System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization

A complex system for control of swarms of micro aerial vehicles (MAV), in literature also called as unmanned aerial vehicles (UAV) or unmanned aerial systems (UAS), stabilized via an onboard visual relative localization is described in this paper. The main purpose of this work is to verify the possibility of self-stabilization of multi-MAV groups without an external global positioning system. This approach enables the deployment of MAV swarms outside laboratory conditions, and it may be considered an enabling technique for utilizing fleets of MAVs in real-world scenarios. The proposed visual-based stabilization approach has been designed for numerous different multi-UAV robotic applications (leader-follower UAV formation stabilization, UAV swarm stabilization and deployment in surveillance scenarios, cooperative UAV sensory measurement) in this paper. Deployment of the system in real-world scenarios truthfully verifies its operational constraints, given by limited onboard sensing suites and processing capabilities. The performance of the presented approach (MAV control, motion planning, MAV stabilization, and trajectory planning) in multi-MAV applications has been validated by experimental results in indoor as well as in challenging outdoor environments (e.g., in windy conditions and in a former pit mine)

Tracking and sensor coverage of spatio-temporal quantities using a swarm of artificial foraging agents

Using a network of mobile sensors to track and map a dynamic spatio-temporal process in the environment is one of the current challenges in multi-agent systems. In this work, a distributed probabilistic multi-agent algorithm inspired by the bacterium foraging behavior is presented. The novelty of the algorithm lies in being capable of tracking and mapping a spatio-temporal quantity without the need of machine learning, estimation algorithms or future planning. This is unlike most current techniques that rely heavily on machine learning to estimate the distribution as well as the profile of spatio-temporal quantities. The experimental studies carried out in this work show that the algorithm works well by following the concentration gradient of a dynamic plume created under diffusive conditions. Furthermore, the algorithm is inherently capable of finding the source of a diffusive spatio-temporal quantity as well as performing environmental exploration. It is computationally tractable for simple agents, shown to adapt to its environment and can deal successfully with noise in sensor readings as well as in robot dynamics