Guest Editorial: Special issue on constrained decision-making in robotics
نویسندگان
چکیده
As the complexity of tasks envisioned for robotic systems increases, their decision making module needs to be capable of concurrently evaluating and trading off multiple, possibly contradicting and stochastic objectives. For example, a rescue robot might be required to plan trajectories so as to maximize the probability of success to reach a critical location and, at the same time, minimize the duration of the traversal. Other more general examples include trading off information gathering versus energy expenditures, computation time versus optimality of a decision, or learning versus safety. A natural framework to address this class of problems is constrained decision-making, whereby a decision maker seeks to optimize a given cost function (often stochastic) while keeping other costs (usually involving risk assessments) below given bounds. In the last decade, the operations research community has made significant strides on the topics of constrained decision-making (notoriously more challenging than the unconstrained counterpart) and risk assessment in dynamic scenarios. The result is a comprehensive theory and a set of algorithmic tools for this class of decision-making problems. Yet, despite their relevance, these results have seen limited application within the robotics domain. Accordingly, in July 2014 we organized the workshop “Constrained decision-making in robotics: models, algorithms, and applications” during the Robotics Science and
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عنوان ژورنال:
- Auton. Robots
دوره 39 شماره
صفحات -
تاریخ انتشار 2015