finite duration with resetting to the starting point once each execution is complete. The distinguishing feature of this form of control is the use of information from previous executions of the task to update the control signal to be applied on the next execution and thereby sequentially improve performance. Once...
Recognition of human gesture leads to a dynamic field that produces many various methods. The main way to improve the recognition process is to perform data fusion based on a qualification of each recognition method. The advance in data fusion gives also several solutions and the choice of a fusion method is a crucial point. The goal of this paper is to present an approach were the choice of the fusion...
This paper presents an extension of our previous work on hybrid metric/topological maps to enable uncertainty reduction planning through the map, taking into account both map uncertainty and distance. An enhancement of the edge structure which enables the simulation of bidirectional edge propagation through an extended Kalman filter is proposed in our heuristic search planning algorithm to plan for...
We present a novel efficient algorithm for object classification. Our method is based on the active learning framework, in which training and classification are performed in loops, and new ground truth labels are queried from the supervisor in each loop. Our underlying classifier is from the family of boosting methods, but in contrast to earlier methods, our Confidence Boosting particularly focusses...
We consider partially observable Markov decision processes (POMDPs), that are a standard framework for robotics applications to model uncertainties present in the real world, with temporal logic specifications. All temporal logic specifications in linear-time temporal logic (LTL) can be expressed as parity objectives. We study the qualitative analysis problem for POMDPs with parity objectives that...
This paper proposes an adaptive motion control system for a linear motor single-axis robot, which is mainly composed of a linear induction motor (LIM). On the basis of indirect field-oriented control mechanism, the dynamics of linear motor robot is investigated for considering the time-varying uncertainties that exist in the LIM drive. Next, a backstepping controller is designed for the objective...
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