Recent advances in robotics bring us closer to the reality of living, co-habiting, and sharing personal spaces with robots. However, it is not clear how close a co-located robot can be to a human in a shared environment without making the human uncomfortable or anxious. This research aims to map safe and comfortable zones for co-located aerial robots. The objective is to identify the distances at which a drone causes discomfort to a co-located human and to create a map showing no-fly, moderate-fly, and safe-fly zones. We recruited a total of 18 participants and conducted two indoor laboratory experiments, one with a single drone and the other set with two drones. Our results show that multiple drones cause more discomfort when close to a co-located human than a single drone. We observed that distances below 200 cm caused discomfort, the moderate fly zone was 200 - 300 cm, and the safe-fly zone was any distance greater than 300 cm in single drone experiments. The safe zones were pushed further away by 100 cm for the multiple drone experiments. In this paper, we present the preliminary findings on safe-fly zones for multiple drones. Further work would investigate the impact of a higher number of aerial robots, the speed of approach, direction of travel, and noise level on co-located humans, and autonomously develop 3D models of trust zones and safe zones for co-located aerial swarms.

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of predictive formal modelling on performance and human-swarm interaction. 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any time step by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human And Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators’ workload or affecting the system's usability.

This paper discusses the research work conducted to quantify the effective range of lighting levels and ambient noise levels in order to inform the design and development of a multimodal speech and visual gesture (mSVG) control interface for the control of a UAV. Noise level variation from 55 dB to 85 dB is observed under control lab conditions to determine where speech commands for a UAV fails, and to consider why, and possibly suggest a solution around this. Similarly, lighting levels are varied within the control lab condition to determine a range of effective visibility levels. The limitation of this work and some further work from this were also presented.

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. In this paper, we use predictive formal modelling (PFM) at runtime in a human-swarm mission and show that this integration can be used to improve the performance of human-swarm teams. We recruited 60 participants to operate a simulated aerial swarm to deliver parcels to target locations. In the PFM condition, operators were informed of the estimated completion times given the number of drones deployed, whereas, in the No-PFM condition, operators did not have this information. The operators could control the mission by adding or removing drones from the mission and thereby, increasing or decreasing the overall mission cost. The evaluation of human-swarm performance relied on four metrics: the task completion time, the number of agents, the number of completed tasks, and the cost per task. Our results show that PFM modelling at runtime improves mission performance without significantly affecting the operator's workload or the system's usability.