Relative localization is a key capability for autonomous robot swarms, and it is a substantial challenge, especially for small flying robots, as they are extremely restricted in terms of sensors and processing while other robots may be located anywhere around them in three-dimensional space. In this article, we generalize wireless ranging-based relative localization to three dimensions. In particular, we show that robots can localize others in three dimensions by ranging to each other and only exchanging body velocities and yaw rates. We perform a nonlinear observability analysis, investigating the observability of relative locations for different cases. Furthermore, we show both in simulation and with real-world experiments that the proposed method can be used for successfully achieving various swarm behaviours. In order to demonstrate the method’s generality, we demonstrate it both on tiny quadrotors and lightweight flapping wing robots.

Since humans and robots are increasingly sharing portions of their operational spaces, experimental evidence is needed to ascertain the safety and social acceptability of robots in human-populated environments. Although several studies have aimed at devising strategies for robot trajectory planning to perform safe motion in populated environments, a few efforts have measured to what extent a robot trajectory is accepted by humans. Here, we present a navigation system for autonomous robots that ensures safety and social acceptability of robotic trajectories. We overcome the typical reactive nature of state-of-the-art trajectory planners by leveraging non-cooperative game theory to design a planner that encapsulates human-like features of preservation of a personal space, recognition of groups, sequential and strategized decision making, and smooth obstacle avoidance. Social acceptability is measured through a variation of the Turing test administered in the form of a survey questionnaire to a pool of 691 participants. Comparison terms for our tests are a state-of-the-art navigation algorithm (Enhanced Vector Field Histogram, VFH) and purely human trajectories. While all participants easily recognized the non-human nature of VFH-generated trajectories, the distinction between game-theoretical trajectories and human ones were hardly revealed. Our results mark a strong milestone toward the full integration of robots in social environments.