Micro Air Vehicle Laboratory, Faculty of Aerospace Engineering, TU Delft, Delft, Netherlands.
Autonomous Insect Robotics Laboratory, Department of Mechanical Engineering and Paul G. Allen School of Computer Science, University of Washington, Seattle, WA, USA.
Sci Robot. 2022 Jun 15;7(67):eabl6334. doi: 10.1126/scirobotics.abl6334.
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.
自主机器人有望在复杂、未知的环境中执行各种复杂的任务。然而，现有的板载计算能力和算法代表了实现更高自主性水平的一个相当大的障碍，特别是随着机器人变得更小，摩尔定律的终结临近。在这里，我们认为，从昆虫智能中获得灵感是一种有前途的替代经典机器人方法的方法，这种方法需要用于小型移动机器人自主性的人工智能。昆虫智能的优势源于其资源效率（或简约性），特别是在功率和质量方面。首先，我们讨论了支持这种简约性的昆虫智能的主要方面：体现、感觉-运动协调和群集。然后，我们评估了昆虫启发式人工智能作为替代其他方法的地位，这些方法对于导航等重要机器人任务很重要，并确定了在更广泛采用它的道路上的开放挑战。最后，我们反思了适合实现昆虫启发式人工智能的处理器类型，从传统的微控制器和现场可编程门阵列到非常规的神经形态处理器。我们认为，即使对于神经形态处理器，也不应该简单地应用现有的人工智能算法，而是应该利用自然昆虫智能的见解，为机器人自主性获得最大效率的人工智能。
