Autonomous System Design and Controls Design for Operations in High Risk Environments

Abstract

Autonomous systems operating in dangerous and hard-to-reach environments such as defense systems deployed into enemy territory, petroleum installations running in remote arctic and off-shore environments, or space exploration systems operating on Mars and further out in the solar system often are designed with a wide operating envelope and deployed with control systems that are designed to both protect the system and complete mission objectives, but only when the on-the-ground environment matches the expected and designed for environment. This can lead to overly conservative operating strategies such as preventing a rover on Mars from exploring a scientifically rich area due to potential hazards outside of the original operating envelope and can lead to unanticipated failures such as the loss of underwater autonomous vehicles operating in Earth’s oceans. This paper presents an iterative method that links computer simulation of operations in unknown and dangerous environments with conceptual design of systems and development of control system algorithms. The Global to Local Path Finding Design and Operation Exploration (GLPFDOE) method starts by generating a general mission plan from low resolution environmental information taken from remote sensing data (e.g.: satellites, plane fly-overs, telescope observations, etc.) and then develops a detailed path plan from simulated higher-resolution data collected “in situ” during simulator runs. GLPFDOE attempts to maximize system survivability and scientific or other mission objective yield through iterating on control system algorithms and system design within an in-house-developed physics-based autonomous vehicle and terrain simulator. GLPFDOE is best suited for autonomous systems that cannot have easy human intervention during operations such as in the case of robotic exploration reaching deeper into space where communications delays become unacceptably large and the quality of a priori knowledge of the environment becomes lower fidelity. Additionally, in unknown extraterrestrial environments, a variety of unexpected hazards will be encountered that must to be avoided and areas of scientific interest will be found that must be explored. Existing exploratory platforms such as the Mars Exploratory Rovers (MERs) Curiosity and Opportunity either operate in environments that are sufficiently removed from immediate danger or take actions slowly enough that the signal delay between the system and Earth-based operators is not too great to allow for human intervention in hazardous scenarios. Using the GLPFDOE methodology, an autonomous exploratory system can be developed that may have a higher likelihood of survivability, can accomplish more scientific mission objectives thus increasing scientific yield, and can decrease risk of mission-ending system damage. A case study is presented in which an autonomous Mars Exploration Rover (MER) is generated and then refined in a simulator using the GLPFDOE method. Development of the GLPFDOE methodology allows for the execution of more complex missions by autonomous systems in remote and inaccessible environments.

Digital Twin-Enabled Decision Support in Mission Engineering and Route Planning

“…However, during mission execution, an adapted version of this method may be useful for UAS to execute autonomously as battlefield situations change. Indeed, such autonomous system behaviors have been suggested in space systems contexts in the past [30,31,67,68] although without the use of a DT.…”