A Low-cost, Safe and Easy-to-Use Flying Platform for Outdoor Robotic Research and Education

Aerial Robotics are interesting and useful tools because they have some intrinsic advantages over ground and water based systems, like the possibility to observe things from above or to locomote without being perturbed by obstacles and rough terrain. However, research in this field is limited by the complexity and expensiveness of existing platforms. To boost the popularity of aerial robotics and provide easier access to it, we present the first steps of developing an autonomous flying platform that is unique in its way of combining simplicity, robustness and ease of operation. This will be accomplished by using only a small set of simple and reliable sensors together with a modular electronic control system. A robust and safe airframe will allow to fly almost everywhere, in particular near universities and research labs. We aim at an aerial testbed which is readily usable out of the box and targeted at a wide range of real-world outdoor experiments

A Minimalist Control Strategy for Small UAVs

Most autopilots of existing Miniature Unmanned Air Vehicles (MUAVs) rely on control architectures that typically use a large number of sensors (gyros, accelerometers, magnetometers, GPS) and a computationally demanding estimation of flight states. As a consequence, they tend to be complex, require a significant amount of processing power and are usually expensive. Many research projects that aim at experiments with one, or even several, MUAVs would benefit from a simpler, potentially smaller, lighter and less expensive autopilot for their flying platforms. In this paper, we present a minimalist control strategy for fixed-wing MUAVs that provides the three basic functionalities of airspeed, altitude and heading turnrate control while only using two pressure sensors and a single- axis rate gyro. To achieve this, we use reactive control loops, which rely on direct feedback from the sensors instead of full state information. In order to characterize the control strategy, it was implemented on a custom-made autopilot. With data recorded during flight experiments, we carried out a statistical analysis of step responses to altitude and turnrate commands as well as responses to artificial perturbations

A Simple and Robust Fixed-Wing Platform for Outdoor Flying Robot Experiments

Aerial Robotics are interesting and useful tools because they have some intrinsic advantages over ground and water based systems, like the possibility to observe things from above or to locomote without being perturbed by obstacles and rough terrain. However, research in this field is limited by the complexity and expensiveness of existing platforms. To boost the popularity of aerial robotics and provide easier access to it, we present the first steps of developing an autonomous flying platform that is unique in its way of combining simplicity, robustness and ease of operation. This will be accomplished by using only a small set of simple and reliable sensors together with a modular electronic control system. A robust and safe airframe will allow to fly almost everywhere, in particular near universities and research labs. We aim at an aerial testbed which is readily usable out of the box and targeted at a wide range of real-world outdoor experiments

Communication-based Swarming for Flying Robots

Swarms of flying robots can be used in disaster areas to autonomously create communication networks for rescuers and victims. Flying robots have the advantage of rapidly overcoming difficult terrain and providing unobstructed wireless communication. To allow for a swarm composed of cheap, transportable and robust robots, we avoid using positioning sensors which typically depend on the environment (GPS, cameras) or are expensive and heavy (lasers, radars). Instead, robot behaviors depend on local communication with robots within transmission range. There currently exists no methodology to design robot controllers resulting in the emergence of desired swarm behaviors. Here, we propose two bio-inspired techniques to overcome this problem. In the first approach, we use artificial evolution as a mean to automatically design simple, efficient and unthought-of controllers for robots. We then reverse-engineer these controllers and reuse the discovered principles in a wide variety of scenarios. In the second approach, we look at the creation, maintenance and evaporation of army-ant pheromone trails during foraging and apply the same principles to the design of robot controllers for the deployment, maintenance and retraction of communication networks

Beat-Based Synchronization and Steering for Groups of Fixed-Wing Flying Robots

Groups of fixed-wing robots can benefit from moving in synchrony to share sensing and communication capabilities, avoid collisions or produce visually pleasing choreographies. Synchronous motion is especially challenging when using fixed-wing robots that require continuous forward motion to fly. For such platforms, performing trajectories with forward speed lower than the minimum speed of the robot can only be achieved by acting on its heading turn rate. Synchronizing such highly dynamical systems would typically require position information and entail frequent sensing and communication among robots within the group. Instead here we propose a simple controller that reacts to regular beats received through wireless transmissions. Thanks to these beats, robot headings synchronize over time. Furthermore, these controllers can easily be parameterized to steer and regulate the global progression speed of groups of robots. Experiments are performed both in simulation and using up to five fixed-wing flying robots

Aerial collective systems

Deployment of multiple flying robots has attracted the interest of several research groups in the recent times both because such a feat represents many interesting scientific challenges and because aerial collective systems have a huge potential in terms of applications. By working together, multiple robots can perform a given task quicker or more efficiently than a single system. Furthermore, multiple robots can share computing, sensing and communication payloads thus leading to lighter robots that could be safer than a larger system, easier to transport and even disposable in some cases. Deploying a fleet of unmanned aerial vehicles instead of a single aircraft allows rapid coverage of a relatively larger area or volume. Collaborating airborne agents can help each other by relaying communication or by providing navigation means to their neighbours. Flying in formation provides an effective way of decongesting the airspace. Aerial swarms also have an enormous artistic potential because they allow creating physical 3D structures that can dynamically change their shape over time. However, the challenges to actually build and control aerial swarms are numerous. First of all, a flying platform is often more complicated to engineer than a terrestrial robot because of the inherent weight constraints and the absence of mechanical link with any inertial frame that could provide mechanical stability and state reference. In the first section of this chapter, we therefore review this challenges and provide pointers to state-of-the-art methods to solve them. Then as soon as flying robots need to interact with each other, all sorts of problems arise such as wireless communication from and to rapidly moving objects and relative positioning. The aim of section 3 is therefore to review possible approaches to technically enable coordination among flying systems. Finally, section 4 tackles the challenge of designing individual controllers that enable a coherent behavior at the level of the swarm. This challenge is made even more difficult with flying robots because of their 3D nature and their motion constraints that are often related to the specific architectures of the underlying physical platforms. In this third section is complementary to the rest of this book as it focusses only on methods that have been designed for aerial collective systems

Reynolds flocking in reality with fixed-wing robots: communication range vs. maximum turning rate

The success of swarm behaviors often depends on the range at which robots can communicate and the speed at which they change their behavior. Challenges arise when the communication range is too small with respect to the dynamics of the robot, preventing interactions from lasting long enough to achieve coherent swarming. To alleviate this dependency, most swarm experiments done in laboratory environments rely on communication hardware that is relatively long range and wheeled robotic platforms that have omnidirectional motion. Instead, we focus on deploying a swarm of small fixed-wing flying robots. Such platforms have limited payload, resulting in the use of short-range communication hardware. Furthermore, they are required to maintain forward motion to avoid stalling and typically adopt low turn rates because of physical or energy constraints. The tradeoff between communication range and flight dynamics is exhaustively studied in simulation in the scope of Reynolds flocking and demonstrated with up to 10 robots in outdoor experiments

Reynolds flocking in reality with fixed-wing robots: communication range vs. maximum turning rate

The success of swarm behaviors often depends on the range at which robots can communicate and the speed at which they change their behavior. Challenges arise when the communication range is too small with respect to the dynamics of the robot, preventing interactions from lasting long enough to achieve coherent swarming. To alleviate this dependency, most swarm experiments done in laboratory environments rely on communication hardware that is relatively long range and wheeled robotic platforms that have omnidirectional motion. Instead, we focus on deploying a swarm of small fixed-wing flying robots. Such platforms have limited payload, resulting in the use of short-range communication hardware. Furthermore, they are required to maintain forward motion to avoid stalling and typically adopt low turn rates because of physical or energy constraints. The tradeoff between communication range and flight dynamics is exhaustively studied in simulation in the scope of Reynolds flocking and demonstrated with up to 10 robots in outdoor experiments

Enabling Large-Scale Collective Systems in Outdoor Aerial Robotics

For many real-life applications such as monitoring, mapping, search-and-rescue or ad-hoc communication networks, fleets of flying robots are expected to out-perform existing solutions. Robots can join forces to cover larger areas in less time, act as efficient communication relays and overcome difficult terrain more easily than ground-based robots. Compared to a single robot, the main advantages of collective systems are robustness, parallelism, flexibility, and the fact that they enable tasks that could not be solved by a single robot. While a multitude of theoretical models for collective operation have already been developed and tested in simulation, they have rarely been validated with physical robots yet. Transition to reality has so far been inhibited because of strong limitations in scalability. Indeed, the cost, safety risk, and number of operators associated with a collective aerial system increase proportionally with the number of robots. Substituting such systems for single robots is therefore unattractive or even impracticable. In previous experimental approaches, a maximum of five physical robots were operated simultaneously in outdoor scenarios, assisted by human backup pilots on the ground. In contrast, most theoretical models rely on a minimum of ten robots to obtain interesting swarm effects. With respect to this discrepancy, we consider here ten robots to be a large-scale system in aerial robotics. In order to scale real collective aerial systems to at least ten robots, we suggest the following paradigm: flying robots must be low-cost, inherently safe, deal with mid-air collisions and require minimal supervision from an operator on the ground, such that the entire system warrants similar cost as well as similarly safe and easy deployment as a single, classical flying robot. Moreover, this would make swarms of flying robots as accessible as wheeled robots that have already been used successfully in larger collective systems. The above criteria can best be addressed with a global, systematic approach on all major design levels, which are the robot's airframe, low-level control, collective supervision and control as well as mid-air collision avoidance. On each level, we identified the bottlenecks related to scaling and developed methods for compliant robot design. Based on a systematic analysis of airframe configurations, a flying-wing is proposed that is made of low-cost, safe and durable foam material and can be deployed everywhere by hand-launch. A model for the dimensioning of such an airframe is presented. For low-level control, a novel minimalist control strategy is proposed, implemented on an inexpensive, custom-made autopilot and validated in field tests. In order to enable robots for collective operation, we suggest to use WiFi communication links and embedded Linux-computers onto which swarm designers can download distributed controllers for collective behaviors directly from their computer simulation. Furthermore, the idea is put forward to enable collective supervision through an operator interface with the modality for direct group-control. Implemented and field-proven, these solutions can serve as guidelines for other developers. Finally, the problem that robots may collide with each other during collective operation has been addressed with a model assessing the collision risk and a distributed strategy for mid-air collision avoidance, validated on real robots. Applying the proposed methodology allowed us to demonstrate the world's first collective system composed of ten autonomous flying robots in outdoor experiments with several different collective behaviors. Robots are low-cost (about 1/10 the cost of commercially available robots), inherently safe (kinetic energy of a medium-sized bird) and can be configured, deployed and supervised by a single operator on the ground. This represents a significant step for the scaling of flying collective systems with respect to the state of the art as well as an unprecedented operator-to-robot ratio