Kimberly McGuire

TL;DR: The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors and this method allows the MAV to control its velocity and avoid obstacles.

TL;DR: The swarm gradient bug algorithm (SGBA) as mentioned in this paper maximizes coverage by having robots travel in different directions away from the departure point, and then perform a gradient search toward a home beacon.

TL;DR: In this article, a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth is presented. But it is not suitable for flying in indoor environments, and autonomous navigation is challenging due to their strict hardware limitations.

TL;DR: This work presents a review and discussion of the challenges that must be solved in order to successfully develop swarms of Micro Air Vehicles (MAVs) for real world operations, and extracts constraints and links that relate the local level MAV capabilities to the global operations of the swarm.

TL;DR: In this article, the authors present a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation, and compare a selection of bug algorithms in a simulated robot and environment where they endure different types of noise and failure cases of their on-board sensors.