Flying robots are usually rather delicate and require protective enclosures when facing the risk of collision, while high complexity and reduced payload are recurrent problems with collision-resilient flying robots. Inspired by arthropods' exoskeletons, we design a simple, open source, easily manufactured, semi-rigid structure with soft joints that can withstand high-velocity impacts. With an exoskeleton, the protective shell becomes part of the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components (e.g. bamboo skewers) and consumer-grade 3D printers. The result is CogniFly, a sub-250 g autonomous quadcopter that survives multiple collisions at speeds up to 7 m s−1. In addition to its collision-resilience, CogniFly carries sensors that allow it to fly for approx. 17 min without the need of GPS or an external motion capture system, and it has enough computing power to run deep neural network models on-board. This structure becomes an ideal platform for high-risk activities, such as flying in a cluttered environment or reinforcement learning training, by dramatically reducing the risks of damaging its own hardware or the environment. Source code, 3D files, instructions and videos are available (open source license) through the project's website: https://thecognifly.github.io.

One of the key factors for extended autonomy and resilience of battery-powered multirobot systems is their ability to maintain energy sufficiency by recharging when needed. In situations with limited access to charging facilities, robots need to be able to share and coordinate recharging activities, with guarantees that no robot will run out of energy. In this work, we present an approach based on control barrier functions (CBFs) to enforce both energy sufficiency (ensuring that no robot runs out of battery) and coordination constraints (guaranteeing mutual exclusive use of an available charging station) in a mission agnostic fashion. Moreover, we investigate the system capacity in terms of the relation between individual robot properties and the limit on temporal separation requirements within charging cycles. We show physics-based simulation results as well as real robot experiments that demonstrate the effectiveness of the proposed approach.

Connectivity maintenance is crucial for the real world deployment of multi-robot systems, as it ultimately allows the robots to communicate, coordinate and perform tasks in a collaborative way. A connectivity maintenance controller must keep the multi-robot system connected independently from the system’s mission and in the presence of undesired real world effects such as communication delays, model errors, and computational time delays, among others. In this paper we present the implementation, on a real robotic setup, of a connectivity maintenance control strategy based on Control Barrier Functions. During experimentation, we found that the presence of communication delays has a significant impact on the performance of the controlled system, with respect to the ideal case. We propose a heuristic to counteract the effects of communication delays, and we verify its efficacy both in simulation and with physical robot experiments.

One of the key factors for extended autonomy and resilience of multi-robot systems, especially when robots operate on batteries, is their ability to maintain energy sufficiency by recharging when needed. In situations with limited access to charging facilities, robots need to be able to share and coordinate recharging activities, with guarantees that no robot will run out of energy. In this work, we present an approach based on Control Barrier Functions (CBFs) to enforce both energy sufficiency (assuring that no robot runs out of battery) and coordination constraints (guaranteeing mutual exclusive use of an available charging station), all in a mission agnostic fashion. Moreover, we investigate the system capacity in terms of the relation between feasible requirements of charging cycles and individual robot properties. We show simulation results, using a physics-based simulator and real robot experiments to demonstrate the effectiveness of the proposed approach.