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Sunday, April 19, 2020 | History

3 edition of Task planning with uncertainty for robotic systems found in the catalog.

Task planning with uncertainty for robotic systems

Tiehua Cao

Task planning with uncertainty for robotic systems

  • 208 Want to read
  • 17 Currently reading

Published by Rensselaer Polytechnic Institute, Electrical, Computer, and Systems Engineering, National Aeronautics and Space Administration, National Technical Information Service, distributor in Troy, N.Y, [Washington, DC, Springfield, Va .
Written in English

    Subjects:
  • Robots -- Control systems -- Planning.,
  • Uncertainty.

  • Edition Notes

    Statementby Tiehua Cao.
    Series[NASA contractor report] -- NASA CR-193253., CIRSSE report -- #137., NASA contractor report -- NASA CR-193253., CIRSSE report -- #137.
    ContributionsUnited States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL14696290M

    Lyapunov-Based Control of Robotic Systems describes nonlinear control design solutions for problems that arise from robots required to interact with and manipulate their environments. Since most practical scenarios require the design of nonlinear controllers to work around uncertainty and measurement-related issues, the authors use Lyapunov’s. We investigate the complexity of the classical problem of optimal policy computation in Markov decision processes. All three variants of the problem (finite horizon, infinite horizon discounted, and infinite horizon average cost) were known to be solvable in polynomial time by dynamic programming (finite horizon problems), linear programming, or successive approximation techniques (infinite Cited by: Suggested Citation:"Discussion: Issues in Design and Uncertainty."National Research Council. Human Factors in Automated and Robotic Space Systems: Proceedings of a gton, DC: The National Academies Press. doi: /   Robotic Systems as Context Entities (I) → Black Box View Action Specification Action Parametization Action Monitoring and Control Task Specification Task Parametization Task Monitoring and Control Localization Mapping Motion Constraints Interfaces CONTEXT DATA ID Robot Type Attributes: Current State and Configuration of Layers.


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Task planning with uncertainty for robotic systems by Tiehua Cao Download PDF EPUB FB2

TASK PLANNING WITH UNCERTAINTY FOR ROBOTIC SYSTEMS by Tiehua Cao Rensselaer Polytechnic Institute Electrical, Computer, and Systems Engineering Troy, New York Get this from a library. Task planning with uncertainty for robotic systems. [Tiehua Cao; United States.

National Aeronautics and Space Administration.]. Full text of "Task planning with uncertainty for robotic systems" See other formats.

We show how planning with assumptions, combined with layered knowledge, solves several problems in AI for robotics: (i) planning and acting under uncertainty, (ii) planning and acting in open worlds, (iii) explaining task failure, and (iv) verifying by:   During the generation and execution of task plans, different kinds of uncertainties need to be handled to ensure the efficiency and reliability of the system.

Following a systematic modeling procedure, a fuzzy Petri net is constructed based on geometric relations, fuzzy variables, and.

This book covers integration planning and control based on prior knowledge and real-time sensory information. A new task-oriented approach to sensing, planning and control introduces an event-based method for system design together with task planning and three dimensional modeling in the execution of remote operations.

Robot software systems tend to be complex. This complexity is due, in large part, to the need to control diverse sensors and actuators in real time, in the face of significant uncertainty and noise.

PREFACE; CHAPTER 1 INTRODUCTION; Task Planning: Representation and Search; Task Planning for Robotic Systems; Overview of the Book; CHAPTER 2 LITERATURE REVIEW; Introduction; Task Planning; Assembly Planning; Planning Under Uncertainty; Petri Nets with Fuzzy Data; Conclusion of Literature Reviews; CHAPTER 3.

Motion planning under uncertainty is a critical ability for autonomous robots operating in uncontrolled en- vironments, such as homes or offices. For robotic systems, uncertainty arises from two. Probabilistic planning is very useful for handling uncertainty in planning tasks to be carried out by robots.

ROSPlan is a framework for task planning in the Robot Operating System (ROS), but until now it has not been possible to use probabilistic planners within the : Gerard Canal, Michael Cashmore, Senka Krivić, Guillem Alenyà, Daniele Magazzeni, Carme Torras. Reviewer: Raphael M. Malyankar Researchers in the field of artificial intelligence (AI) have long studied automated planning, and there is a vast body of literature related to AI planning, ranging from journal and conference research papers, to several edited collections of papers and books describing approaches or systems, or case studies of applications.

how planning with assumptions, combined with layered knowledge, solves several problems in AI for robotics: (i) planning and acting under uncertainty, (ii) planning and acting in open worlds, (iii) explaining task failure, and (iv) verifying explanations.

IDD—a schema for robot knowledgeFile Size: 2MB. Task planning is divided into three phases: modeling, task specification, and manipulator program synthesis. The term generalized configuration space is used to describe systems in which other objects are included as part of the configuration.

These may be movable, and their shapes may vary. (Uncertainty) Planning compliant motions for. Robotic systems are now ubiquitous in the manufacturing industry. Robots are capable of reliably manipulating objects using artificial intelligence techniques, which allows a machine to determine how a task can be completed successfully [].However, when employed in the manufacturing process, robots are pre-programmed with limited or no decision-making by: 6.

The research centers on the development of new algorithmic frameworks for modeling, simulating, and planning for human-robot collaboration, which requires advances in robot training, task modeling, human motion understanding, high-dimensional motion planning with uncertainty, and metrics to assess human-robot joint action.

Skrzypczyk K., Mellado M. () Multi-robot Task Planning Problem with Uncertainty in Game Theoretic Framework. In: Nawrat A., Simek K., Świerniak A. (eds) Advanced Technologies for Intelligent Systems of National Border Security. Studies in Computational Intelligence, vol Springer, Berlin, HeidelbergAuthor: Krzysztof Skrzypczyk, Martin Mellado.

Robotic bin picking requires using a perception system to estimate the posture of parts in the bin. The selected singulation plan should be robust with respect to perception uncerCited by: 2. The book is organized into five parts, representative of critical long-term and emerging research thrusts in the multi-robot community: Coordination for Perception, Coverage, and Tracking; Task Allocation and Coordination Strategies; Modular Robots and Novel Mechanisms and Sensors; Formation Control and Planning for Robot Teams; and Learning.

It has long been the goal of engineers to develop tools that enhance our ability to do work, increase our quality of life, or perform tasks that are either beyond our ability, too hazardous, or too tedious to be left to human efforts.

Autonomous mobile robots are the culmination of decades of research and development, and their potential is seemingly p to the FutureServing as. Design and Implementation of Distributed Autonomous Coordinators for Cooperative Multi-Robot Systems: /IJSDA The paper presents a systematic method of the design of cooperative task planning and execution for complex robotic systems using multiple robots.

BecauseCited by: 2. We present an efficient approach to generating paths for a robotic manipulator that are collision-free and guaranteed to meet task specifications despite pose uncertainty. We first describe how to use task space regions (TSRs) to specify grasping and object placement tasks for a manipulator.

We then show how to modify a set of TSRs for a certain task to take into account pose uncertainty. Discrete Event Systems Based Robotic Task Modeling. Most of the existing robotic task models are not based on formal approaches, are concerned only with a small number of behaviors and are typically tailored to the task at hand.

We have proposed, back to [1] a systems-theory-based task modeling approach for general robotic tasks which. Issues of Perceptual Anchoring in Ubiquitous Robotic Systems. Proc. of the ICRA Workshop on Omniscient Space. Roma, Italy, C. Galindo, J.A. Fernández-Madrigal, J. González, A.

Saffiotti. Using Semantic Information for Improving Efficiency of Robot Task Planning. Proc. of the ICRA Workshop on Semantic Information in Robotics. Roma. Tung and A. Kak, "Integrating Sensing, Task Planning, and Execution for Robotic Assembly," IEEE Transactions on Robotics and Automation, pp.Vol.

12, Number 2, April Deforming Virtual Objects Interactively in Accordance with an Elastic Model. Virtual Reality. Cambridge University Press, (expected). See the Virtual Reality page to download a FREE COPY.

This book covers the fundamentals of virtual reality systems, including geometric modeling, transformations, graphical rendering, optics, the human vision, auditory, and vestibular systems, interface design, human factors, developer recommendations, and technological issues.

He, E. Brunskill and N. Roy. "Efficient Planning under Uncertainty with Macro-actions". Journal of Artificial Intelligence Research. Vol pages. The Journal of Intelligent and Robotic Systems (JINT) publishes peer-reviewed and original, invited, survey and review papers.

These papers should promote and disseminate scientific knowledge and information in the fields of system theory, control systems, distributed systems, bioengineering, robotics and automation, human-robot interaction, human-machine interfaces and interaction, robot. Mantegh, Iraj, and S.

Darbandi, Nazanin. "Knowledge-Based Task Planning Using Natural Language Processing for Robotic Manufacturing." Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering by: 2.

for in the planning phase before task execution. We leverage the increasingly fast performance of sampling-based motion planners available for certain robots, combined with stochastic modeling, to enable these robots to quickly and effectively respond to uncertainty during task execution.

In this paper we consider tasks in which the objective is to. A Joint Report of AAPM Task Group and the European Federation of Organizations for Medical Physics (EFOMP) Task Group Report: Utilization of [18F]Fluorodeoxyglucose Positron Emission Tomography ([18F]FDG-PET) in Radiation Therapy.

AAPM task group comprehensive proton therapy machine quality assurance. Distributed robotics is a rapidly growing and maturing interdisciplinary research area lying at the intersection of computer science, network science, control theory, and electrical and mechanical engineering.

The goal of the Symposium on Distributed Autonomous Robotic. Foundations of Robotics: Analysis and Control. () by T Yoshikawa The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning.

Motion planning (also known as the navigation problem or the piano mover's problem) is a term used in robotics is to find a sequence of valid configurations that moves the robot from the source to destination. For example, consider navigating a mobile robot inside a building to a distant waypoint.

It should execute this task while avoiding walls and not falling down stairs. understand fundamental theory forming the basis of six robotics disciplines: kinematics, dynamics, controls, grasping, planning, and human-robot interaction.

know principles of robotic systems design, and be able to analyze trade-offs in such designs. be able to integrate a system of several components.

The method is designed for motion planning problems involving robotic systems with non-linear (but linearizable) dynamics, any cost function with positive (semi)definite Hessians, and motion uncertainty that can be reasonably modeled using Gaussian distributions that can be state- and by: This paper develops a decentralized multi-agent task allocation (Dec-MATA) algorithm for multi-robot applications.

The task planning problem is posed as a maximum-weighted matching of a bipartite graph, the solution of which using the blossom algorithm allows each robot to autonomously identify the optimal sequence of tasks it should undertake.

The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and.

@article{osti_, title = {A dosimetric uncertainty analysis for photon-emitting brachytherapy sources: Report of AAPM Task Group No. and GEC-ESTRO}, author = {DeWerd, Larry A. and Ibbott, Geoffrey S. and Meigooni, Ali S.

and Mitch, Michael G. and Rivard, Mark J. and Stump, Kurt E. and Thomadsen, Bruce R. and Venselaar, Jack L. and. Virtual Reality.

Cambridge University Press, See the Virtual Reality page to download a FREE COPY. This book covers the fundamentals of virtual reality systems, including geometric modeling, transformations, graphical rendering, optics, the human vision, auditory, and vestibular systems, interface design, human factors, developer recommendations, and technological issues.

Sampling-based Motion Planning for Robotic Information Gathering Geoffrey A. Hollinger School of Mechanical, Industrial & Manufacturing Engineering Oregon State University Corvallis, ORUSA Email: [email protected] Gaurav S. Sukhatme Department of Computer Science University of Southern California Los Angeles, CAUSA.

the task planning has become an important aspect for smooth operation of such a system. Task planning implies the design of strategies for task execution. In other words, a task planning algorithm provides a set of desired (i.e., reference) trajectories for the position and force variables, which are used by the controller to execute a given task.Dealing with uncertainties along with high-efficiency planning for task assignment problem is still challenging, especially for multi-agent systems.

In this paper, two frameworks—Compromise View model and the Nearest-Neighbour Search model—are analyzed and compared for co-operative path planning combined with task assignment of a multi-agent system in dynamic by: 2.Intelligent Robotic Systems: Theory, Design and Applications, presents and justifies the fundamental concepts and ideas associated with the modeling and analysis of intelligent robotic systems.

Appropriate for researchers and engineers in the general area of robotics and automation, Intelligent Robotic Systems is both a solid reference as well.