Neuromorphic computing mimics computational principles of the brain in $\textit{silico}$ and motivates research into event-based vision and spiking neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity changes and offer superior power consumption, response latencies, and dynamic ranges. SNNs replicate biological neuronal dynamics and have demonstrated potential as alternatives to conventional artificial neural networks (ANNs), such as in reducing energy expenditure and inference time in visual classification. Nevertheless, these novel paradigms remain scarcely explored outside the domain of aerial robots. To investigate the utility of brain-inspired sensing and data processing, we developed a neuromorphic approach to obstacle avoidance on a camera-equipped manipulator. Our approach adapts high-level trajectory plans with reactive maneuvers by processing emulated event data in a convolutional SNN, decoding neural activations into avoidance motions, and adjusting plans using a dynamic motion primitive. We conducted experiments with a Kinova Gen3 arm performing simple reaching tasks that involve obstacles in sets of distinct task scenarios and in comparison to a non-adaptive baseline. Our neuromorphic approach facilitated reliable avoidance of imminent collisions in simulated and real-world experiments, where the baseline consistently failed. Trajectory adaptations had low impacts on safety and predictability criteria. Among the notable SNN properties were the correlation of computations with the magnitude of perceived motions and a robustness to different event emulation methods. Tests with a DAVIS346 EC showed similar performance, validating our experimental event emulation. Our results motivate incorporating SNN learning, utilizing neuromorphic processors, and further exploring the potential of neuromorphic methods.

翻译：类神经形态计算在硅基系统中模拟大脑的计算原理，推动了事件驱动视觉和脉冲神经网络的研究。事件相机仅捕捉局部亮度变化，具有优越的功耗、响应延迟和动态范围。脉冲神经网络复制了生物神经元的动态特性，已在视觉分类等领域展现出替代传统人工神经网络的潜力，例如降低能耗和推理时间。然而，这些新型范式在无人机领域之外仍鲜有探索。为研究受大脑启发的感知与数据处理方法的实用性，我们开发了一种基于类神经形态的障碍规避方法，并应用于配备相机的机械臂。该方法通过以下步骤实现：将模拟事件数据输入卷积脉冲神经网络进行处理，将神经激活解码为避障运动，并利用动态运动基元调整高层轨迹规划。我们使用Kinova Gen3机械臂在多种任务场景中执行简单抓取任务，并与非自适应基线方法进行对比实验。在基线方法持续失败的仿真与真实实验中，我们的类神经形态方法成功实现了可靠的临近碰撞规避。轨迹调整对安全性和可预测性指标影响较小。脉冲神经网络的显著特性包括：计算量与感知运动幅度相关，以及对不同事件仿真方法具有鲁棒性。使用DAVIS346事件相机的测试结果与实验事件仿真性能一致，验证了方法的有效性。研究结果表明，将脉冲神经网络学习、类神经形态处理器集成并进一步探索类神经形态方法潜力具有重要价值。