Holistic Risk Modeling and Path Planning for Marine Robotics

Abstract

Risk awareness and assessment are fundamental aspects of human cognition and situational awareness, and play crucial roles in problem solving and decision-making. In this article, we present a novel methodology for integrated risk modeling and path planning in robotics mimicking these human processes. This approach creates a holistic geospatial data structure of risk, showing what may go wrong, where and when it is more likely, and the potential causes and consequences; all of which may be used as input to planning and decision-making algorithms for improved robotic autonomy. First, a hazard analysis of the operation is performed, with the objective of analyzing possible hazardous events, their causal factors, and potential consequences. This knowledge is then incorporated into a Bayesian belief network for estimating the risk at a particular point in space and time. Two methods for path planning taking these results as input are proposed: first, the risk-based path planner, and second, the risk-based traveling salesperson, both of which can balance the tradeoffs between risk and reward related to the mission objectives. We demonstrate the novel methodology with a real case study: seabed survey of the Tautra coral reef in Norway using an autonomous underwater vehicle (AUV), capitalizing on data from previous field operations. The case study shows that the AUV adapts its mission based on the perceived and assessed risk. By combining methods from robotics, artificial intelligence, risk science, and geoinformatics this work provides an interdisciplinary and novel contribution to enhanced robotic autonomy.