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We consider the problem of online robotic sampling in environmental monitoring tasks where the goal is to collect k best samples from n sequentially occurring measurements. In contrast to many existing works that seek to maximize the utility of the selected samples online, we aim to find the cardinality constrained subset of streaming measurements under irrevocable sampling decisions so that the prediction...
In the field of architecture, 3D printing technology has the advantage of shortening the construction period by continuous addition and installing the desired shape and structure directly on site. However, the conventional 3D printer structure has limitations in practical use because of its versatility, mobility, and limited accessibility. In this study, a 3-axis gantry robot type 3D printing simulator...
This paper presents an adaptive nonsingular terminal sliding mode formation control by means of output recurrent fuzzy wavelet neural networks for a group of networked heterogeneous Mecanum-wheeled omnidirectional robots with uncertainties. The dynamic behavior of each uncertain heterogeneous omnidirectional robot is modelled by a reduced three-input-three-output second-order state equation with uncertainties...
This paper focuses on developing new navigation and reconnaissance capabilities for cooperative unmanned systems in uncertain environments. The goal is to design a cooperative multi-vehicle system that can survey an unknown environment and find the most valuable route for personnel to travel. To accomplish the goal, the multi-vehicle system first explores spatially diverse routes and then selects...
This study is aimed to find low order, stable and proper controllers for the linearized models of two well known underactuated robots i.e. Acrobot and Pendubot around their upright equilibrium points in order to maximize the robustness of the feedback loop to uncertainties. The proposed method makes use of the robust stabilization of finite dimensional plants by Nevanlinna-Pick interpolation problem...
We present a method to model and classify trajectory data that come from surveillance videos. Observations of the locations of moving entities are used to estimate their expected velocity in the scene. Such estimation is performed by a Gaussian process regression that enables to approximate probabilistically the expected velocity of entities given some observed evidence in the scene. Subsequently,...
The paper discusses the assessment of humanrobot interaction (HRI) experiments in social robotics. Some of the MOnarCH project experiments are analyzed, illustrating key ideas on performance indicators based in activation rates of micro-behaviors and environment models. The consistency of the results obtained indicates that the ideas are fully applicable to other experiments in social robotics.
A wheeled mobile robotic system (WMR) is a kind modular robotic, nonlinear and the system subjected to nonholonomic constraints. It is impossible to obtain an exact dynamics model due to unknown parameters, unpredictable and irregular features, external disturbance. This paper proposes an adaptive robust controller to guide WMR to follow the desired trajectory. The cascade control system is applied...
In this work, we consider a testbed for a robotic astronomical 0.5-m. telescope mount. We focus on the problem of identification uncertainty that cannot be obtained from analytical consideration (i.e. friction phenomena). Thus, we use a recursive least-squares algorithm for identification values of friction terms. Since the required signals cannot be obtained directly from the object (or they are...
This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modelled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information...
Model based approaches, such as those that use partial differential equations (PDE), lend themselves to gas distribution mapping and gas source localization. Moreover, they also permit constructing intelligent sampling strategies. However, a realistic mathematical model of gas dispersion is complex and computationally expensive to solve. This is especially the case for inverse problems, where sources...
Robust environment perception is essential for decision-making on robots operating in complex domains. Intelligent task execution requires principled treatment of uncertainty sources in a robot's observation model. This is important not only for low-level observations (e.g., accelerom-eter data), but also for high-level observations such as semantic object labels. This paper formalizes the concept...
Programming by Demonstration allows to transfer skills from human demonstrators to robotic systems by observation and reproduction. One aspect that is often overlooked is that humans show different trajectories over multiple demonstrations for the same task. Observed movements may be more precise in some phases and more diverse in others. It is well-known that the variability of the execution carries...
Agile robots, such as small Unmanned Aerial Vehicles (UAVs) can have a great impact on the automation of tasks, such as industrial inspection and maintenance or crop monitoring and fertilization in agriculture. Their deploy-ability, however, relies on the UAV's ability to self-localize with precision and exhibit robustness to common sources of uncertainty in real missions. Here, we propose a new system...
Parts assembly, in a broad sense, is to make multiple objects to be in specific relative poses in contact with each other. One of the major reasons that make it difficult is uncertainty. Because parts assembly involves physical contact between objects, it requires higher precision than other manipulation tasks like collision avoidance. The key idea of this paper is to use simulation-aided physical...
This paper reports on an algorithm for planning trajectories that allow a multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown parameters. In many problems like self calibration or model parameter identification some states are only observable under a specific motion. These motions are often hard to find, especially for inexperienced users. Therefore, we consider system model...
Inference and decision making under uncertainty are essential in numerous robotics problems. In recent years, the similarities between inference and control triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of the aforementioned efforts, inference and control, as well as inference and belief space planning (BSP) are still...
In this work, we present an anytime planner for creating open-loop trajectories that solve rearrangement planning problems under uncertainty using nonprehensile manipulation. We first extend the Monte Carlo Tree Search algorithm to the unobservable domain. We then propose two default policies that allow us to quickly determine the potential to achieve the goal while accounting for the contact that...
This paper considers the problem of safe mission planning of dynamic systems operating under uncertain environments. Much of the prior work on achieving robust and safe control requires solving second-order cone programs (SOCP). Unfortunately, existing general purpose SOCP methods are often infeasible for real-time robotic tasks due to high memory and computational requirements imposed by existing...
Recognition of sequential human activities, such as “sitting down” and “standing up”, is a common but challenging problem in human-robot interaction, which requires modeling their underlying temporal patterns. Although previous sequence modeling methods, such as Hidden Conditional Random Fields (HCRFs), demonstrated satisfactory recognition accuracy, they do not explicitly model the uncertainty in...
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