BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Pacific BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20211107T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220213T222153Z UID:C0DD7422-0175-4275-A746-3C4E063477FA DTSTART;TZID=Canada/Pacific:20220210T170000 DTEND;TZID=Canada/Pacific:20220210T183000 DESCRIPTION:Abstract:\n\nIn this seminar\, Dr. Nichilas Gans will present a recent investigation into novel control methodologies that employ probabi listic movement primitives (ProMPs)\, control Lyapunov Functions (CLFs) an d control barrier functions (CBFs).\n\nProMPs are a powerful tool for defi ning a distribution of robot trajectories via demonstration. However\, the native ProMP control methods suffer from a number of drawbacks. For examp le\, these methods tend to rely on linear control designs thus limiting th eir applicability in robotics. In addition\, they tend to be overly sensit ive to initial parameters which can lead to stability issues. Conversely\, CLF and CBF control approaches employ feedback linearization to handle no nlinearities in the system dynamics and real-time quadratic programming to find an optimal control that satisfies all safety constraints while minim izing control effort. However\, CLFs and CBFs remain difficult to define a nd implement without expertise in nonlinear control. We propose to define CLFs to regulate a system described by the ProMP mean and CBFs as function of the ProMP standard deviation. Thus\, the system\, such as a robot\, ma y move along a trajectory within the distribution while guaranteeing that the system state never leaves more than a desired distance from the distri bution mean. We then extend this approach to include time-varying CBFs whi ch can be incorporated to avoid static and moving obstacles and investigat e model predictive control approaches to optimize and accommodate constrai nts over a finite horizon. Furthermore\, we highlight how the proposed met hod may allow a designer to emphasize certain safety objectives that are m ore important than the others. A series of simulations and experiments dem onstrate the efficacy of our approach and show it can run in real time.\n\ nBio: Dr. Nicholas Gans is Division Head of Autonomy and Intelligent Syste ms\, the University of Texas at Arlington Research Institute. Prior to thi s position\, he was a professor in the department of Electrical and Comput er Engineering at The University of Texas at Dallas. His research interest s are in the fields of robotics\, nonlinear and adaptive control\, machine vision\, and autonomous vehicles. Dr. Gans earned his BS in electrical en gineering from Case Western Reserve University in 1999\, then his M.S. in electrical and computer engineering in 2002 and his Ph.D. in systems and e ntrepreneurial engineering from the University of Illinois Urbana-Champaig n in 2005. He was a postdoctoral researcher at the University of Florida a nd as a postdoctoral associate with the National Research Council and Air Force Research Laboratory.\n\nSpeaker(s): Dr. Nicholas Gans\, \n\nAgenda: \nMeeting Start: 5:00pm\n\nInvited Talk: 5:00pm-6:00pm\n\nQ&A\, Discussion : 6:00pm to 6:30pm\n\nEveryone Welcome. Please Register\, Thank you.\n\nVi rtual: https://events.vtools.ieee.org/m/299546 LOCATION:Virtual: https://events.vtools.ieee.org/m/299546 ORGANIZER:csrasmcVancouver@gmail.com SEQUENCE:13 SUMMARY:Adding Safety to Probabilistic Movement Primitive Control via Contr ol Barrier Functions by Dr. Nicholas Gans URL;VALUE=URI:https://events.vtools.ieee.org/m/299546 X-ALT-DESC:Description: <br /><p>Abstract:</p>\n<p>In this seminar\, Dr. Ni chilas Gans will present a recent investigation into novel control methodo logies that employ probabilistic movement primitives (ProMPs)\, control Ly apunov Functions (CLFs) and control barrier functions (CBFs).</p>\n<p>ProM Ps are a powerful tool for defining a distribution of robot trajectories v ia demonstration.&nbsp\; However\, the native ProMP control methods suffer from a number of drawbacks. For example\, these methods tend to rely on l inear control designs thus limiting their applicability in robotics. In ad dition\, they tend to be overly sensitive to initial parameters which can lead to stability issues. &nbsp\;Conversely\, CLF and CBF control approach es employ feedback linearization to handle nonlinearities in the system dy namics and real-time quadratic programming to find an optimal control that satisfies all safety constraints while minimizing control effort.&nbsp\; However\, CLFs and CBFs remain difficult to define and implement without e xpertise in nonlinear control.&nbsp\; &nbsp\;&nbsp\;We propose to define C LFs to regulate a system described by the ProMP mean and CBFs as function of the ProMP standard deviation.&nbsp\; &nbsp\;Thus\, the system\, such as a robot\, may move along a trajectory within the distribution while guara nteeing that the system state never leaves more than a desired distance fr om the distribution mean. We then extend this approach to include time-var ying CBFs which can be incorporated to avoid static and moving obstacles a nd investigate model predictive control approaches to optimize and accommo date constraints over a finite horizon. &nbsp\;Furthermore\, we highlight how the proposed method may allow a designer to emphasize certain safety o bjectives that are more important than the others. A series of simulations and experiments demonstrate the efficacy of our approach and show it can run in real time.</p>\n<p>&nbsp\;</p>\n<p>Bio:&nbsp\; Dr. Nicholas Gans is Division Head of Autonomy and Intelligent Systems\, the University of Tex as at Arlington Research Institute.&nbsp\; Prior to this position\, he was a professor in the department of Electrical and Computer Engineering at T he University of Texas at Dallas.&nbsp\; His research interests are in the fields of robotics\, nonlinear and adaptive control\, machine vision\, an d autonomous vehicles. Dr. Gans earned his BS in electrical engineering fr om Case Western Reserve University in 1999\, then his M.S. in electrical a nd computer engineering in 2002 and his Ph.D. in systems and entrepreneuri al engineering from the University of Illinois Urbana-Champaign in 2005. H e was a postdoctoral researcher at the University of Florida and as a post doctoral associate with the National Research Council and Air Force Resear ch Laboratory.</p>\n<p>&nbsp\;</p><br /><br />Agenda: <br /><p>Meeting Sta rt: 5:00pm</p>\n<p>Invited Talk: 5:00pm-6:00pm&nbsp\;</p>\n<p>Q&amp\;A\, D iscussion: 6:00pm to 6:30pm</p>\n<p>Everyone Welcome. Please Register\, Th ank you.&nbsp\;</p> END:VEVENT END:VCALENDAR