Neuromorphic control for optic-flow-based landings of MAVs using the Loihi processor
This enables robust and efficient autonomous skills for constrained robotic systems like MAVs, though it is incremental as it applies an existing neuromorphic method to a new embedded application.
They tackled the reality gap between simulation and real-world deployment of neuromorphic processors on micro air vehicles by implementing a fully embedded Loihi chip for autonomous landing, achieving a root-mean-square error of 0.005 g and over 99.7% spike sequence matching.
Neuromorphic processors like Loihi offer a promising alternative to conventional computing modules for endowing constrained systems like micro air vehicles (MAVs) with robust, efficient and autonomous skills such as take-off and landing, obstacle avoidance, and pursuit. However, a major challenge for using such processors on robotic platforms is the reality gap between simulation and the real world. In this study, we present for the very first time a fully embedded application of the Loihi neuromorphic chip prototype in a flying robot. A spiking neural network (SNN) was evolved to compute the thrust command based on the divergence of the ventral optic flow field to perform autonomous landing. Evolution was performed in a Python-based simulator using the PySNN library. The resulting network architecture consists of only 35 neurons distributed among 3 layers. Quantitative analysis between simulation and Loihi reveals a root-mean-square error of the thrust setpoint as low as 0.005 g, along with a 99.8% matching of the spike sequences in the hidden layer, and 99.7% in the output layer. The proposed approach successfully bridges the reality gap, offering important insights for future neuromorphic applications in robotics. Supplementary material is available at https://mavlab.tudelft.nl/loihi/.