Projects:2019s2-23501 Multi-Drone Tracking and Formation Control Platform

This paper explores solutions to real world problems in the domain of multi-drone control and formation through the development of a generalised platform for indoor UAVs in a confined space. The implementation of the platform is detailed through progressive development and justifies key decisions during development. This wiki details the implementation of the multi-drone platform with a particular focus on the collision avoidance module and rigidbody motion tracking. Dynamic obstacle avoidance is addressed through an artificial potential fields approach with adjustments for various known shortcomings. Reliable rigidbody streaming is achieved through BLANK

Introduction

Through the rise of multi-drone technology over the past decade, key challenges have hindered the ability of researchers to innovate and provide generalised solutions. In particular, the area of UAVs has expanded to various industries including defense, consumer services, agriculture, and transport. Through the development of a generalised platform for indoor UAVs in a confined space, issues challenging multi-drone research will be resolved.

Project team

Project students

• Jacob Stollznow
• Alexander Woolfall

Supervisors

• Dr. Hong Gunn Chew
• Dr. Duong Duc Nguyen

Motivation

A key issue in multi-drone development is the unique implementation and operation protocols involved in the use of each drone type, adding unnecessary overhead. This labour intensive task translates to the development of algorithms with unique interfaces and control schemes dependent on the drone type. The task of implementing any given algorithm across a range of drones does not advance the researchers work. This results in researchers developing sophisticated multi-drone algorithms, backed by theoretical reasoning and simulation results, with no evidence of operation in a generalised physical environment with various drone types.

Another shortcoming in multi-drone development is the lack of usability by any type of user, regardless of their experience flying UAVs. Drones are difficult to control due to the many degrees of freedom and flight parameters, overwhelming users and algorithms alike. This creates a hazardous environment in which the user, obstacles or other drones could be damaged. The safety concerns of flying drones in a physical environment is another contributing factor which limits researchers. Safety constraints are tedious to implement for each algorithm but are necessary to avoid potential damage to expensive drone hardware. Physical constraints can be used to limit damage to the user, but do little to prevent drone-obstacle or drone-drone collisions.

These key issues result in researchers developing multi-drone algorithms with very limited physical testing. In addressing the challenges detailed, researchers would be able to offer complete solutions to real world multi-drone applications supported by safe and generalised physical environment testing with minimal implementation adjustments.

Significance

The solution to address the key issues will affect two target audiences, researchers and general users. Through the implementation of a solution to the solve the use of different platforms for various drone types, researchers will be able to implement a general solution applicable to all drone types. This will reduce the overhead associated with the separate implementations and also the preparation time required for each unique platform. Collision avoidance management will reduce the user implementation required to ensure drones are not damaged in physical testing scenarios. In which case, researchers are able to focus their energy on the implementation of their algorithms rather than implementing their own mechanisms to manage potential collision situations. Through collision management the risks associated with multi-drone operation would be significantly reduced, translating a hazardous high risk physical environment to an environment operatable by users ranging from trained pilots to high school students.

Objectives

The objectives were developed considering the motivation and specify the essential and desired features of the multi-drone platform --

Required

• Integration of more than one drone type.
• Generalised and robust API to control all drone types as a swarm or individually. The API must support MATLAB.
• Collision avoidance management for drones.
• Researcher-specific features, such as extensive feedback and visualisation with intuitive control of drones.

Extension

• API support in Python and C++.
• Reduction in setup and preparation time as is required for each drone implementation.

References

[1] a, b, c, "Simple page", In Proceedings of the Conference of Simpleness, 2010.

[2] ...