A general framework for task-constrained motion planning with moving obstacles

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 575-598 ◽  
Author(s):  
Massimo Cefalo ◽  
Giuseppe Oriolo
Keyword(s):  
Dynamic Models ◽  
Randomized Algorithm ◽  
Time History ◽  
Search Space ◽  
Performance Criterion ◽  
Control Input ◽  
Constrained Motion ◽  
Motion Generation ◽  
Moving Obstacles ◽  
Main Component

SUMMARYConsider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration–exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.

Exploring the combinatorial space of complete pathways to chemicals

2018 ◽  
Vol 46 (3) ◽  
pp. 513-522 ◽  
Author(s):  
Lin Wang ◽  
Chiam Yu Ng ◽  
Satyakam Dash ◽  
Costas D. Maranas
Keyword(s):  
De Novo ◽  
Design Procedure ◽  
Search Space ◽  
Performance Criterion ◽  
Mixed Integer ◽  
Design Tools ◽  
Minimum Number ◽  
Comprehensive Search ◽  
Synthetic Routes ◽  
Combinatorial Space

Computational pathway design tools often face the challenges of balancing the stoichiometry of co-metabolites and cofactors, and dealing with reaction rule utilization in a single workflow. To this end, we provide an overview of two complementary stoichiometry-based pathway design tools optStoic and novoStoic developed in our group to tackle these challenges. optStoic is designed to determine the stoichiometry of overall conversion first which optimizes a performance criterion (e.g. high carbon/energy efficiency) and ensures a comprehensive search of co-metabolites and cofactors. The procedure then identifies the minimum number of intervening reactions to connect the source and sink metabolites. We also further the pathway design procedure by expanding the search space to include both known and hypothetical reactions, represented by reaction rules, in a new tool termed novoStoic. Reaction rules are derived based on a mixed-integer linear programming (MILP) compatible reaction operator, which allow us to explore natural promiscuous enzymes, engineer candidate enzymes that are not already promiscuous as well as design de novo enzymes. The identified biochemical reaction rules then guide novoStoic to design routes that expand the currently known biotransformation space using a single MILP modeling procedure. We demonstrate the use of the two computational tools in pathway elucidation by designing novel synthetic routes for isobutanol.

Vibration Control With Neural Network Dynamic Compensator

1995 ◽  
Author(s):  
Toshinari Shiotsuka ◽  
Kazuo Yoshida ◽  
Akio Nagamatsu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Object ◽  
Time History ◽  
Network Dynamic ◽  
Control Input ◽  
Object A ◽  
Neural Network Controller ◽  
Dynamic Compensator ◽  
Suspension Model

Abstract An approach is presented on designing the dynamic compensator-type controller, using two kinds of neural networks. One is used for identification of system dynamic characteristics of the control object. A time history of response under sine-sweep input is used as the teaching signal of this neural network. The other is used as the neural network controller. The control input is determined with this neural network in order that a performance index concerning the state variable and the input force takes the minimum value. These two neural networks are combined reciprocally in a cascade type in designing the controller. Validity and usefulness of the presented approach are verified by both an computer simulation and an experiment with an active suspension model.

scholarly journals Analysis of Influence of a Crane Flexible Supports, Link Flexibility, and Joint Friction on Vibration Associated with Programmed Motion Execution

2019 ◽  
Vol 8 (2) ◽  
pp. 337-350 ◽  
Author(s):  
Elżbieta Jarzębowska ◽  
Andrzej Urbaś ◽  
Krzysztof Augustynek
Keyword(s):  
Dynamic Models ◽  
Computational Procedure ◽  
Task Demands ◽  
Constrained Systems ◽  
Constrained Motion ◽  
Joint Friction ◽  
Flexible Supports ◽  
Flexible Parts ◽  
Link Flexibility ◽  
Service Task

Abstract Background The paper presents vibration analysis of dynamic models of systems with flexible mechanical components, friction modeled and subjected to position and kinematic programmed constraints, which can be imposed as control goals, work or service task demands. Methods The constrained dynamics is derived using an automated computational procedure dedicated to constrained systems. The procedure was successfully implemented to rigid system models. A class of systems composed of flexible parts and subjected to programmed motions is considered in the paper. Their motion analysis has to be accompanied by vibration inspection. The novelty of the presented approach is in the possibility of analyzing system motions and vibrations that can be induced by the presence of programmed constraints. Conclusions The constrained motion is examined by the example of a crane model equipped with a flexible link, e.g. a jib, friction modeled in its joints and subjected to programmed constraints. The example delivers a realistic work situation, in which the crane carries loads and moves according to the programmed constraint put on motion.

scholarly journals Predictive Control of Mobile Robot Using Kinematic and Dynamic Models

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Manel Mendili ◽  
Faouzi Bouani
Keyword(s):  
Optimal Control ◽  
Mobile Robot ◽  
Predictive Control ◽  
Dynamic Models ◽  
Kinematic Model ◽  
Performance Criterion ◽  
Control Sequence ◽  
Omnidirectional Mobile Robot ◽  
Quadratic Performance ◽  
Predictive Controllers

This paper presents a predictive control of omnidirectional mobile robot with three independent driving wheels based on kinematic and dynamic models. Two predictive controllers are developed. The first is based on the kinematic model and the second is founded on the dynamic model. The optimal control sequence is obtained by minimizing a quadratic performance criterion. A comparison has been done between the two controllers and simulations have been done to show the effectiveness of the predictive control with the kinematic and the dynamic models.

Tangent bundle RRT: A randomized algorithm for constrained motion planning

Robotica ◽  
2014 ◽  
Vol 34 (1) ◽  
pp. 202-225 ◽  
Author(s):  
Beobkyoon Kim ◽  
Terry Taewoong Um ◽  
Chansu Suh ◽  
F. C. Park
Keyword(s):  
Motion Planning ◽  
Configuration Space ◽  
Tangent Bundle ◽  
Numerical Experiments ◽  
Randomized Algorithm ◽  
Random Trees ◽  
Constrained Motion ◽  
Wide Range ◽  
Random Node ◽  
Planning Problems

SUMMARYThe Tangent Bundle Rapidly Exploring Random Tree (TB-RRT) is an algorithm for planning robot motions on curved configuration space manifolds, in which the key idea is to construct random trees not on the manifold itself, but on tangent bundle approximations to the manifold. Curvature-based methods are developed for constructing tangent bundle approximations, and procedures for random node generation and bidirectional tree extension are developed that significantly reduce the number of projections to the manifold. Extensive numerical experiments for a wide range of planning problems demonstrate the computational advantages of the TB-RRT algorithm over existing constrained path planning algorithms.

A strategy for safe 3D navigation of non-holonomic robots among moving obstacles

Robotica ◽  
2017 ◽  
Vol 36 (2) ◽  
pp. 275-297 ◽  
Author(s):  
Chao Wang ◽  
Andrey V. Savkin ◽  
Matthew Garratt
Keyword(s):  
Formal Analysis ◽  
Autonomous Vehicle ◽  
Control Input ◽  
Bounded Control ◽  
3D Navigation ◽  
Final Destination ◽  
Moving Obstacles ◽  
Navigation Algorithm ◽  
Holonomic Robot ◽  
And Performance

SUMMARYA non-holonomic robot with a bounded control input travels in a dynamic unknown 3D environment with moving obstacles. We propose a 3D navigation strategy to reach a given final destination point while avoiding collisions with obstacles. A formal analysis of the proposed 3D robot navigation algorithm is given. Computer simulation results and experiments with a real flying autonomous vehicle confirm the applicability and performance of the proposed guidance approach.

Velocity Occupancy Space: Robot Navigation and Moving Obstacle Avoidance With Sensor Uncertainty

2009 ◽  
Author(s):  
Rachael Bis ◽  
Huei Peng ◽  
Galip Ulsoy
Keyword(s):  
Robot Navigation ◽  
Search Space ◽  
Sensor Data ◽  
Occupancy Grid ◽  
Moving Obstacles ◽  
Robotic Vehicle ◽  
Stationary Obstacles ◽  
Velocity Obstacle ◽  
Moving Obstacle ◽  
Limited Accuracy

In order to autonomously navigate in an unknown environment, a robotic vehicle must be able to sense obstacles, determine their velocities, and follow a clear path to a goal. However, the perceived location and motion of the obstacles will be uncertain due to the limited accuracy of the robot’s sensors. Thus, it is necessary to develop a system that can avoid moving obstacles using uncertain sensor data. The method proposed here is based on a certainty occupancy grid—which has been used to avoid stationary obstacles in an uncertain environment—in conjunction with the velocity obstacle concept—which allows a robot to avoid well-known moving obstacles. The combination of these two techniques leads to velocity occupancy space: a search space which allows the robot to avoid moving obstacles and navigate efficiently to a goal using uncertain sensor data.

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