Multimodal Human Aerobotic Interaction

Multimodal Human Aerobotic Interaction

Ayodeji Opeyemi Abioye (University of Southampton, UK), Stephen D. Prior (University of Southampton, UK), Glyn T. Thomas (University of Southampton, UK), Peter Saddington (Tekever Ltd., UK) and Sarvapali D. Ramchurn (University of Southampton, UK)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-8365-3.ch006

Abstract

This chapter discusses HCI interfaces used in controlling aerial robotic systems (otherwise known as aerobots). The autonomy control level of aerobot is also discussed. However, due to the limitations of existing models, a novel classification model of autonomy, specifically designed for multirotor aerial robots, called the navigation control autonomy (nCA) model is also developed. Unlike the existing models such as the AFRL and ONR, this model is presented in tiers and has a two-dimensional pyramidal structure. This model is able to identify the control void existing beyond tier-one autonomy components modes and to map the upper and lower limits of control interfaces. Two solutions are suggested for dealing with the existing control void and the limitations of the RC joystick controller –the multimodal HHI-like interface and the unimodal BCI interface. In addition to these, some human factors based performance measurement is recommended, and the plans for further works presented.
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Background

A particular class of unmanned aerial vehicles (UAVs) gaining wide popularity with applications cutting across diverse fields, is the small unmanned multirotor aircraft system. The most common application of this system is cost-effective image or data acquisition from remote locations, high altitudes, hazardous environments, or positions that are difficult or more expensive for a human to reach. Another rising application is drone delivery of goods, medical, or military supplies. However, in this research, a particular application of interest for these small multirotor aircraft systems is in aerial robotics. This research focuses on control for these aerial robots. Control for delivery applications or data/image acquisition can be achieved via a combination of manual or automatic control. However, for aerial robotic application, some higher-level control methods may be required. This chapter explores a few relevant control concepts.

Table 1.
Civilian applications of aerial robots
S/No.CategoryApplications
1Aerial Inspection• Rail Network Inspection
• Offshore oil drilling maintenance inspection
2Disaster Relief• Search and rescue operations
• Healthcare supply delivery
3Emergency Services• Law enforcement applications such as portable aerial surveillance
• Firefighting applications
4Entertainment Applications• Commercial Newsgathering
• Filmmaking
• Theatrical entertainment (Murphy et al., 2011)
• Drone racing
5Environmental Application• Wildlife conservation
• Habitat exploration
• Flood monitoring
• Open water monitoring
6Field Application• Aerial Survey & Mapping
• Agricultural applications
7Industrial• Aerial robotic manipulators - flying robotic arms in manufacturing
• Warehouse quick light-goods transportation
• Indoor inspection
8Photography• Selfies
• Sport Videography
• Mountaineering expedition
9Transportation• Drone delivery of goods e.g. Amazon & DHL delivery trials
• Drone rides transporting humans

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