Antonio Sgorbissa

A Minimalistic Quadrotor Navigation Strategy for Indoor Multi-floor Scenarios

This paper presents the application of a minimalistic navigation strategy, based on the well-known BUG2 algorithm, to solve the problem of reaching a goal position in a multi-floor indoor scenario using a quadrotor. Examples of this scenario include buildings and in general cluttered indoor areas. As far as energy backup is concerned the quadrotor shows stricts constraints: for this reason implementing a low-consumption navigation strategy is a major issue. We present a two-layer navigation strategy, called MF-BUG2, useful to navigate in multi-floor buildings starting from the ground floor toward the last or vice versa while searching for an interesting physical quantity (i.e,. gas leak, electromagnetic source). In the lower layer a BUG-like algorithm is able to drive the flying robot, equipped with a salient-cue sensor and a laser-range-finder, toward the estimated position of goal on the horizontal plane while avoiding obstacles and using minimal computational power and memory (the boundary-following behavior uses an Artificial Potential Field to navigate around the obstacles). If the estimated goal position is reached but the salient-cue-sensor does not detect a salient quantity the higher level of the planner calls Dijkstra algorithm to computes the minimum-distance path to change the floor, assuming to know in advance the 2D position of the passages among different floors, and then moves vertically. The overall strategy is usefull for indoor inspection in hazardous scenarios. The algorithm is validated in simulation, investigating the robustness with respect to the laser-range-finder noise.

Chapter 3.3 – Planning and Obstacle Avoidance in Mobile Robotics1

This chapter focuses on the navigation subsystem of a mobile robot which operates in human environments to carry out different tasks, such as transporting waste in hospitals or escorting people in exhibitions. The chapter describes a hybrid approach (Roaming Trails), which integrates a priori knowledge of the environment with local perceptions in order to carry out the assigned tasks efficiently and safely: that is, by guaranteeing that the robot can never be trapped in deadlocks even when operating within a partially unknown dynamic environment. This chapter includes a discussion about the properties of the approach, as well as experimental results recorded during real-world experiments.