Difference: FRCVPublicProject (10 vs. 11)

Revision 112019-09-18 - LabTech

Line: 1 to 1
 
META TOPICPARENT name="FordhamRoboticsAndComputerVisionLaboratory"
Changed:
<
<

Overview of Research Projectsin Progress at the FRCV Lab

>
>

Overview of Research Projectsin Progress at the FRCV Lab

Using Air Disturbance Detection for Obstacle Avoidance in Drones

The use of unmanned aerial vehicles (drones) is expanding to commercial, scientific, and agriculture applications, such as surveillance, product deliveries and aerial photography etc. One challenge for applications of drones is detecting obstacles and avoiding collisions. Especially small drones in proximity to people need to detect people around them and avoid injuring those people. A typical solution to this issue is the use of camera sensor, ultrasonic sensor for obstacle detection or sometimes just manual control (teleoperation). However, these solutions have costs in battery lifetime, payload, operator skill. Due to their diminished ability to support any payload, it is difficult to put extra stuff on small drones. Fortunately, most drones are equipped with an inertial measurement unit (IMU).

The IMU can tell us the drone’s attitude and accelerations from the gyroscope and accelerometer. We note that there will be air disturbance in the vicinity of the drone when it’s moving close to obstacles or other drones. The data from the gyroscope and accelerometer will change to reflect this. Our objective is to detect obstacles from the aforementioned air disturbance by analyzing the data from the gyroscope and accelerometer. Air disturbance can be produced by many reasons such as ground effect, drones in proximity to people or wind gust from other sides. These situations can occur at the same time to make things more complicated. To make the experiment simpler, we just detect air disturbance produced from flying close to or underneath an overhead drone.

We choose a small drone, the Crazyflie 2.0, as the experiment tool. The Crazyflie 2.0 is a lightweight, open source flying development platform based on a micro quadcopter. It has several built-in sensors including gyroscope, accelerometer etc. ROS (Robot Operating System) is a set of software libraries and tools for modular robot applications. The point of ROS is to create a robotics standard. ROS has great simulation tools such as Rviz and Gazebo to help us to run the simulation before conducting real experiments on drones. Currently there is little Crazyflie support in ROS, 4 however, we wish to use ROS to conduct our experimentation because it has become a de facto standard. More details here

 

Multilingual Static Analysis (MLSA)

Multilingual Software Analysis (MLSA) or Melissa is a lightweight tool set developed for the analysis of large software systems which are multilingual in nature (written in more than one programming language). Large software systems are often written in more than one programming language, for example, some parts in C++, some in Python etc. Typically, software engineering tools work on monolingual programs, programs written in single language, but since in practice many software systems or code bases are written in more than on language, this can be less ideal.

Melissa produces tools to analyze programs written in more than one language and generate for example, dependency graphs and call graphs across multiple languages, overcoming the limitation of software tools only work on monolingual software system or programs.

Leveraging the static analysis work developed for DTRA, we are looking at multilingual to provide refactoring and other information for very large, multi language software code bases. This project is funded by a two year grant from Bloomberg NYC. The objective of the project is to make a number of open-source MLSA tools available for general use and comment. For more details, see here.

Changed:
<
<
multilingual_system.png MLSA_logo.png  callgraph-1.png
>
>
multilingual_system.png MLSA_logo.png callgraph-1.png
 

TOAD Tracking: Automating Behavioral Research of the Kihansi Spray Toad

The Kihansi Spray Toad, officially classified as ‘extinct in the wild’, is being bred by the Bronx Zoo in an effort to reintroduce the species back into the wild. With thousands of toads already bred in captivity, an opportunity to learn about the toads behavior presents itself for the first time ever, at scale. In order to accurately and efficiently gain information about the toads behavior, we present an automated tracking system that is based on the Intel Real Sense SR300 Camera. As the average size of the toad is less than 1 inch, existing tracking systems prove ineffective. Thus, we developed a tracking system using a combination of depth tracking and color correlation to identify and track individual toads. Depth and color video sequences are produced from the SR300 camera. Depth video sequences, in grayscale, are derived from an infrared sensor and sense any motion that may occur, hence detecting moving toads. Color video sequences, in RGB, allow for color correlation while tracking targets. A template of the color of a toad is taken manually, once, as a universal example of what the color of a toad should be. This is then compared against potential targets every frame to increase the confidence a toad has been detected versus, for example, a leaf moving. The program detects and tracks toads from frame to frame, and produces a set of tracks in 2 and 3 dimensions, as well as 2 dimensional heat maps. For further details click here.

Screen_Shot_2019-01-17_at_4.03.44_PM.png

TOAD1.png

Getting it right the first time! Establishing performance guarantees for C-WMD autonomous robot missions

In research being conducted for the Defense Threat Reduction Agency (DTRA), we are concerned with robot missions that may only have a single opportunity for successful completion, with serious consequences if the mission is not completed properly. In particular we are investigating missions for Counter-Weapons of Mass Destruction (C-WMD) operations, which require discovery of a WMD within a structure and then either neutralizing it or reporting its location and existence to the command authority. Typical scenarios consist of situations where the environment may be poorly characterized in advance in terms of spatial layout, and have time-critical performance requirements. It is our goal to provide reliable performance guarantees for whether or not the mission as specified may be successfully completed under these circumstances, and towards that end we have developed a set of specialized software tools to provide guidance to an operator/commander prior to deployment of a robot tasked with such a mission. We have developed a novel static analysis approach to analysing behavior based programs, coupled with a Bayesian network approach to predicting performance. Comparing predicted results to extensive empirical validation conducted at GATech's mobile robots lab, we have shown we can verify/predict reasltic performance for waypoint missions, multiple robot missions, missions with uncertain obstacles, missions including localization software. We are currently working on human-in-the-loop systems.

3D_graph.jpg

Space-Based Potential Fields: Exploring buildings using a distributed robot team navigation algorithm

. In this work we propose an approach, the Space-Based Potential Field (SBPF) approach, to controlling multiple robots for area exploration missions that focus on robot dispersion. The SBPF method is based on a potential field approach that leverages knowledge of the overall bounds of the area to be explored. This additional information allows a simpler potential field control strategy for all robots but which nonetheless has good dispersion and overlap performance in all the multi-robot scenarios while avoiding potential minima. Both simulation and robot experimental results are presented as evidence.

explore.jpg

Visual Homing with Stereovision

Visual Homing is a navigation method based on comparing a stored image of a goal location to the current image to determine how to navigate to the goal location. It is theorized that insects such as ants and bees employ visual homing techniques to return to their nest or hive. Visual Homing has been applied to robot platforms using two main approaches: holistic and feature-based. Both methods aim at determining the distance and direction to the goal location. Visual navigational algorithms using Scale Invariant Feature Transform (SIFT) techniques have gained great popularity in the recent years due to the robustness of the SIFT feature operator. There are existing visual homing methods that use the scale change information from SIFT such as Homing in Scale Space (HiSS). HiSS uses the scale change information from SIFT to estimate the distance between the robot and the goal location to improve homing accuracy. Since the scale component of SIFT is discrete with only a small number of elements, the result is a rough measurement of distance with limited accuracy. We have developed a visual homing algorithm that uses stereo data, resulting in better homing performance. This algorithm, known as Homing with Stereovision utilizes a stereo camera mounted on a pan-tilt unit, which is used to build composite wide-field images. We use the wide-field images coupled with the stereo data obtained from the stereo camera to extend the SIFT keypoint vector to include a new parameter depth (z). Using this information, Homing with Stereovision determines the distance and orientation from the robot to the goal location. The algorithm is novel in its use of a stereo camera to perform visual homing. We compare our method with HiSS in a set of 200 indoor trials using two Pioneer 3-AT robots. We evaluate the performance of both methods using a set of performance metrics described in this paper and we show that Homing with Stereovision improves on HiSS for all the performance metrics for these trials.

In current work we have modified the HSV code to use a database of stored stereoimagery and we are conducting extensive testing of the algorithm.

labimage.jpg

Ghosthunters! Filtering mutual sensor interference in closely working robot teams

We address the problem of fusing laser ranging data from multiple mobile robots that are surveying an area as part of a robot search and rescue or area surveillance mission. We are specifically interested in the case where members of the robot team are working in close proximity to each other. The advantage of this teamwork is that it greatly speeds up the surveying process; the area can be quickly covered even when the robots use a random motion exploration approach. However, the disadvantage of the close proximity is that it is possible, and even likely, that the laser ranging data from one robot include many depth readings caused by another robot. We refer to this as mutual interference. Using a team of two Pioneer 3-AT robots with tilted SICK LMS-200 laser sensors, we evaluate several techniques for fusing the laser ranging information so as to eliminate the mutual interference. There is an extensive literature on the mapping and localization aspect of this problem. Recent work on mapping has begun to address dynamic or transient objects. Our problem differs from the dynamic map problem in that we look at one kind of transient map feature, other robots, and we know that we wish to completely eliminate the feature. We present and evaluate three different approaches to the map fusion problem: a robot-centric approach, based on estimating team member locations; a map-centric approach, based on inspecting local regions of the map, and a combination of both approaches. We show results for these approaches for several experiments for a two robot team operating in a confined indoor environment .

dataview.jpg

Drone project : Crazyflie

Added:
>
>
Drones are an exciting kind of robot that has recently found their way into commercial mainstream robotics. The most recent high-profile example of this is their appearance in the opening ceremonies of the 2018 Winter Olympics in Pyeong Chang, South Korea. Due to their epic appearance, the drones received high acclaim from the audience, solidifying the idea of using drones as performers in a public arena, capable of carrying out emotion-filled acts.

Beyond this example, our vision is to utilize drones, more specifically drone swarms, to not only perform theatrical performances, but also operate as a collective entity that can communicate and interact meaningfully with ordinary people in daily life activities. Our thesis is that drone swarms can more effectively impart emotive communication than solo drones. For instance, drone swarms can play the role of a tour guide at attractions or museums, to bring tourists on a trip through the most notable points at the site. In emergency situations that require evacuation of large crowds, drone swarms can help guide and coordinate the movement of survivors towards safe areas, as well as signaling first responders towards areas where help is needed the most.

Advantages of Drone Swarms

The main advantages of drone swarms over solo drones are the added dimensions of freedom. More specifically, with multiple drones, we can

  • Harness the 3D space in the form of occupancy volume. Drones can be commanded to spread apart or come close to one another, as well as hover in space at specific relative positions to one another, to depict different shapes of varying volume.
  • Express group-based dynamic properties such as coordination or synchronization, similar to team dance with human dancers.
  • Depict emotional states with collective group-based motions. Specifically, the movement velocity of individual drones in a swarm, either relative to each other or as absolute values, can be tweaked to depict internal states of emotions. For instance, while keeping the collective velocity zero, agitation can be expressed by fast moving drones, while calmness can be depicted by constant and slow motions, causing a therapeutic effect.
  • Convey metaphorical messages in the form of sketches that involve more than one entities. For instance, depicting the notions of reciprocal love or fighting is much easier done using two or more drones than with single ones.
Motivating Applications

Equipped with the ability to impart emotive messages, drone swarms could be used for crowd control and guidance, e.g.,

  • Shepherding groups of visitors around a site such as tourists visiting a location, or school groups visiting a museum or zoo. In this scenario, the drone swarm form boundaries around the group and shepherd them around. In addition, the swarm can behave in a manner to elicit emotions, such as excitement, as particular stations on the tour or landmarks are encountered.
  • Controlling crowds in a large gathering such as a music concert or a large community meeting. In this scenario, the drone swarm needs to keep the crowd within the confines of the meeting and patrol the crowd boundaries to prevent
    access to prohibited areas. Emergencies happening in such large crowds, e.g., a person feeling ill or a fight breaking out, can pose challenges.
  • Advertising produces and stores to passing crowds of potential customers. In this scenario, the swarm can be deployed at the entrance of shops or attractions, attracting customers to products or stores in a non-invasive way. For example, they can spell out the names or the shapes of the products being sold. Since the drones are airborne and do not occupy any ground space, they will not interfere with uninterested passers-by.
Technical Challenges

In order to construct drone swarms that can communicate, interact with, and operate within the public space, we believe that the following technical challenges need to be addressed.

 This project page is here

Older Projects

This includes the following projects that are temporarily on hiatus:

- Spatial Stereograms: a 3D landmark representation

- Efficient legged locomotion: Rotating Tripedal Mechanism

- Cognitive Robotics: ADAPT. Synchronizing real and synthetic imagery.

  • Persons/group who can view/change the page:

<meta name="robots" content="noindex" />

-- (c) Fordham University Robotics and Computer Vision

META FILEATTACHMENT attachment="explore.jpg" attr="" comment="" date="1395178199" name="explore.jpg" path="explore.jpg" size="182499" user="DamianLyons" version="1"
META FILEATTACHMENT attachment="3D_graph.jpg" attr="" comment="" date="1395178321" name="3D_graph.jpg" path="3D_graph.jpg" size="108310" user="DamianLyons" version="1"
META FILEATTACHMENT attachment="labimage.jpg" attr="" comment="" date="1395178511" name="labimage.jpg" path="labimage.jpg" size="128945" user="DamianLyons" version="1"
META FILEATTACHMENT attachment="dataview.jpg" attr="" comment="" date="1395178580" name="dataview.jpg" path="dataview.jpg" size="73620" user="DamianLyons" version="1"
META FILEATTACHMENT attachment="Screen_Shot_2019-01-17_at_4.03.44_PM.png" attr="" comment="A sample TOAD analysis frame" date="1547760310" name="Screen_Shot_2019-01-17_at_4.03.44_PM.png" path="Screen Shot 2019-01-17 at 4.03.44 PM.png" size="1790173" user="PhilipBal" version="1"
META FILEATTACHMENT attachment="TOAD1.png" attr="" comment="Sample Output of TOAD Program" date="1547760891" name="TOAD1.png" path="TOAD1.png" size="433858" user="PhilipBal" version="1"
META FILEATTACHMENT attachment="MLSA_logo.png" attr="" comment="" date="1559144426" name="MLSA_logo.png" path="MLSA_logo.png" size="39428" user="LabTech" version="1"
META FILEATTACHMENT attachment="multilingual_system.png" attr="" comment="" date="1559144507" name="multilingual_system.png" path="multilingual_system.png" size="49163" user="LabTech" version="1"
META FILEATTACHMENT attachment="1.pdf" attr="" comment="" date="1559144515" name="1.pdf" path="1.pdf" size="12174" user="LabTech" version="1"
META FILEATTACHMENT attachment="callgraph.pdf" attr="" comment="Call graph showing Python calling C++ with different variant examples." date="1559144588" name="callgraph.pdf" path="callgraph.pdf" size="12174" user="LabTech" version="1"
META FILEATTACHMENT attachment="callgraph-1.png" attr="" comment="Call graph showing Python calling C++ with different variant examples." date="1559145344" name="callgraph-1.png" path="callgraph-1.png" size="27383" user="LabTech" version="2"
 
This site is powered by the TWiki collaboration platform Powered by PerlCopyright © 2008-2019 by the contributing authors. All material on this collaboration platform is the property of the contributing authors.
Ideas, requests, problems regarding TWiki? Send feedback