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DTSTART:20151025T030000
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UID:calendar.7064.field_data.0@www.diag.uniroma1.it
DTSTAMP:20260404T175011Z
CREATED:20150528T154923Z
DESCRIPTION:Articulated tracked robots are currently used in search and res
 cue\, military\, agriculturaland planetary exploration applications\, wher
 e terrain conditions are difficult and unpredictable. They are better suit
 ed for such tasks than wheeled vehicles due to the larger contact area of 
 tracks with the ground\, which provides better traction on harsh terrains.
  As such robots are deployed  in dynamic\, unstructured\, and open environ
 ments\, such as a disaster scenario\, a key requirement is the ability to 
 interpet the environment\, plan highly efficient motions and execute these
  motions for autonomous safe navigation and morphological adaptation. Seve
 ral research efforts in robotics have been made to increase the level of a
 utonomy of articulated tracked vehicles\, focusing on adaptation\, stabili
 ty\, self-reconfiguration\, track-soil interaction\, motion planning and c
 ontrol. The aim of this workshop is to bring together researchers of diffe
 rent areas and students interested in the subject to discuss the main open
  research challenges in 3D motion planning and control of these robots in 
 Urban Search & Rescue. The workshop includes talks from three speakers tha
 t are active in the different subareas of this topic and that will present
  their research work as well as their in-field experience in deploying the
 se robots in real disaster scenarios.  Schedule   First talk: 10:00 - 10:4
 5 Speaker: Dr. Arun Kumar Singh\, Post-doctoral researcher\, Bio-Medical R
 obotics Lab\, BGU\, Israel. Title: Some fresh ideas for motion planning on
  uneven/rough terrains. Abstract: In this talk\, I will discuss some key c
 hallenges associated with motion planning on uneven terrains. In particula
 r I will show\, how the complexity is decided by how rigorously we model t
 he robot-terrain interaction. If we model interaction in terms of only pos
 tural changes induced on the robot by the underlying terrain\, the plannin
 g task remains fairly simple. However\, sometimes it becomes necessary to 
 incorporate a more complex dynamic robot-terrain interaction during motion
  planning and this leads to a significant increase in the complexity of th
 e problem. I will show that the first key step in performing a dynamic mot
 ion planning on uneven terrain is to decipher how for a particular control
  input\, the robot-terrain interacts to lead to a particular state evoluti
 on. Thereafter\, I will show that once we have figured out this state evol
 ution puzzle\, the task of navigating through an uneven/rough terrain can 
 be framed as an optimization with state and control dependent  differentia
 l constraints. The peculiar and challenging part is that these constraints
  rarely have an analytical form\, thus\, forcing us to adopt an sampling a
 pproach to decide whether a particular state and control combination satis
 fy the differential constraints. To this end\, I will present a concept ca
 lled Feasible Acceleration Count which can be exploited to develop a greed
 y sampling based motion planner. I will also talk about a new concept call
 ed non-linear time scaling which can be exploited to efficiently solve dif
 ferential constraints and thus finds strong application in uneven terrain 
 motion planning. Towards the end of the talk\, I will discuss some current
  open questions and promising future research directions. Short bio: Dr.Ar
 un Kumar Singh obtained his Bachelors in Mechanical Engineering from Natio
 nal Institute of Technology\, Durgapur\, India and his PhD in Electronics 
 and Communication Engineering from Robotics Research Center\, IIIT-Hyderab
 ad\, India. Currently he is a Postdoctoral fellow at the ABC Robotics Init
 iative of Ben-Gurion University\, Israel. His research interests lies in m
 otion planning and control of  complex dynamical systems. At present he is
  trying to develop human-in-loop motion planning methodologies for reducin
 g the cognitive load of the operator in tele-operation tasks\, with specia
 l focus towards  Robot Assisted Surgeries.  He is also highly interested i
 n fusing conventional machine learning algorithms and findings of neurosci
 ence communities for human motion prediction in complex scenarios Second t
 alk: 11:00 - 11:45 Speaker: Prof. Mario Gianni\, DIAG 'A. Ruberti'\, Sapie
 nza University of Rome\, Italy. Title: Adaptive robust three-dimensional t
 rajectory tracking for actively articulated tracked vehicles. Abstract: In
  this talk\, I will present a unified framework for trajectory tracking co
 ntroller design of Actively Articulated Tracked Vehicles. The approach dev
 elops both a direct and differential kinematic model of the AATV correlati
 ng the robot body motion to the sub-tracks motion. The benefit of this app
 roach is to allow the controller to flexibly manage all the DOF of the AAT
 V as well as the steering. The differential kinematic model integrates a d
 ifferential drive robot model\, compensating the slippage between the vehi
 cle tracks and the traversed terrain. The underlying feedback control law 
 dynamically accounts for the kinematic singularities of the mechanical veh
 icle structure. The designed controller also integrates a strategy selecto
 r to reduce both the effort of the sub-tracks servo motors and the tractio
 n force on the robot body\, recognizing when the robot is moving on horizo
 ntal plane surfaces. According to this strategy\, rotational motions of th
 e robot\, moving within narrow passages\, are also facilitated. This frame
 work has been used for the design of the trajectory tracking controller of
  the actively articulated racked vehicle Absolem\, recently realized for t
 he EU-FP7-ICT Project NIFTi. Several experiments have been performed\, in 
 both virtual and real scenarios\, to validate the designed trajectory trac
 king controller\, while the robot Absolem negotiates rubbles\, stairs and 
 complex terrain surfaces. Short bio: Mario Gianni is Assistant Professor a
 t the Department of Computer Control\, and Management Engineering “A. Rube
 rti”\, Sapienza University of Rome. He is a member of the research group a
 t the Vision\, Perception and Learning Robotics Laboratory\, ALCOR\, direc
 ted by Prof. Fiora Pirri. From January 2010 till December 2013 he has been
  working for the EU FP7 ICT 247870 project NIFTI\, aiming at providing nat
 ural cooperation between humans and teams of robots in Urban Search and Re
 scue scenarios. Currently\, he is working for the EU FP7 ICT 609763  proje
 ct TRADR\, aiming at developing new S&T for long-term human-robot teaming 
 for robot assisted disaster response. In 2012 he tightly collaborated with
  the Italian National Fire Corps in order to deploy a human-robot team for
  assessing damage of historical buildings and cultural artifacts of the an
 cient city of Mirandola\, in Northern Italy\, hit by an earthquake. He rec
 eived the Ph.D. in Computer Engineering from Sapienza University of Rome\,
  with a dissertation titled “Multilayered Cognitive Control for Unmanned G
 round Vehicles”. Research interests include statistics and logic\, applied
  to robotics\, autonomous navigation and adaptation for self-reconfigurabl
 e robots in cluttered environments\, low and high-level control in multi-r
 obot collaboration. Third talk: 12:00 - 12:45 Speaker: Federico Ferri\, Ph
 D student\, DIAG 'A. Ruberti'\, Sapienza University of Rome\, Italy. Title
 : 3D motion planning with dynamics  Abstract: In motion planning with dyna
 mics\, the objective is to compute a trajectory to the goal region that no
 t only avoids collisions with obstacles but also satisfies differential co
 nstraints imposed by robot dynamics. It is motivated by navigation\, explo
 ration\, search and rescue missions\, where it is essential to compute tra
 jectories that can be followed by the robot in a physical real world. Moti
 on planning\, in its early years\, did not take dynamics into account. Ins
 tead\, it considered only the geometry of the robot and of obstacles. This
  simplified view fueled research in sampling-based approaches\, such as pr
 obabilistic roadmap\, rapidly-exploring random tree and expansive space tr
 ee methods. However\, it has been noted that when dealing with challenges 
 in problems with dynamics\, sampling-based motion planners slow down signi
 ficantly. In this talk\, I will describe an approach to incorporates dynam
 ics into sampling-based methods for real-time motion planning for articula
 ted tracked robots. This approach makes use of a motion planning algorithm
  for sampling primitives in the control space of the robot and relies on a
  real-time physics engine which performs a state propagation process to es
 timates the resulting robot state. I'll illustrate the framework  which sy
 nergically combines motion planning and the underlying phisical simulation
 \, aslo discussing pro and cons.        Short bio: Federico Ferri received
  his M.Sc. in Engineering in Artificial Intelligence and Robotics from the
  Department of Computer Control\, and Management Engineering “A. Ruberti”\
 , Sapienza University of Rome. He is a member of the research group at the
  Vision\, Perception and Learning Robotics Laboratory\, ALCOR\, directed b
 y Prof. Fiora Pirri. Currently\, he is a PhD student at Sapienza Universit
 y of Rome. He is working for the H2020-ICT Project SecondHands aiming at d
 esigning a robot assistant for maintenance tasks that either pro-actively 
 or as a result of prompting\, can offer assistance to maintenance technici
 ans performing routine and preventative maintenance. Research interests in
 clude reasoning\, knowledge representation and robot planning. 
DTSTART;TZID=Europe/Paris:20150603T100000
DTEND;TZID=Europe/Paris:20150603T100000
LAST-MODIFIED:20150602T094316Z
LOCATION:Aula B203\, Via Ariosto 25
SUMMARY:Challenges in 3D motion planning and control for articulated tracke
 d robots in Urban Search & Rescue - Arun Kumar Singh\, Mario Gianni\, Fede
 rico Ferri
URL;TYPE=URI:http://www.diag.uniroma1.it/node/7064
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