Biologically inspired computation for intelligent autonomous exploration

Autonomous operation of unmanned spacecraft, aircraft, and other robotic vehicles has long been a goal for the exploration of faraway, hostile frontiers of space and hard-to-access or dangerous environments on Earth. Robotic capabilities already exist that allow hazard avoidance by smart navigation systems using fast, fault-tolerant, and reliable onboard computing devices that can withstand harsh environments.1, 2 However, systems do not yet exist that are able to employ a sophisticated enough understanding of scientific data to enable trustworthy autonomous decision making based on information learned in situ. For example, NASA’s Mars Exploration Rovers have excellent hazard avoidance capabilities based on perceived terrain properties, but do not have onboard understanding of scientific data capable of recognizing scientifically interesting surface features. The rovers thus cannot autonomously decide to examine interesting science opportunities instead of passing them by based on preprogrammed navigation commands. Autonomous robotic operations are based on the information provided by the data collected on board and provided for decision making. In the case of scientific (as well as surveillance and other) applications this often means extracting, in sufficient detail, relevant information from a mass of complicated highdimensional (multivariate) data. A prime example in space and Earth applications is analysis of hyperspectral imagery, which employs more than the standard three to eight channels, acquired inmost missions for the wealth of information it contains. Detailed analysis of this and other similarly complex data, however, has proved difficult with conventional approaches. Intelligent data interpretation is a core challenge, which in turn requires complex algorithms that can be computationally Figure 1. A simple simulated six-band image with five classes for concept demonstration. Each pixel is a 6D stack vector (a ‘spectrum’). The color blocks at left show how the spectral types, plotted right, are distributed in the image. Class U contains only one pixel. The spectra are offset for clarity.