Asgeir Johan Sorensen

Real-world robotic systems must comply with safety requirements in the presence of uncertainty. To define and measure requirement adherence, Signal Temporal Logic (STL) offers a mathematically rigorous and expressive language. However, standard STL cannot account for uncertainty. We address this problem by presenting pacSTL, a framework that combines Probably Approximately Correct (PAC) bounded set predictions with an interval extension of STL through optimization problems on the atomic proposition level. pacSTL provides PAC-bounded robustness intervals on the specification level that can be utilized in monitoring. We demonstrate the effectiveness of this approach through maritime navigation and analyze the efficiency and scalability of pacSTL through simulation and real-world experimentation on model vessels.

Future marine operations objectives and tasks require a paradigm shift from robots to autonomous robotic organizations (AROs). These AROs must have advanced cooperative skills, control capabilities, and resilience both as individuals and as heterogeneous robot teams operating in space and air, on the sea surface, and underwater.

Traditional large Earth Observation Satellites (EOS) are designed with simple imaging strategies. However, small satellites can satisfy immediate user needs through agile imaging modes. In this paper, we model several agile imaging mode concepts and test them experimentally on the push-broom hyperspectral imaging cubesat HYPSO-1. Six imaging modes are tested: slewing, multi-target, dual-angle, wide swath, on-board processing, and dynamic pointing. The results show that the slewing and multi-angle mode increased estimated Signal-to-Noise Ratio (SNR) by 1.4x to 1.7x, the on-board processing mode decreased data latency, and the multi-target, wide swath and dynamic pointing modes enhanced spatial coverage.

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.

Operations of multiagent systems consisting of autonomous underwater vehicles (AUVs) and autonomous surface vessels (ASVs) offer a cost-effective solution to a wide range of marine applications, such as mapping and monitoring of the oceans. This article proposes a switching controller for tracking one or multiple AUVs with an ASV. Using three control modes, the controller ensures that: 1) each AUV eventually gets tracked and aided; 2) collisions with the AUVs are avoided; and 3) the ASV stops active propulsion whenever practical in order to conserve energy and reduce acoustic noise caused by the propulsion system. The switching of modes is based on the relative geometry between the ASV and the AUVs and backward reachable sets (BRSs). The switching controller is experimentally demonstrated with satisfying results in a series of field experiments in the Trondheim Fjord, where up to three AUVs executing missions simultaneously are tracked and aided by one ASV.