Projects - User contributions [en]

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-10-24T00:14:09Z

A1744051:

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|113311]]
In prior work a decentralised algorithm was developed to navigate a team of robotic agents through an obstacle course. The algorithm enabled the robotic agents to navigate and arrive simultaneously at multiple static target locations in a two-dimensional (2D) simulation environment. The objective of this project is to expand upon this algorithm by transitioning the algorithm to work for aerial robotic agents and include extensions to the algorithm, such as; making the agents arrive at a pre-specified angle and to allow for moving targets. The project then involves simulating the created algorithm using the SCRIMMAGE simulation software.
== Introduction ==
This project is sponsored by the Defence Science and Technology Group (DST). Students will be working with staff at DST to support Australian Defence capabilities.
[[File:Isometric.png|thumb|Flight trajectories from simulation using matplotlib and Python]]
=== Project team ===
==== Project students ====
* Sebastian Fortuna
* James Roughan

==== Supervisors ====
* Duong Duc Nguyen
* Cheng Chew-Lim
* David Hubczenko (DST)

=== Aim ===
Prior work in the area has analysed a scenario consisting of 4 agents that must travel
towards 4 static objectives in two-dimensions. This has been developed upon in the
following ways:
* Expanded to three dimensions
* Enabled targeting of randomly moving objectives
* Implemented functionality to control the angle of impact in both azimuth and elevation
* Investigated limitations regarding the physical sensors and actuators used to control flight with the SCRIMMAGE toolbox; using a fixed-wing unmanned aerial vehicle (UAV) model.

== Autonomy Algorithm ==
[[File:Angle left cropped.png|thumb|right|Flight trajectories when using angle control to collide to the left of the target]]
The existing algorithm used a proportional navigation
(PN) based approach to guide the agents to the target.
This maintains a constant line-of-sight (LOS) angle
throughout the trajectory, ensuring collision with a
moving target. This contrasts with pursuit guidance
(PG) which acts to minimise the LOS angle so that the
target is directly head-on from the agent. To guide the
agent to collide at a specific impact angle additional
terms were incorporated into the autonomy
calculation. While effective the
resultant trajectories often had the agent moving in
wide arcs where targets left the field of view (FOV) of
the agent. To solve this issue the more rudimentary PG
approach was combined with PN to achieve a dual
algorithm that used PN when the agent was facing
near to the target, but reverted to PG when the target
approached the edge of the FOV. This proved effective
in providing impact angle control in both azimuth and
elevation, while maintaining LOS with the target
throughout the flight.
[[File:Autonomy Equation.png|thumb|centre|400px|Expanded form of autonomy equation]]

== Simulation ==
The results from the SCRIMMAGE
simulator were less productive - while
in the two-dimensional plane, the
drones track and pursue their targets
effectively, they are unable to manage
altitude effectively. This leads to
minimal misses when the agents and
targets are in the same plane, but a
significant number of misses when the
agents start out substantially higher
than their targets. Even the successful
hits often do so after a long
downwards spiral, having reached the
correct x and y coordinates while still
above their targets.
In areas not impacted by this issue, the
algorithm is effective, and capable of
using angle control in the azimuth
plane. This is confirmed to be a
problem with the ability of the
simulated airframe to correctly
respond to the commanded changes in
altitude. These errors are theorised to
be a product either of the PID
controller being incorrectly tuned, or to
the physical model of the UAV being
insufficiently responsive when it comes
to desired changes in altitude.
[[File:Scrimmage start.png|thumb|centre|600px|Top-down view of SCRIMMAGE simulation working effectively in X-Y plane]]
== Project Outcomes ==
As a result of this project a three-dimensional version of the PN and PG guidance laws
was produced, which functions under ideal circumstances to target randomly moving
objectives at specified impact angles. However, this algorithm is less effective in the
context of more realistic simulations. Inspection thus far has not yielded a simple fix,
but it is theorised that further time and effort would result in a solution that better
integrates the hardware and guidance dynamics. Following on from that, this would
be a good base to begin implementing further improvements to the system which had
been planned as potential future works, such as noisy and restricted sensor modules,
or dedicated communication protocols between the agents. These features would
increase the realism of the testing scenario and permit further refinement of the
algorithm for later deployment in physical prototypes.

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-10-24T00:13:37Z

A1744051:

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|113311]]
In prior work a decentralised algorithm was developed to navigate a team of robotic agents through an obstacle course. The algorithm enabled the robotic agents to navigate and arrive simultaneously at multiple static target locations in a two-dimensional (2D) simulation environment. The objective of this project is to expand upon this algorithm by transitioning the algorithm to work for aerial robotic agents and include extensions to the algorithm, such as; making the agents arrive at a pre-specified angle and to allow for moving targets. The project then involves simulating the created algorithm using the SCRIMMAGE simulation software.
== Introduction ==
This project is sponsored by the Defence Science and Technology Group (DST). Students will be working with staff at DST to support Australian Defence capabilities.
[[File:Isometric.png|thumb|Flight trajectories from simulation using matplotlib and Python]]
=== Project team ===
==== Project students ====
* Sebastian Fortuna
* James Roughan

==== Supervisors ====
* Duong Duc Nguyen
* Cheng Chew-Lim
* David Hubczenko (DST)

=== Aim ===
Prior work in the area has analysed a scenario consisting of 4 agents that must travel
towards 4 static objectives in two-dimensions. This has been developed upon in the
following ways:
* Expanded to three dimensions
* Enabled targeting of randomly moving objectives
* Implemented functionality to control the angle of impact in both azimuth and elevation
* Investigated limitations regarding the physical sensors and actuators used to control flight with the SCRIMMAGE toolbox; using a fixed-wing unmanned aerial vehicle (UAV) model.

== Autonomy Algorithm ==
[[File:Angle left cropped.png|thumb|right|250px|Flight trajectories when using angle control to collide to the left of the target]]
The existing algorithm used a proportional navigation
(PN) based approach to guide the agents to the target.
This maintains a constant line-of-sight (LOS) angle
throughout the trajectory, ensuring collision with a
moving target. This contrasts with pursuit guidance
(PG) which acts to minimise the LOS angle so that the
target is directly head-on from the agent. To guide the
agent to collide at a specific impact angle additional
terms were incorporated into the autonomy
calculation. While effective the
resultant trajectories often had the agent moving in
wide arcs where targets left the field of view (FOV) of
the agent. To solve this issue the more rudimentary PG
approach was combined with PN to achieve a dual
algorithm that used PN when the agent was facing
near to the target, but reverted to PG when the target
approached the edge of the FOV. This proved effective
in providing impact angle control in both azimuth and
elevation, while maintaining LOS with the target
throughout the flight.
[[File:Autonomy Equation.png|thumb|centre|400px|Expanded form of autonomy equation]]

== Simulation ==
The results from the SCRIMMAGE
simulator were less productive - while
in the two-dimensional plane, the
drones track and pursue their targets
effectively, they are unable to manage
altitude effectively. This leads to
minimal misses when the agents and
targets are in the same plane, but a
significant number of misses when the
agents start out substantially higher
than their targets. Even the successful
hits often do so after a long
downwards spiral, having reached the
correct x and y coordinates while still
above their targets.
In areas not impacted by this issue, the
algorithm is effective, and capable of
using angle control in the azimuth
plane. This is confirmed to be a
problem with the ability of the
simulated airframe to correctly
respond to the commanded changes in
altitude. These errors are theorised to
be a product either of the PID
controller being incorrectly tuned, or to
the physical model of the UAV being
insufficiently responsive when it comes
to desired changes in altitude.
[[File:Scrimmage start.png|thumb|centre|600px|Top-down view of SCRIMMAGE simulation working effectively in X-Y plane]]
== Project Outcomes ==
As a result of this project a three-dimensional version of the PN and PG guidance laws
was produced, which functions under ideal circumstances to target randomly moving
objectives at specified impact angles. However, this algorithm is less effective in the
context of more realistic simulations. Inspection thus far has not yielded a simple fix,
but it is theorised that further time and effort would result in a solution that better
integrates the hardware and guidance dynamics. Following on from that, this would
be a good base to begin implementing further improvements to the system which had
been planned as potential future works, such as noisy and restricted sensor modules,
or dedicated communication protocols between the agents. These features would
increase the realism of the testing scenario and permit further refinement of the
algorithm for later deployment in physical prototypes.

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-10-24T00:13:06Z

A1744051:

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|113311]]
In prior work a decentralised algorithm was developed to navigate a team of robotic agents through an obstacle course. The algorithm enabled the robotic agents to navigate and arrive simultaneously at multiple static target locations in a two-dimensional (2D) simulation environment. The objective of this project is to expand upon this algorithm by transitioning the algorithm to work for aerial robotic agents and include extensions to the algorithm, such as; making the agents arrive at a pre-specified angle and to allow for moving targets. The project then involves simulating the created algorithm using the Gazebo simulation software.
== Introduction ==
This project is sponsored by the Defence Science and Technology Group (DST). Students will be working with staff at DST to support Australian Defence capabilities.
[[File:Isometric.png|thumb|Flight trajectories from simulation using matplotlib and Python]]
=== Project team ===
==== Project students ====
* Sebastian Fortuna
* James Roughan

==== Supervisors ====
* Duong Duc Nguyen
* Cheng Chew-Lim
* David Hubczenko (DST)

=== Aim ===
Prior work in the area has analysed a scenario consisting of 4 agents that must travel
towards 4 static objectives in two-dimensions. This has been developed upon in the
following ways:
* Expanded to three dimensions
* Enabled targeting of randomly moving objectives
* Implemented functionality to control the angle of impact in both azimuth and elevation
* Investigated limitations regarding the physical sensors and actuators used to control flight with the SCRIMMAGE toolbox; using a fixed-wing unmanned aerial vehicle (UAV) model.

== Autonomy Algorithm ==
[[File:Angle left cropped.png|thumb|right|250px|Flight trajectories when using angle control to collide to the left of the target]]
The existing algorithm used a proportional navigation
(PN) based approach to guide the agents to the target.
This maintains a constant line-of-sight (LOS) angle
throughout the trajectory, ensuring collision with a
moving target. This contrasts with pursuit guidance
(PG) which acts to minimise the LOS angle so that the
target is directly head-on from the agent. To guide the
agent to collide at a specific impact angle additional
terms were incorporated into the autonomy
calculation. While effective the
resultant trajectories often had the agent moving in
wide arcs where targets left the field of view (FOV) of
the agent. To solve this issue the more rudimentary PG
approach was combined with PN to achieve a dual
algorithm that used PN when the agent was facing
near to the target, but reverted to PG when the target
approached the edge of the FOV. This proved effective
in providing impact angle control in both azimuth and
elevation, while maintaining LOS with the target
throughout the flight.
[[File:Autonomy Equation.png|thumb|centre|400px|Expanded form of autonomy equation]]

== Simulation ==
The results from the SCRIMMAGE
simulator were less productive - while
in the two-dimensional plane, the
drones track and pursue their targets
effectively, they are unable to manage
altitude effectively. This leads to
minimal misses when the agents and
targets are in the same plane, but a
significant number of misses when the
agents start out substantially higher
than their targets. Even the successful
hits often do so after a long
downwards spiral, having reached the
correct x and y coordinates while still
above their targets.
In areas not impacted by this issue, the
algorithm is effective, and capable of
using angle control in the azimuth
plane. This is confirmed to be a
problem with the ability of the
simulated airframe to correctly
respond to the commanded changes in
altitude. These errors are theorised to
be a product either of the PID
controller being incorrectly tuned, or to
the physical model of the UAV being
insufficiently responsive when it comes
to desired changes in altitude.
[[File:Scrimmage start.png|thumb|centre|600px|Top-down view of SCRIMMAGE simulation working effectively in X-Y plane]]
== Project Outcomes ==
As a result of this project a three-dimensional version of the PN and PG guidance laws
was produced, which functions under ideal circumstances to target randomly moving
objectives at specified impact angles. However, this algorithm is less effective in the
context of more realistic simulations. Inspection thus far has not yielded a simple fix,
but it is theorised that further time and effort would result in a solution that better
integrates the hardware and guidance dynamics. Following on from that, this would
be a good base to begin implementing further improvements to the system which had
been planned as potential future works, such as noisy and restricted sensor modules,
or dedicated communication protocols between the agents. These features would
increase the realism of the testing scenario and permit further refinement of the
algorithm for later deployment in physical prototypes.

File:Scrimmage start.png

2021-10-24T00:10:40Z

A1744051:

Top-down view of SCRIMMAGE
simulation working effectively in X-Y plane

File:Angle left cropped.png

2021-10-24T00:07:37Z

A1744051:

Flight trajectories when using angle control to collide to the left of the target

File:Angle left.png

2021-10-24T00:06:10Z

A1744051:

Flight trajectories when using angle control to
collide to the left of the target respectively

File:Autonomy Equation.png

2021-10-24T00:03:28Z

A1744051:

Expanded form of autonomy equation

File:Isometric.png

2021-10-23T23:59:03Z

A1744051:

Flight trajectories from
simulation using matplotlib and Python

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-04-06T06:02:57Z

A1744051:

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-04-06T06:02:42Z

A1744051:

Projects:2021s1-13311 Decentralised Control for Cooperative Task Execution in 3D Multi-agent System

2021-04-06T06:01:46Z

A1744051: Created page with "Category:Projects Category:Final Year Projects 113311 Abstract here == Introduction == In prior work a decentralised algorithm was developed to nav..."

[[Category:Projects]]
[[Category:Final Year Projects]]
[[Category:2021s1|113311]]
Abstract here
== Introduction ==
In prior work a decentralised algorithm was developed to navigate a team of robotic agents through a obstacle course. The algorithm enabled the robotic agents to navigate and arrive simultaneously at multiple static target locations in a two-dimensional (2D) simulation environment. The objective of this project is to expand upon this algorithm by transitioning the algorithm to work for aerial robotic agents and include extensions to the algorithm, such as; making the agents arrive at a pre-specified angle and to allow for moving targets. The project then involves simulating the created algorithm using the Gazebo simulation software.

=== Project team ===
==== Project students ====
* Sebastian Fortuna
* James Roughan

==== Supervisors ====
* Duong Duc Nguyen
* Cheng Chew-Lim
* David Hubczenko (DST)

== Background ==