A Comparative Study of Simple Dynamic Models and Control Schemes for Elastic Manipulators

1992 ◽  
Author(s):  
Keith W. Buffinton ◽  
Joseph Lam
Keyword(s):  
Comparative Study ◽  
Dynamic Models ◽  
Control Schemes ◽  
And Control

Distributed Cohesive Motion Control of Quadrotor Vehicle Formations

2010 ◽  
Author(s):  
I˙smail Bayezit ◽  
Barıs¸ Fidan ◽  
Mehdi M. Amini ◽  
Iman Shames
Keyword(s):  
Motion Control ◽  
Dynamic Models ◽  
Individual Agent ◽  
3 Dimensional ◽  
Vehicle System ◽  
Control Scheme ◽  
Control Schemes ◽  
Vehicle Systems ◽  
And Control ◽  
Coordination And Control

In this paper, we focus on distributed cohesive motion control of 3-dimensional multi-vehicle systems considering individual agent dynamic behaviors as well as the overall multi-vehicle system. In this context, we examine maintenance of geometric formation of a swarm of autonomous quadrotor vehicles, i.e. maintenance of the distance between each agent pair in the swarm, during arbitrary maneuvers. A distributed scheme for the formation maintenance task is developed first. This coordination scheme is integrated with low level dynamic controllers designed for the agents considering practical kinematic and dynamic models for quadrotor vehicles. The distributed motion control scheme is implemented to move the vehicles whose initial positions satisfying the desired formation maintenance constraints are specified, to a set of final desired positions satisfying the same constraints cohesively without deviating from the desired geometric formation during motion. The developed coordination and control schemes are tested via a number of simulations.

Biohybrid robots are synthetic biology systems

2018 ◽  
Author(s):  
Joseph Ayers
Keyword(s):  
Nervous System ◽  
Synthetic Biology ◽  
Behavioral Control ◽  
Eukaryotic Cells ◽  
Chemical Senses ◽  
Biomimetic Robots ◽  
Control Schemes ◽  
Induced Pluripotent ◽  
And Control ◽  
And Robotics

This chapter describes how synthetic biology and organic electronics can integrate neurobiology and robotics to form a basis for biohybrid robots and synthetic neuroethology. Biomimetic robots capture the performance advantages of animal models by mimicking the behavioral control schemes evolved in nature, based on modularized devices that capture the biomechanics and control principles of the nervous system. However, current robots are blind to chemical senses, difficult to miniaturize, and require chemical batteries. These obstacles can be overcome by integration of living engineered cells. Synthetic biology seeks to build devices and systems from fungible gene parts (gene systems coding different proteins) integrated into a chassis (induced pluripotent eukaryotic cells, yeast, or bacteria) to produce devices with properties not found in nature. Biohybrid robots are examples of such systems (interacting sets of devices). A nascent literature describes genes that can mediate organ levels of organization. Such capabilities, applied to biohybrid systems, portend truly biological robots guided, controlled, and actuated solely by life processes.

scholarly journals Vertical Jumping for Legged Robot Based on Quadratic Programming

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3679
Author(s):  
Dingkui Tian ◽  
Junyao Gao ◽  
Xuanyang Shi ◽  
Yizhou Lu ◽  
Chuzhao Liu
Keyword(s):  
Quadratic Programming ◽  
Stance Phase ◽  
Center Of Mass ◽  
Maximum Error ◽  
Legged Robot ◽  
Flight Phase ◽  
Programming Framework ◽  
Vertical Jumping ◽  
Control Schemes ◽  
And Control

The highly dynamic legged jumping motion is a challenging research topic because of the lack of established control schemes that handle over-constrained control objectives well in the stance phase, which are coupled and affect each other, and control robot’s posture in the flight phase, in which the robot is underactuated owing to the foot leaving the ground. This paper introduces an approach of realizing the cyclic vertical jumping motion of a planar simplified legged robot that formulates the jump problem within a quadratic-programming (QP)-based framework. Unlike prior works, which have added different weights in front of control tasks to express the relative hierarchy of tasks, in our framework, the hierarchical quadratic programming (HQP) control strategy is used to guarantee the strict prioritization of the center of mass (CoM) in the stance phase while split dynamic equations are incorporated into the unified quadratic-programming framework to restrict the robot’s posture to be near a desired constant value in the flight phase. The controller is tested in two simulation environments with and without the flight phase controller, the results validate the flight phase controller, with the HQP controller having a maximum error of the CoM in the x direction and y direction of 0.47 and 0.82 cm and thus enabling the strict prioritization of the CoM.

scholarly journals On the generation of validated manufacturing process optimization and control schemes

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 57-62
Author(s):  
Alexios Papacharalampopoulos ◽  
Harry Bikas ◽  
Christos Michail ◽  
Panagiotis Stavropoulos
Keyword(s):  
Process Optimization ◽  
Manufacturing Process ◽  
Control Schemes ◽  
Optimization And Control ◽  
And Control

Design, Construction and Control of a Remotely Operated Vehicle (ROV)

2011 ◽  
Author(s):  
Alireza Marzbanrad ◽  
Jalil Sharafi ◽  
Mohammad Eghtesad ◽  
Reza Kamali
Keyword(s):  
High Speed ◽  
Degrees Of Freedom ◽  
Remotely Operated Vehicle ◽  
Six Degrees Of Freedom ◽  
Depth Sensors ◽  
Control Schemes ◽  
On Line ◽  
Operation Unit ◽  
And Control ◽  
Design Construction

This is report of design, construction and control of “Ariana-I”, an Underwater Remotely Operated Vehicle (ROV), built in Shiraz University Robotic Lab. This ROV is equipped with roll, pitch, heading, and depth sensors which provide sufficient feedback signals to give the system six degrees-of-freedom actuation. Although its center of gravity and center of buoyancy are positioned in such a way that Ariana-I ROV is self-stabilized, but the combinations of sensors and speed controlled drivers provide more stability of the system without the operator involvement. Video vision is provided for the system with Ethernet link to the operation unit. Control commands and sensor feedbacks are transferred on RS485 bus; video signal, water leakage alarm, and battery charging wires are provided on the same multi-core cable. While simple PI controllers would improve the pitch and roll stability of the system, various control schemes can be applied for heading to track different paths. The net weight of ROV out of water is about 130kg with frame dimensions of 130×100×65cm. Ariana-I ROV is designed such that it is possible to be equipped with different tools such as mechanical arms, thanks to microprocessor based control system provided with two directional high speed communication cables for on line vision and operation unit.

Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models

2017 ◽  
Vol 140 (3) ◽  
Author(s):  
Nurali Virani ◽  
Devesh K. Jha ◽  
Zhenyuan Yuan ◽  
Ishana Shekhawat ◽  
Asok Ray
Keyword(s):  
Particle Filtering ◽  
Dynamic Models ◽  
Training Data ◽  
Data Driven ◽  
Conditional Density ◽  
Bayesian Filtering ◽  
State Dependent ◽  
State Evolution ◽  
And Control ◽  
Nonparametric Kernel

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation technique to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Overview of Path-Planning and Obstacle Avoidance Algorithms for UAVs: A Comparative Study

2018 ◽  
Vol 06 (02) ◽  
pp. 95-118 ◽  
Author(s):  
Mohammadreza Radmanesh ◽  
Manish Kumar ◽  
Paul H. Guentert ◽  
Mohammad Sarim
Keyword(s):  
Path Planning ◽  
Comparative Study ◽  
Operating Conditions ◽  
Computational Time ◽  
Safe Operation ◽  
Sense And Avoid ◽  
Planning Algorithms ◽  
And Control ◽  
Global And Local ◽  
Further Development

Unmanned aerial vehicles (UAVs) have recently attracted the attention of researchers due to their numerous potential civilian applications. However, current robot navigation technologies need further development for efficient application to various scenarios. One key issue is the “Sense and Avoid” capability, currently of immense interest to researchers. Such a capability is required for safe operation of UAVs in civilian domain. For autonomous decision making and control of UAVs, several path-planning and navigation algorithms have been proposed. This is a challenging task to be carried out in a 3D environment, especially while accounting for sensor noise, uncertainties in operating conditions, and real-time applicability. Heuristic and non-heuristic or exact techniques are the two solution methodologies that categorize path-planning algorithms. The aim of this paper is to carry out a comprehensive and comparative study of existing UAV path-planning algorithms for both methods. Three different obstacle scenarios test the performance of each algorithm. We have compared the computational time and solution optimality, and tested each algorithm with variations in the availability of global and local obstacle information.

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