  Emergency happens in human-UAV cooperation, such as criminal activity
tracking and urgent needs for ground assistance. Emergency response usually has
high requirements on the motion control of the multi-UAV system, by maintaining
both the team performance and team behaviors. However When a UAV swarm executes
tasks in a real-world environment, because of real-world factors, such as
system reliability and environmental disturbances, some robots in the swarm
will behave abnormally, such as slow flocking speed, wrong heading direction,
or poor spatial relations. In the meanwhile, incorrect trust between human and
UAV swarm could map the abnormal behavior of faulty robot to the whole swarm
and request a time-consuming intervention from human supervisor, damage the UAV
swarm response for a dynamic task, even evolve to a failure of task because of
accumulated error. To correct reflect the trust between humans and UAV swarm
and rebuild the trust to improve the performance caused by incorrect trust. We
propose a dynamic trust repair model. The dynamic trust model focus on
human-supervisory UAV system which can help UAV swarm to reduce the negative
influence from faulty UAV on the performance of the UAV swarm, get a flexible
reaction and stable human-supervisory UAV task performance. Results show that
trust model could improve the performance of the swarm for dynamic task
response and regain human trust.

Robotics