A key problem in cooperative robotics is the maintenance of a geometric configuration during movement. As a solution for this, a multi-layered and distributed control system is proposed for the swarm of drones in the formation of hierarchical levels based on the leader–follower approach. The complexity of developing a large system can be reduced in this way. To ensure the tracking performance and response time of the ensemble system, nonlinear and linear control designs are presented; (a) Sliding Mode Control connected with a Proportional-Derivative controller and (b) Linear Quadratic Regular with integral action respectively. The safe travel distance strategy for collision avoidance is introduced and integrated into the control designs for maintaining the hierarchical states in the formation. Both designs provide a rapid adoption with respect to their settling time without introducing oscillations for the dynamic flight movement of vehicles in the cases of (1) nominal, (2) plant-model mismatch, and (3) external disturbance inputs. Also, the nominal settling time of the swarm is improved by 44% on average when using the nonlinear method as compared to the linear method. Furthermore, the proposed methods are fully distributed so that each UAV autonomously performs the feedback laws in order to achieve better modularity and scalability.

In this paper, the reconfiguration of swarms of unmanned aerial vehicles after simultaneous failures of multiple nodes is considered. The objectives of the post-failure reconfiguration are to provide collision avoidance and smooth energy-efficient movement. To incorporate such a mechanism, three different failure recovery algorithms are proposed namely thin-plate spline, distance- and time-optimal algorithms. These methods are tested on six swarms, with two variations on failing nodes for each swarm. Simulation results of reconfiguration show that the execution of such algorithms maintains the desired formations with respect to avoiding collisions at run-time. Also, the results show the effectiveness concerning the distance travelled, kinetic energy, and energy efficiency. As expected, the distance-optimal algorithm gives the shortest movements, and the time-optimal algorithm gives the most energy-efficient movements. The thinplate spline is also found to be energy-efficient and has less computational cost than the other two proposed methods. Despite the suggested heuristics, these are combinatorial in nature and might be hard to use in practice. Furthermore, the use of the regularization parameter λ in thin-plate spline is also investigated, and it is found that too large values on λ can lead to incorrect locations, including multiple nodes on the same location. In fact, it is found that using λ = 0 worked well in all cases.

In this paper, we present a HRI study that reports on the potential of NAO as a socially assistive robot in Pakistan. Our findings generated through interviewing 2 parents and 5 teachers on the plausibility of using NAO robot as an interaction partner show that both groups welcomed the use of NAO at schools. They, however, were sceptical due to missing NAO’s facial expressions and certain body parts such as nose and lips. They also emphasised the importance of creating natural text to speech interface for the Urdu Language. Our findings taken from 7 autistic children to measure their level of social interaction during HRI revealed that children positively engaged with the NAO robot and showed a significant number of both verbal and non-verbal behaviours.