Interdrone 2015 Notes: Difference between revisions
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From [[Interdrone 2015]] | From [[Interdrone 2015]] | ||
== People == | |||
* Dyan Gibbens - currently owns two drone-based companies. I was very impressed with Dyan and think she could be a valuable consultant in assisting with the regulatory challenges. | |||
* Jim Williams - former FAA employee that wrote much of the new regulation regarding UAVs. | |||
== Areas for Further Research == | == Areas for Further Research == | ||
Line 5: | Line 10: | ||
* [http://dronekit.io/ Dronekit] | * [http://dronekit.io/ Dronekit] | ||
* [https://www.dronecode.org/ DroneCode] | * [https://www.dronecode.org/ DroneCode] | ||
* Driden model (?) | |||
* [https://en.wikipedia.org/wiki/Computer_stereo_vision Stereo Vision] (see OpenCV) | |||
* [http://opencv.org/ OpenCV] | |||
* [https://en.wikipedia.org/wiki/Gimli_Glider Gimli Glider] | |||
* [http://beagleboard.org/ BeagleBoard] - Rasberry Pi Competitor | |||
== Crowd Analytics through Facial Recognition == | == Crowd Analytics through Facial Recognition == | ||
Line 48: | Line 58: | ||
* mark@visuallaw.com | * mark@visuallaw.com | ||
* sketchfab - 3d modeling software | * sketchfab - 3d modeling software | ||
* pointcloud | |||
* DroneDeploy - android app for creating 3d mesh and orthomap ($300 a month) | |||
* Pix4D - the primary software for creating 3 modeling of terrain | |||
* Adjisoft Photoscan (?) - Competitor to pix4d | |||
* Autodesk is working on comparable software | |||
== DroneKit == | |||
== Flying in Wind: State Estimation for UAS == | |||
* Douglas Weibel, PHD | |||
* Wind - a vector velocity field with spatial and temporal variation | |||
* Wind has structured (predictive) and random properties (turbulence) that are difficult to model | |||
* State | |||
** Attitude (Roll, Pitch, Yaw, Roll Rate, Pitch Rate, Yaw Rate) | |||
** Attitude Rates | |||
** Position | |||
** Velocity | |||
* Other possible states | |||
** Wind Paramters | |||
** Height over ground | |||
** Battery energy | |||
** gyro bias | |||
** accel bias | |||
* Attitude Estimation | |||
** Stabilized Sub Platform | |||
** Whabba's problem | |||
** Integration | |||
** Combined | |||
* Contempory Sensors | |||
** Gyros | |||
** Accelerometers | |||
** Magnetomers | |||
** GPS Baseline | |||
** Thermopile | |||
** Elecstratic potential | |||
** Opical sensors | |||
* Federated Filter | |||
* Attitude Filter ( Cascaded Filter ) | |||
== Applications of Rotation Matrices and Matrix-Vector Algebra in Autopilots == | |||
Bill Premerlani | |||
* A rotation matrix is a linear homegeneous transformation | |||
* [https://en.wikipedia.org/wiki/Tensor Tensor] | |||
== FAA Regulations: Past, Present, and Future == | |||
* Jim Williams | |||
* Drones are named from the first unmanned aircraft in Britain during WW1 | |||
* [https://www.faa.gov/documentLibrary/media/Advisory_Circular/AC_00-1_1A.pdf] | |||
* FAA cant regulate anything congress doesn't give them authority over | |||
* Required for an aircraft to be regulated | |||
** Approved Pilot | |||
** Aircraft Certificate | |||
** | |||
* Code of Federal Regulations (91.113) - you must be able to see and avoid other aircraft (thus-- you need a 333) | |||
* COWA - Certificate of Waiver or Authorization | |||
* AC - 91.57A new AC that regulates RC hobby | |||
* AC's are not regulatory | |||
* CFR 14 91.13 Careless and Reckless Operation | |||
** Aircraft operations for the purpose of air navigation. No person may operate an aircraft in a careless or reckless manner so as to endanger the life or property of another. | |||
** Don't do stupid things that put people in the air at risk. | |||
* 14 CFR Part 107 creates new rules for small unmanned aircraft systems | |||
* Examples | |||
** CNN is looking at tethered aircraft (tether provides power) - Power Electronics 01 | |||
** Railroad wants UAV to monitor tracks in front of trains looking for issues with tracks | |||
** Farmers are looking for ways to safely use UAV without LOS by monitoring airspace for incoming commercial aircraft | |||
* FAA - Department of Transportation interactions take a lot of time | |||
* Most insurance policies will not cover accidents caused by commercial use of drones | |||
* Some companies with 333 exemptions are suing companies operating without the proper exemptions | |||
== Embedded Computer Vision for Safer, Faster Drones == | |||
* Goksel Dedeoglu, Founder PercepTonic, LLC | |||
* Computer vision algorithms analyze images to extract information about the world (allowing machines that perceive) | |||
** Size and shape | |||
** 3d position | |||
** Identity | |||
** Expression | |||
** Location | |||
** Motion | |||
** Illumniation, weather, etc. | |||
* Two things relevant for drone operation: | |||
** Motion | |||
** Depth | |||
* Inertial sensors | |||
** Identifies key points | |||
* GerbilBall is a safety sphere enabled by real-time depth sensing | |||
** Stereo-based | |||
** Has limitations on how close the two objects can be to each other | |||
** Can have as many cameras as needed to create a realtime look in all directions (expensive) | |||
** For objects that don't move often, may be cheaper to build an internal model of what has been seen | |||
* Best guess for power efficient vision computation is heterogeneous and multi-core architechtures | |||
* Examples | |||
** Fire Phone makes good use of computer vision with multiple cameras | |||
** Dyson 360 Roomba | |||
* [http://www.cvisioncentral.com/evw2015 http://www.cvisioncentral.com/evw2015] | |||
* [http://www.embedded-vision.com http://www.embedded-vision.com] | |||
== DroneKit == | |||
* A single API that runs onboard drones | |||
** Cloud REST API | |||
** Android SDK ("3DR Services") | |||
** Python SDK | |||
* Dronekit exists as a compatibility layer on top of the autopilot | |||
* Onboard and offboard MAVLink | |||
* Droneshare.com | |||
** REST API (speaks JSON) | |||
** Used for storing fetching displaying logs and other data | |||
* Full autopilot control from mobile | |||
* Built on Android AIDL infrastructure | |||
* Examples | |||
** Tower, DroidPlanner, Tower Pebble, Tower Wear, Solo | |||
* DK Python | |||
* "Solo is the best tool for aerial video". | |||
** Runs on PixHawk 2 (unreleased) | |||
** SDK for Solo [http://dev.3dr.com dev.3dr.com] | |||
*** Prototype in Python without low level system knowledge | |||
* Pixhawk | |||
* tim@3dr.com | |||
== Structural Inspections == | |||
* 6 weeks to 190 days to get 333 | |||
* Several of the participants have run into issues with RF interference. One person had a fly away in downtown LA. It was identified as interference caused by a nearby consumer WIFI device. One person recommended an RF analysis as part of the pre-flight. | |||
* Military-grade components have much better shielding and seem to handle interference better. | |||
* | |||
== Human Factors == | |||
* Alexander Stimson - alex@autonometrics.com * | |||
* In order to keep from overwhelming the operator | |||
** Multimodal - Use things like sound, or LEDS, to indicate status | |||
* Multiple HSC Tasks = divided attention problem - most people don't multitask well | |||
** Encourage task switching efficiency | |||
** Minimize the switching costs | |||
** Ensure that the operator is focused on the appropriate tasks | |||
*** Procedures | |||
*** Structure based abstractions - colors, arrows, etc. | |||
*** Automation Cueing | |||
*** Cues/alerts should be appropriate and not overwhelming. There is a cognitive cost to processing cues. Don't make the problem worse. | |||
*** Consider Interruption Lag cueing-- an early warning about something that will eventually become important (the upcoming block in tetris) - studies are not conclusive on effectivity | |||
*** Increase role of automation | |||
*** Provide stopping rules - let the operator know when to move on | |||
* Humans are not rational decision makers | |||
* Three general classes of heuristics | |||
** Representative - humans are generally insensitive to prior probability (what happened yesterday) | |||
** Anchoring - early assumptions have more weight than new information | |||
** Availability - | |||
* Bias | |||
** Confirmation Bias - once a hypothesis has been made, people tend to try and support it | |||
**Assimilation Bias - once a hypothesis has been made, new evidence tends to be bias towards that hypothesis | |||
** Automation Bias - when you have an autonomous system, people tend to give it too much weight | |||
* Supervisory Monitoring of Operations - System identifies that the operator is overwhelmed | |||
** Use metrics to identify poor performance | |||
** Can be human or automated | |||
** How to handle an overloaded operator | |||
*** Notifiy the operator | |||
*** Redistribute task load to other team members | |||
*** Bring in more team members | |||
*** Modify the mission objectives (accept it) | |||
* Distributed Decision Making | |||
** Building Situational Awareness | |||
*** Ensley Model | |||
**** Perception | |||
**** Comprehension | |||
**** Projection | |||
** Use Collaboration to Increase SA | |||
*** Same time and place - face to face | |||
*** Same Time/Different Place - radio/video | |||
*** Different Time/Different Place - email, text | |||
*** Different Time/Same Place - log books, etc. | |||
* Trust and Reliability | |||
** Design a system that is trusted and utilized, but not overly trusted so operators become complacent | |||
*** Provide metrics on reliability so that the operator knows how well they should trust the system | |||
**** Is it raining? | |||
**** How reliable are my sensors? | |||
**** What resolution is the data | |||
**** Number of active satellites | |||
*** Keep metrics frequent and appropriate | |||
**** Don't show old information that doesn't matter anymore | |||
* Role of Automation | |||
** There are different levels of automation (1-10) from operator does nothing to operator is overworked | |||
** The level may need to change depending on the operators effectivity | |||
** Use cuing | |||
*** Environment (time/event) | |||
*** Psychophysiological | |||
*** Performance metrics | |||
* Accountability | |||
** Don't shoot kids! | |||
== Fuzzy Logic Control Systems for Fixed-Wing Drones == | |||
* FLC provide a simpler method for controlling fixed wing aircraft |
Latest revision as of 16:11, 22 September 2015
From Interdrone 2015
People
- Dyan Gibbens - currently owns two drone-based companies. I was very impressed with Dyan and think she could be a valuable consultant in assisting with the regulatory challenges.
- Jim Williams - former FAA employee that wrote much of the new regulation regarding UAVs.
Areas for Further Research
- Dronekit
- DroneCode
- Driden model (?)
- Stereo Vision (see OpenCV)
- OpenCV
- Gimli Glider
- BeagleBoard - Rasberry Pi Competitor
Crowd Analytics through Facial Recognition
Eyeris
Software package called ImoVu - Deep Learning architecture for image processes that imitates human recognition
- Vision for Embedded Systems
- 97% accurate
- 140 FPS on i7 processor
- Ambient Intelligence (AML) - Electronic environments that are sensitive and responsive to the presence of people.
- Minimal required resolution for a face: 50x50px, Ideal 70x70px
- Does not use FACS
- Uses OpenCV open source software to identify faces and for some devices, sometimes falls back to the devices facial recognition
- This software is not useful for things or places (only people)
- Allows for
- Access to Hard to reach places
- Large coverage area
- Eye Level
- Improved angles
- Trained For
- 5 Major ethnic groups
- 5 age groups
- 2 genders
- 10 lightning conditions
- 13 head poses
- supervised and unsupervised
- different camera structures
- different resolution
- different face attributes
- Joy
- Surprise
- Sadness
- Disgust
- Anger
- Fear
- Neutral
3D Mapping
- mark@visuallaw.com
- sketchfab - 3d modeling software
- pointcloud
- DroneDeploy - android app for creating 3d mesh and orthomap ($300 a month)
- Pix4D - the primary software for creating 3 modeling of terrain
- Adjisoft Photoscan (?) - Competitor to pix4d
- Autodesk is working on comparable software
DroneKit
Flying in Wind: State Estimation for UAS
- Douglas Weibel, PHD
- Wind - a vector velocity field with spatial and temporal variation
- Wind has structured (predictive) and random properties (turbulence) that are difficult to model
- State
- Attitude (Roll, Pitch, Yaw, Roll Rate, Pitch Rate, Yaw Rate)
- Attitude Rates
- Position
- Velocity
- Other possible states
- Wind Paramters
- Height over ground
- Battery energy
- gyro bias
- accel bias
- Attitude Estimation
- Stabilized Sub Platform
- Whabba's problem
- Integration
- Combined
- Contempory Sensors
- Gyros
- Accelerometers
- Magnetomers
- GPS Baseline
- Thermopile
- Elecstratic potential
- Opical sensors
- Federated Filter
- Attitude Filter ( Cascaded Filter )
Applications of Rotation Matrices and Matrix-Vector Algebra in Autopilots
Bill Premerlani
- A rotation matrix is a linear homegeneous transformation
- Tensor
FAA Regulations: Past, Present, and Future
- Jim Williams
- Drones are named from the first unmanned aircraft in Britain during WW1
- [1]
- FAA cant regulate anything congress doesn't give them authority over
- Required for an aircraft to be regulated
- Approved Pilot
- Aircraft Certificate
- Code of Federal Regulations (91.113) - you must be able to see and avoid other aircraft (thus-- you need a 333)
- COWA - Certificate of Waiver or Authorization
- AC - 91.57A new AC that regulates RC hobby
- AC's are not regulatory
- CFR 14 91.13 Careless and Reckless Operation
- Aircraft operations for the purpose of air navigation. No person may operate an aircraft in a careless or reckless manner so as to endanger the life or property of another.
- Don't do stupid things that put people in the air at risk.
- 14 CFR Part 107 creates new rules for small unmanned aircraft systems
- Examples
- CNN is looking at tethered aircraft (tether provides power) - Power Electronics 01
- Railroad wants UAV to monitor tracks in front of trains looking for issues with tracks
- Farmers are looking for ways to safely use UAV without LOS by monitoring airspace for incoming commercial aircraft
- FAA - Department of Transportation interactions take a lot of time
- Most insurance policies will not cover accidents caused by commercial use of drones
- Some companies with 333 exemptions are suing companies operating without the proper exemptions
Embedded Computer Vision for Safer, Faster Drones
- Goksel Dedeoglu, Founder PercepTonic, LLC
- Computer vision algorithms analyze images to extract information about the world (allowing machines that perceive)
- Size and shape
- 3d position
- Identity
- Expression
- Location
- Motion
- Illumniation, weather, etc.
- Two things relevant for drone operation:
- Motion
- Depth
- Inertial sensors
- Identifies key points
- GerbilBall is a safety sphere enabled by real-time depth sensing
- Stereo-based
- Has limitations on how close the two objects can be to each other
- Can have as many cameras as needed to create a realtime look in all directions (expensive)
- For objects that don't move often, may be cheaper to build an internal model of what has been seen
- Best guess for power efficient vision computation is heterogeneous and multi-core architechtures
- Examples
- Fire Phone makes good use of computer vision with multiple cameras
- Dyson 360 Roomba
- http://www.cvisioncentral.com/evw2015
- http://www.embedded-vision.com
DroneKit
- A single API that runs onboard drones
- Cloud REST API
- Android SDK ("3DR Services")
- Python SDK
- Dronekit exists as a compatibility layer on top of the autopilot
- Onboard and offboard MAVLink
- Droneshare.com
- REST API (speaks JSON)
- Used for storing fetching displaying logs and other data
- Full autopilot control from mobile
- Built on Android AIDL infrastructure
- Examples
- Tower, DroidPlanner, Tower Pebble, Tower Wear, Solo
- DK Python
- "Solo is the best tool for aerial video".
- Runs on PixHawk 2 (unreleased)
- SDK for Solo dev.3dr.com
- Prototype in Python without low level system knowledge
- Pixhawk
- tim@3dr.com
Structural Inspections
- 6 weeks to 190 days to get 333
- Several of the participants have run into issues with RF interference. One person had a fly away in downtown LA. It was identified as interference caused by a nearby consumer WIFI device. One person recommended an RF analysis as part of the pre-flight.
- Military-grade components have much better shielding and seem to handle interference better.
Human Factors
- Alexander Stimson - alex@autonometrics.com *
- In order to keep from overwhelming the operator
- Multimodal - Use things like sound, or LEDS, to indicate status
- Multiple HSC Tasks = divided attention problem - most people don't multitask well
- Encourage task switching efficiency
- Minimize the switching costs
- Ensure that the operator is focused on the appropriate tasks
- Procedures
- Structure based abstractions - colors, arrows, etc.
- Automation Cueing
- Cues/alerts should be appropriate and not overwhelming. There is a cognitive cost to processing cues. Don't make the problem worse.
- Consider Interruption Lag cueing-- an early warning about something that will eventually become important (the upcoming block in tetris) - studies are not conclusive on effectivity
- Increase role of automation
- Provide stopping rules - let the operator know when to move on
- Humans are not rational decision makers
- Three general classes of heuristics
- Representative - humans are generally insensitive to prior probability (what happened yesterday)
- Anchoring - early assumptions have more weight than new information
- Availability -
- Bias
- Confirmation Bias - once a hypothesis has been made, people tend to try and support it
- Assimilation Bias - once a hypothesis has been made, new evidence tends to be bias towards that hypothesis
- Automation Bias - when you have an autonomous system, people tend to give it too much weight
- Supervisory Monitoring of Operations - System identifies that the operator is overwhelmed
- Use metrics to identify poor performance
- Can be human or automated
- How to handle an overloaded operator
- Notifiy the operator
- Redistribute task load to other team members
- Bring in more team members
- Modify the mission objectives (accept it)
- Distributed Decision Making
- Building Situational Awareness
- Ensley Model
- Perception
- Comprehension
- Projection
- Ensley Model
- Use Collaboration to Increase SA
- Same time and place - face to face
- Same Time/Different Place - radio/video
- Different Time/Different Place - email, text
- Different Time/Same Place - log books, etc.
- Building Situational Awareness
- Trust and Reliability
- Design a system that is trusted and utilized, but not overly trusted so operators become complacent
- Provide metrics on reliability so that the operator knows how well they should trust the system
- Is it raining?
- How reliable are my sensors?
- What resolution is the data
- Number of active satellites
- Keep metrics frequent and appropriate
- Don't show old information that doesn't matter anymore
- Provide metrics on reliability so that the operator knows how well they should trust the system
- Design a system that is trusted and utilized, but not overly trusted so operators become complacent
- Role of Automation
- There are different levels of automation (1-10) from operator does nothing to operator is overworked
- The level may need to change depending on the operators effectivity
- Use cuing
- Environment (time/event)
- Psychophysiological
- Performance metrics
- Accountability
- Don't shoot kids!
Fuzzy Logic Control Systems for Fixed-Wing Drones
- FLC provide a simpler method for controlling fixed wing aircraft