Human-Comfortable Collision Free Navigation for Personal Aerial Vehicles

Semi- or fully-autonomous Personal Aerial Vehicles (PAVs) are currently studied and developed by public and private organizations as a solution for traffic congestion. While optimal collision-free navigation algorithms have been proposed for autonomous robots, trajectories and accelerations for PAVs should also take into account human comfort. In this work, we propose a reactive decentralized collision avoidance strategy that incorporates passenger physiological comfort based on the Optimal Reciprocal Collision Avoidance strategy [1]. We study in simulation the effects of increasing PAV densities on the level of comfort, on the relative flight time and on the number of collisions per flight hour and demonstrate that our strategy reduces collision risk for platforms with limited dynamic range. Finally, we validate our strategy with a swarm of 10 quadcopters flying outdoors

Rapid Evolution of Robot Gaits

The promise of Evolutionary Robotics (ER) to completely automatize the design of robot controllers and/or morphologies is an idea with great appeal not only to researchers, but also to students. However, when attempting to grab and hold student interest, the large requirements of time and computational resources required to achieve good results in ER systems may be discouraging. In fact, after two years of using our RoboGen evolutionary robotics system for class projects, the biggest student complaints all concerned the slow speed of evolutionary progress. In order to overcome these limitations, we investigate a simple and effective technique for rapidly evolving robot gaits in a manner of seconds or minutes rather than hours or days. We rely on two basic techniques to speed up evolution: Compositional Pattern Producing Network (CPPN) encodings and simple parameterized oscillator neurons. When combined with a previously executed iterative tuning procedure, many of these evolved gaits can be transferred to real robots with reasonable fidelity

Dynamic Routing for Flying Ad Hoc Networks

This paper reports experimental results on self-organizing wireless networks carried by small flying robots. Flying ad hoc networks (FANETs) composed of small unmanned aerial vehicles (UAVs) are flexible, inexpensive and fast to deploy. This makes them a very attractive technology for many civilian and military applications. Due to the high mobility of the nodes, maintaining a communication link between the UAVs is a challenging task. The topology of these networks is more dynamic than that of typical mobile ad hoc networks (MANETs) and of typical vehicle ad hoc networks (VANETs). As a consequence, the existing routing protocols designed for MANETs partly fail in tracking network topology changes. In this work, we compare two different routing algorithms for ad hoc networks: optimized link-state routing (OLSR), and predictive-OLSR (P-OLSR). The latter is an OLSR extension that we designed for FANETs; it takes advantage of the GPS information available on board. To the best of our knowledge, P-OLSR is currently the only FANET-specific routing technique that has an available Linux implementation. We present results obtained by both Media Access Control (MAC) layer emulations and real-world experiments. In the experiments, we used a testbed composed of two autonomous fixed-wing UAVs and a node on the ground. Our experiments evaluate the link performance and the communication range, as well as the routing performance. Our emulation and experimental results show that P-OLSR significantly outperforms OLSR in routing in the presence of frequent network topology changes

Bioinspired morphing wings for extended flight envelope and roll control of small drones

Small-winged drones can face highly varied aerodynamic requirements, such as high manoeuvrability for flight among obstacles and high wind resistance for constant ground speed against strong headwinds that cannot all be optimally addressed by a single aerodynamic profile. Several bird species solve this problem by changing the shape of their wings to adapt to the different aerodynamic requirements. Here, we describe a novel morphing wing design composed of artificial feathers that can rapidly modify its geometry to fulfil different aerodynamic requirements. We show that a fully deployed configuration enhances manoeuvrability while a folded configuration offers low drag at high speeds and is beneficial in strong headwinds. We also show that asymmetric folding of the wings can be used for roll control of the drone. The aerodynamic performance of the morphing wing is characterized in simulations, in wind tunnel measurements and validated in outdoor flights with a small drone

Testbed for Fast-Deployable Flying WiFi Networks

EPFL's Information Processing Group (IPG) and Laboratory for Intelligent Systems (LIS), in conjunction with SenseFly (a LIS spin-off) recently started a project aimed at developing a testbed to experiment with self-organized wireless networks carried by autonomous unmanned aircrafts. The idea is to use drones developed by SenseFly to carry the infrastructure of a self-organized WiFi network for easy and rapid deployment. The network can be used to connect people on the ground (e.g. rescue people in case of catastrophe) and/or to send back to a data center the information collected by sensors. The sensors might also be carried by some of the drones. The drones have a high degree of autonomy. In particular, they are capable of carrying out a missions and land without human intervention. In this work we present the state of this ongoing project that involves many challenges, including resource management, mobility management, self-organization, and scalability

Extension of a Ground Control Interface for Swarms of Small Drones

Although the technology for fully autonomous swarms of robots is rapidly progressing, the human operator will continue to play an important role during any swarming mission due to safety, monitoring and control constraints. In this paper, we present the set of features that a Ground Control Interface (GCI) must incorporate to allow monitoring, control and safety of outdoor missions with a swarm of Small Drones (drones of less than 1kg). We extend a widely used GCI by incorporating those features and we demonstrate its usage on a swarm of 10 Small Drones flying outdoor

Distributed Formation Control of Fixed Wing Micro Aerial Vehicles for Uniform Area Coverage

Teams of fixed wing micro-aerial vehicles (MAVs) could provide a wide area coverage and relay data in wireless ad-hoc networks. In such applications fixed wing MAVs have to be able to regulate an inter-robot distance. Fixed wing MAVs have reduced maneuverability, that is, they cannot perform sharp turns or hover on the spot. This kinematic property presents the main challenge to design a formation algorithm that will regulate inter-MAV distance and cover the desired area. In this paper we present a distributed control strategy that is based on attraction and repulsion between MAVs and relies only on local information. We show in simulation and in field experiments with a team of fixed wing MAVs that using our strategy MAVs can uniformly cover an area and regulate communication link quality between neighboring MAVs

Evaluation of control strategies for fixed-wing drones following slow-moving ground agents

There are many situations where fixed-wing drones may be required to track ground moving agents, such as humans or cars, which are typically slower than drones. Some control strategies have been proposed and validated in simulations using the average distance between the target and the drone as a performance metric. However, besides the distance metric, energy expenditure of the flight also plays an important role in assessing the overall performance of the flight. In this paper, we propose a new methodology that introduces a new metric (energy expenditure), we compare existing methods on a large set of target motion patterns and present a comparison between the simulation and field experiments on proposed target motion patterns. Using this new methodology we examine the performance of three control strategies: the Lyapunov Guidance Vector Field strategy, the Bearing-only strategy and the Oscillatory strategy. Among the three strategies considered, we demonstrate that the Lyapunov Guidance Vector Field strategy has the best performance for all target motion patterns. Field experiments with fixed-wing drones provide additional insights into the benefits and shortcomings of each strategy in practice