Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor

Dupeyroux, Julien; Hagenaars, Jesse; Paredes-Vallés, Federico; Croon, Guido de

doi:10.1109/icra48506.2021.9560937

Cited by 32 publications

(14 citation statements)

References 26 publications

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“…This scheme based on frequency shows a better performance than other strategies previously introduced (Dupeyroux et al, 2021 ; Guo et al, 2021 ) since it demonstrates good noise mitigation capacity employing only one neuron.…”

Section: Proposed Methodology For a Neural Filter Designmentioning

confidence: 81%

Neuromorphic Signal Filter for Robot Sensoring

et al. 2022

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Noise management associated with input signals in sensor devices arises as one of the main problems limiting robot control performance. This article introduces a novel neuromorphic filter model based on a leaky integrate and fire (LIF) neural model cell, which encodes the primary information from a noisy input signal and delivers an output signal with a significant noise reduction in practically real-time with energy-efficient consumption. A new approach for neural decoding based on the neuron-cell spiking frequency is introduced to recover the primary signal information. The simulations conducted on the neuromorphic filter demonstrate an outstanding performance of white noise rejecting while preserving the original noiseless signal with a low information loss. The proposed filter model is compatible with the CMOS technology design methodologies for implementing low consumption smart sensors with applications in various fields such as robotics and the automotive industry demanded by Industry 4.0.

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Section: Proposed Methodology For a Neural Filter Designmentioning

confidence: 81%

Neuromorphic Signal Filter for Robot Sensoring

et al. 2022

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“…In the last few years, considerable progress has been made by leveraging data-driven algorithms [9,22,35,86] and novel sensors as event-based cameras [36,87], that provide a high dynamic range, low latency, and low battery consumption [88]. A major opportunity for future work is to complement the existing capabilities of Agilicious with novel compute devices such as the Intel Loihi [89][90][91] or SynSense Dynap [92] neuromorphic processing architecture, which are specifically designed to operate in an event-driven compute scheme. Due to the modular nature of Agilicious, individual software components can be replaced by these novel computing architectures, supporting rapid iteration and testing.…”

Section: Discussionmentioning

confidence: 99%

Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight

et al. 2022

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Autonomous, agile quadrotor flight raises fundamental challenges for robotics research in terms of perception, planning, learning, and control. A versatile and standardized platform is needed to accelerate research and let practitioners focus on the core problems. To this end, we present Agilicious, a codesigned hardware and software framework tailored to autonomous, agile quadrotor flight. It is completely open source and open hardware and supports both model-based and neural network–based controllers. Also, it provides high thrust-to-weight and torque-to-inertia ratios for agility, onboard vision sensors, graphics processing unit (GPU)–accelerated compute hardware for real-time perception and neural network inference, a real-time flight controller, and a versatile software stack. In contrast to existing frameworks, Agilicious offers a unique combination of flexible software stack and high-performance hardware. We compare Agilicious with prior works and demonstrate it on different agile tasks, using both model-based and neural network–based controllers. Our demonstrators include trajectory tracking at up to 5 g and 70 kilometers per hour in a motion capture system, and vision-based acrobatic flight and obstacle avoidance in both structured and unstructured environments using solely onboard perception. Last, we demonstrate its use for hardware-in-the-loop simulation in virtual reality environments. Because of its versatility, we believe that Agilicious supports the next generation of scientific and industrial quadrotor research.

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“…There is an increasing number of examples that show the potential of these processors both in terms of energy expenditure and in execution speed. For instance, in (188), an SNN composed of only 35 spiking neurons controlled a flying robot for performing optic flow landings, with the controller running onboard the Loihi neuromorphic processor at 265 kHz. In (189), an on-chip SNN model of a proportional, integrative, derivative (PID) controller was used to control a 1-DOF (degree of freedom) quadrotor arm at 1 kHz, with an average 0.0126 mW power consumption per time stamp for a total of 40,000 neurons.…”

Section: Of 11mentioning

confidence: 99%

Insect-inspired AI for autonomous robots

et al. 2022

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Autonomous robots are expected to perform a wide range of sophisticated tasks in complex, unknown environments. However, available onboard computing capabilities and algorithms represent a considerable obstacle to reaching higher levels of autonomy, especially as robots get smaller and the end of Moore’s law approaches. Here, we argue that inspiration from insect intelligence is a promising alternative to classic methods in robotics for the artificial intelligence (AI) needed for the autonomy of small, mobile robots. The advantage of insect intelligence stems from its resource efficiency (or parsimony) especially in terms of power and mass. First, we discuss the main aspects of insect intelligence underlying this parsimony: embodiment, sensory-motor coordination, and swarming. Then, we take stock of where insect-inspired AI stands as an alternative to other approaches to important robotic tasks such as navigation and identify open challenges on the road to its more widespread adoption. Last, we reflect on the types of processors that are suitable for implementing insect-inspired AI, from more traditional ones such as microcontrollers and field-programmable gate arrays to unconventional neuromorphic processors. We argue that even for neuromorphic processors, one should not simply apply existing AI algorithms but exploit insights from natural insect intelligence to get maximally efficient AI for robot autonomy.

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Neuromorphic control for optic-flow-based landing of MAVs using the Loihi processor

Cited by 32 publications

References 26 publications

Neuromorphic Signal Filter for Robot Sensoring

Neuromorphic Signal Filter for Robot Sensoring

Agilicious: Open-source and open-hardware agile quadrotor for vision-based flight

Insect-inspired AI for autonomous robots

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