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