Robust executive enabling long horizon multi-agent campaign

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020Cataloged from PDF version of thesis.Includes bibliographical references (pages 151-153).Autonomous mobile systems are being tasked to perform increasingly complex missions. These campaigns frequently involve the coordination of agents with continuous dynamics to achieve multiple goals over long horizons and often occur in hazardous environments that can change unpredictably. Campaigns are specified by hybrid activity plans, featuring both finite domain and real-valued variables. This thesis introduces a robust, centralized executive, Magellan, to facilitate the execution of long horizon hybrid campaigns. Previous hybrid execution approaches address the challenges of generating dynamically feasible trajectories that satisfy the goals of a higher level plan. However, these executives do not scale well to campaigns with long horizons or multiple agents. By leveraging the insights of previous work, Magellan robustly executes campaigns specified by hybrid activity plans, while monitoring, and adapting to, disturbances on-the-fly.Our approach to robust hybrid execution hinges on three key innovations. First, we recognize that an executive must generate dynamically accurate trajectories to control continuous agents in real-time while also ensuring that all activities in the campaign plan can be achieved. Magellan address these competing needs using a receding horizon control strategy, only generating trajectories with sufficiently accurate dynamics, modeled in discrete time, over a limited horizon. Magellan avoids being myopic by reasoning over the full campaign plan, using a continuous time formulation with simplified dynamics, to guide the limited horizon trajectory. Magellan achieves a factor of 2 improvement in solution quality compared to the state-of-the-art. Second, hybrid activity plans are often full or partially grounded, however, grounded plans are brittle in the face of unforeseen disturbances during execution.Magellan provides an algorithm for lifting a grounded hybrid activity plan to a flexibly executable plan that entails the same goals but is minimally constrained, allowing Magellan to adapt on-the-fly. Third, agents can deviate from plans during execution due to environmental uncertainty or actuator noise. Magellan monitors the state of the agents during execution and assesses whether constraints in the hybrid campaign plan have been violated. We present a procedure for monitoring both the finite domain and real-valued variables in a hybrid campaign plan.by Marlyse Helena Reeves.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Toward Information-Driven and Risk-Bounded Autonomy for Adaptive Science and Exploration

© 2020 The MITRE Corporation. All Rights Reserved. While the primary purpose of robotic space exploration systems is to gather scientific data, it is equally important that engineering operations are performed and engineering constraints are respected in order to prolong the mission life and ensure the integrity of the observations taken. However, science and engineering operations are often at odds with each other as attempting to obtain the “best” data may violate engineering operations constraints and place the mission at risk. Historically, mission systems engineering has separated the process of planning for science from engineering operations, with the engineering operations constrained to support the science measurement plan with acceptable risk. This task division leads to multiple design iterations between the science and engineering operations which results in compromised, conservative operations that reduce science return and are more brittle than desired. To overcome these limitations, we present an approach for autonomous mission planning that explicitly models and reasons about the coupling between science and engineering operations, resulting in higher science return, while maintaining acceptable levels of risk. Our approach is to develop an information-driven, risk-bounded plan executive that is capable of producing missions satisfying the goals and constraints expressed in these programs. In this paper, we describe in detail the risk-bounded, information-driven execution problem and lay out the architecture used in our information-directed plan executive ‘Enterprise’. We then show the performance of the current version of Enterprise on two space exploration scenarios. Finally, we conclude with thoughts on future work, including on the design of a proposed information-theoretic language that will allow operators and scientists to specify their objectives in terms of questions about scientific phenomena or the configuration of the space system