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📌 July 6 & 7 — 8:00 AM (PDT)
📺 Streamed LIVE on YouTube, Twitter, and Facebook.
✨ Check-out all the info and register on our website https://px4.io/virtual-2020/

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Navigation & Controls [clear filter]
Monday, July 6
 

9:30am PDT

An In-depth Look at the Multicopter Control System Architecture
This session takes a deep dive into the multicopter control system. The current controller architecture will be presented and explained. Block diagrams and the relevant sections in code of each individual controller will be given.

The aim is to provide sufficient information for new developers that would like to design a control system for a multicopter either by tuning the current PID gains, extending and/or modifying the current controllers. To do this, knowledge of the control system architecture is required. This session provides that information to empower developers.

Speakers
avatar for Anton Erasmus

Anton Erasmus

PhD Student, Stellenbosch University



Monday July 6, 2020 9:30am - 9:45am PDT
PRE-RECORDED

10:35am PDT

Overview of multicopter control from sensors to motors
We give an overview of the components within PX4 to control multicopter vehicles from sensor input pipeline over estimation, through cascaded position, attitude, rate control and the output allocation. We can only zoom in on certain selected aspects and are happy to answer additional related questions.

Speakers
avatar for Mathieu Bresciani

Mathieu Bresciani

Flight Control Engineer, Auterion
avatar for Matthias Grob

Matthias Grob

Flight Control Engineer, Auterion
PX4 Maintainer



Monday July 6, 2020 10:35am - 11:05am PDT
LIVE

12:15pm PDT

Field Tests of a Novel Swarm Control Framework
The Raytheon BBN Technologies Unmanned Innovations Lab and teammates SIFT and OSU recently conducted the third planned field exercise of a novel swarm control framework, Command and Control of Aggregate Swarm Tactics (CCAST). The framework envisions controlling and monitoring swarms of up to 250 collaborative unmanned systems. The proposed video will show how we prepared for and successfully deployed over 60 heterogeneous autonomous systems simultaneously to cooperatively explore an urban area and modify their behaviors while interacting with pre-positioned items in the environment.

Speakers
avatar for Kyle Usbeck

Kyle Usbeck

Lead Engineer, Systems & Technology Research
Kyle conducts research and development in collaborative autonomy of unmanned systems.


Monday July 6, 2020 12:15pm - 12:30pm PDT
PRE-RECORDED
 
Tuesday, July 7
 

9:00am PDT

(CANCELED) Extreme level localisation for Swarm UAVs in Indoor Unknown Environment
THIS SESSION IS CANCELED DUE TO UNCONTROLLABLE CIRCUMSTANCES

THANKS FOR UNDERSTANDING



This session will talk about an extreme localization method for swarm UAVs in an Indoor Unknown Environment, with just a monocular camera and a tracking camera. I'll also talk about mapping (primarily for search and rescue purposes) and talk about the algorithm I've developed as part of my mandatory degree requirement.

Speakers
avatar for Nadeem Mohammed Shajahan

Nadeem Mohammed Shajahan

Student Researcher, TKM College of Engineering
I like to play with drones and built real world applications using Deep Learning and Computer Vision.


Tuesday July 7, 2020 9:00am - 9:15am PDT
PRE-RECORDED

11:30am PDT

Autonomous Exploration Path Planning in High-risk Environments using Aerial Robots
In this talk we present a library of exploration path planning algorithms for aerial robots operating in high-risk settings such as underground environments. Given a previously unknown space, the exploration planners provide collision-free trajectories to navigate the space and build a complete map of the environment autonomously. The set of presented planners emphasize different aspects, such as on large-scale narrow-width subterranean environments, saliency-aware exploration, uncertainty-aware navigation and more. Among the multiple planners in the presented set, we will particularly emphasize on a novel graph-based planning framework purposefully built for aerial robots in subterranean environments such as underground mines, metropolitan infrastructure, and caverns. Given their often large-scale topology and complex network of branches, a bifurcated approach consisting of local and global planning layers is designed. The local planner employs a rapidly-exploring random graph to efficiently identify exploration paths within a local subspace. The global planner is developed as another layer to reposition the robot towards other unexplored areas over the global map when the robot reaches a dead-end situation. This planning algorithm is evaluated extensively in a variety of underground mines and bunkers in the U.S. and in Switzerland. The framework is also being used by the CERBERUS team in DARPA Subterranean Challenge which focuses on innovative robotics solutions for exploration and search missions in underground environments (https://www.subtchallenge.com/). Secondly, we will also focus on two multi-objective planners that co-optimize for exploration (extrinsic goal) and the intrinsic goals of localization uncertainty minimization and salient region re-observation. All the planners are interfaced via the Robot Operating System, a subset is open-sourced already, and to a great extent have been tested with PX4-enabled platforms.

A list of demonstration videos from our experiments and field deployments could be found at https://www.youtube.com/playlist?list=PL3qlfAXmuOFIr-m8ScWJXdDO88_feibTq

Software related to the proposed planners that are already open-sourced is available at
- Graph-based Exploration Planner for Subterranean Environments: https://github.com/unr-arl/gbplanner_ros (in progress)
- Motion-primitives Based Planner for Fast & Agile Exploration: https://github.com/unr-arl/mbplanner_ros (in progress)
- Visual Saliency-aware Exploration Planner: https://github.com/unr-arl/rhem_planner
- Uncertainty-aware Receding Horizon Exploration and Mapping: https://github.com/unr-arl/vseplanner 
- Tutorial: https://www.autonomousrobotslab.com/subtplanning.html

Speakers
avatar for Tung Dang

Tung Dang

Graduate Researcher, University of Nevada, Reno
I am Tung Dang, PhD candidate at the University of Nevada, Reno. Currently, my research is focused on the informative path planning for autonomous exploration in large-scale subterranean environments.


Tuesday July 7, 2020 11:30am - 11:45am PDT
PRE-RECORDED

12:15pm PDT

Control Allocation: reworking the PX4 mixing system
In the PX4 stack, the output of the controllers is converted into actuator commands by mixers.
Although very powerful for the insiders, the current mixing system is not user-friendly and is limited to static configurations.In this session, we present ongoing work on control allocation, which aims at complementing or even replacing the current mixing system.
The future control allocation module promises easier and more flexible airframe configuration, while enabling new features such as the continuous control of tilt-rotor transitions, and the handling of actuator failures.

Speakers
avatar for Julien Lecoeur

Julien Lecoeur

R&D Engineer, Flybotix
avatar for Silvan Fuhrer

Silvan Fuhrer

Flight Control Engineer, Auterion
Flight control engineer with a focus on fixed-wing and VTOL vehicles.


Tuesday July 7, 2020 12:15pm - 12:45pm PDT
LIVE

2:30pm PDT

PX4 state estimation update
The talk will be a status and summary progress report on changes made to the PX4 navigation state estimator since the last conference. It will contain a description of the new yaw estimator algorithm that has enabled operation without a magnetomer or external yaw sensor, and enabled automatic recovery from loss of navigation due to bad magnetometer or yaw sensor data.

Speakers
avatar for Paul Riseborough

Paul Riseborough

Developer, GNC Solutions Pty Ltd
https://github.com/priseborough https://www.linkedin.com/in/paul-riseborough-36066039/



Tuesday July 7, 2020 2:30pm - 2:45pm PDT
PRE-RECORDED
 
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