PGR meeting and Research Presentations – June 2016

The monthly PGR meeting was held on Wednesday 8th June, 14:00-16:00, Room MC3108.

This month speaker was Dr Saddam Bekhet who gave a talk on his latest research findings. The title of his talk was ‘Signature-based Videos’ Visual Similarity Detection and Measurement’.

Dr Massoud Zolgharni  and Dr Marc Hanheide also discussed different issues including:

  • presentations schedule
  • encouragement of PGR students to participate in monthly meetings and present their work
  • PGR progress

 

The date and venue for the next meeting will be announced.

 

 

 

PGRs meeting and Research Presentations – May 2016

The monthly PGRs Research Presentations was held on Thursday 12th May, 2pm, Room MC3108.

This session we had the following presentations:

 

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Title: A ROS framework for single antenna RFID tag localisation with mobile robots.

 

By: George Broughton

 IMG_20160512_141116[1] Abstract: 

Over the last few years, RFID technology has evolved to give mobile robots an extra dimension to sense their surroundings. By determining whether certain tags, often fixed to interesting objects, can be read or not, the robot can effectively sense the objects presence. This is not just useful for finding lost objects, but has also been used for activity recognition. Additionally, tags have been used as landmarks within environments to aid with navigation.

This presentation looks at the development of a ROS framework for localising RFID tags from a mobile robot. The framework combines several different approaches to make use of the information provided by the tag and from the reader, to estimate possible locations of the tag. This is done by taking the output of the different algorithms, and then combining and feeding them into a densely populated occupancy grid using a bayesian update system to calculate the most probable tag location. Rather than rely on multiple antennas for trilateration, the framework exploits a robot’s ability to move within its environment to seek optimal positions to hone in on a tag. This also has the additional benefit of providing resistance to multipath signal errors.

This will lead to a framework that is future-proof, robust, works with multiple models of readers, and can be moulded to suit many needs.

 

 

 

 

 

PGRs meeting and Research Presentations – April 2016

The monthly PGRs Research Presentations was held on Wed. 13th April, 2pm, Room MC3108.

This session we had the following presentations:

 

PGRs Monthly meeting_April2016  (Slides )

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Agenda

  • Speaker –>
  • A quick look at the new “PGRs Management System”,PGR-MS1

PGR-MS2

  • PGRs Blog.

PGR-Blog

  • Discussion of the activities plan.
  • Update and plan for the “Showcase Event”
  • Announcements, AOB, & closing

 

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Title: Life-long Spatio-temporal Exploration of Dynamic Environments: An overview.

By: Joao Santos

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 Abstract: The primary purpose of robotic exploration is to autonomously acquire a complete and precise model of the robot’s operational environment. To explore efficiently, the robot has to direct its attention to environment areas that are currently unknown. If the world was static, these areas would simply correspond to previously unvisited locations. In the case of dynamic environments, visiting all locations only once is not enough, because they may change over time. Thus, dynamic exploration requires that the environment locations are revisited and their (re-) observations are used to update a dynamic environment model. However, revisiting the individual locations with the same frequency and on a regular basis is not efficient because the environment dynamics will, in general, not be homegeneous, (i.e. certain areas change more often and the changes occur only at certain times).

Similarly to the static environment exploration, the robot should revisit only the areas whose states are unknown at the time of the planned visits. Thus, the robot has to use its environment model to predict the uncertainty of the individual locations over time and use these predictions to plan observations that from a theoretical point of view improve its knowledge about the world’s dynamics. Hence, the observations are scheduled in order to obtain information about the environment changes, which are mainly caused by human-activity. As a consequence, using schedules motivated by the changes in metric maps increases the chance to extract  dynamics that are essential for object learning and activity recording tasks.

 

 

 

 

 

Farewell and Thank You

At the end of the March’s meeting, we had a Farewell and Thank You for previous “PGRs Students Reps” and PGRs who have been helping in various activities, including Reading Group, trips, coffee mornings, and the showcase.

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  • PGRs Representatives:

–1st Students Rep:  Touseef Quraishi

2nd Students Reps:

  • Christian
  • Mohammadreza.

 

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PGRs who have been helping:

(George; showcase events & reading group – Saddam; Showcase Events & support PGRs meeting – Francesco; trips & showcase – Ibrahim; trips & showcase – Christian; regular presentations & reading group – Talal; Reading group – Hussein; showcase)

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Thanks to all, and best wishes for the years to come.

 

PGRs meeting and Research Presentations – March. 2016

The monthly PGRs Research Presentations was held on Wed. 9th March, 2pm, Room MC3108.

This session we had the following presentations:

Title: Facilitating Individualised Collaboration with Robots (FInCoR).

By: Peter Lightbody

AGEDNA:

  1. Speaker –>
  2. Break & refreshments
  3. Farewell & Thank you to previous PGRs Student Reps and those who have been actively helping & supporting the PGRs activities & community.
  4. Brief about the responsibilities and benefits of being a PGR Student Rep.      
  5. Reps Election. (By SU Rep.)
  6. Announcements, AOB, & closing

 

Abstract: Enabling a robot to seamlessly collaborate with a human counterpart requires a robot to not only identify human preferences, but also to adapt in order to decrease the likelihood of distress and discomfort for the human collaborator. This work presents the use of qualitative spacial relations, combined with hidden Markov models, to identify and learn the unique characteristics inherent in the way a person performs a task. By doing this in real-time, a collaborative robot will be able to adapt it’s behaviour in order to best accommodate the person intuitive way of performing a task.