There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.
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. I. Nomenclature 𝒜= set of possible actions 𝑎 𝑡 = discrete action at time step 𝑡 ℬ = set of safe states 𝒞 = set of constraints that must be satisfied by returned plan
The objective of the competition was to design and build a small unmanned aerial vehicle (UAV) that could complete a local surveillance mission. A unique feature of the competition was an experimental crowdsourcing model, in which competing teams were able to provide technical feedback to each other as part of the design process. The icarusLabs team's solution to the challenge combined a tricopter and blended wing body aircraft into a single hybrid airframe. This paper presents the rationale and theory behind the design as well as the lessons learned from the technical and logistical challenges faced. In addition, the authors reflect on the crowdsourcing aspect of the challenge from a competitor's perspective. Overall, incentives for individual teams' success directly conflicted with the essence of crowdsourcing, but the competition was successful at stimulating interest to fill a void in current UAV solutions, bringing together passionate individuals from the global unmanned systems community, and generating a broad range of novel approaches to address the presented challenges.
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