AI for Autonomous Robots (AMR)

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.

Fig. 1. Insect intelligence is characterized by parsimonious solutions to achieve successful behavior in complex, dynamic, and sometimes hostile environments.

Autonomous mobile robots, such as drones, rovers, and legged robots, promise to perform a wide range of tasks, from autonomously monitoring crops in greenhouses to last-kilometer delivery. These applications require robots to operate for extended periods while performing complex tasks, often in unknown, changing, and complicated environments. This brings great challenges (1), among which is the difficulty of executing a rich repertoire of autonomous, robust, and adaptive behaviors with onboard resources. This challenge is exemplified by the task of navigation.

The state of the art typically relies on simultaneous localization and mapping (SLAM) algorithms, which require more computational resources than can be mustered by many processors embedded onboard robots (2). More than 10 years ago, it was reasonable to anticipate that further improvements to microprocessors would soon close this performance gap.

At that time, processor development still kept pace with Moore’s law, which predicted a doubling of the number of transistors in a dense integrated circuit about every 2 years. However, with the end of Moore’s law in sight (3, 4), we can no longer count on this. Hence, we need to explore alternative approaches to both the computing hardware and the AI of small, autonomous robots… Read More

Source: Science Robotics