Design, trajectory generation and control of quadrotor research platform

2016 ◽  
Author(s):  
Durgesh Haribhau Salunkhe ◽  
Siddhant Sharma ◽  
Sujal Amrit Topno ◽  
Chandana Darapaneni ◽  
Amol Kankane ◽  
...  
Keyword(s):  
Trajectory Generation ◽  
Research Platform ◽  
And Control

Trajectory generation and control for a biped robot walking upstairs

2010 ◽  
Vol 8 (2) ◽  
pp. 339-351 ◽  
Author(s):  
Chan-Soo Park ◽  
Taesin Ha ◽  
Joohyung Kim ◽  
Chong-Ho Choi
Keyword(s):  
Trajectory Generation ◽  
Biped Robot ◽  
And Control

A Biologically Inspired Autonomous Robot Control Based on Behavioural Coordination in Evolutionary Robotics

2011 ◽  
pp. 107-129 ◽  
Author(s):  
José A. Fernández-León ◽  
Gerardo G. Acosta ◽  
Miguel A. Mayosky ◽  
Oscar C. Ibáñez
Keyword(s):  
Mobile Robots ◽  
Autonomous Vehicle ◽  
Trajectory Generation ◽  
Real Life ◽  
Evolutionary Robotics ◽  
Knowledge Based ◽  
Planning And Control ◽  
Life Problems ◽  
Path Planner ◽  
And Control

This work is intended to give an overview of technologies, developed from an artificial intelligence standpoint, devised to face the different planning and control problems involved in trajectory generation for mobile robots. The purpose of this analysis is to give a current context to present the Evolutionary Robotics approach to the problem, which is now being considered as a feasible methodology to develop mobile robots for solving real life problems. This chapter also show the authors’ experiences on related case studies, which are briefly described (a fuzzy logic based path planner for a terrestrial mobile robot, and a knowledge-based system for desired trajectory generation in the Geosub underwater autonomous vehicle). The development of different behaviours within a path generator, built with Evolutionary Robotics concepts, is tested in a Khepera© robot and analyzed in detail. Finally, behaviour coordination based on the artificial immune system metaphor is evaluated for the same application.

scholarly journals You + ME Registry: A Research Platform to Facilitate Clinical and Therapeutic Discoveries in ME/CFS and Related Diseases

2021 ◽  
Author(s):  
Allison Ramiller ◽  
Kathleen Mudie ◽  
Elle Seibert ◽  
Sadie Whittaker
Keyword(s):  
Multiple Sclerosis ◽  
Big Data ◽  
Chronic Fatigue Syndrome ◽  
Chronic Fatigue ◽  
Health Data ◽  
Myalgic Encephalomyelitis ◽  
Fatigue Syndrome ◽  
Research Platform ◽  
And Control

ME/CFS (Myalgic Encephalomyelitis / Chronic Fatigue Syndrome) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, has transformed the understanding of many complex illnesses like cancer (1,2) and multiple sclerosis (3,4), by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. Solve M.E. developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with Long COVID (LC) and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. The Registry is an invaluable resource because it integrates with a symptom tracking app, as well as a biorepository, to provide a robust and rich dataset that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies.

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