Intelligent mobile robots have recently become able to operate autonomously in large-scale indoor environments for extended periods of time. In this process, mobile robots need the capabilities of both task and motion planning. Task planning in such environments involves sequencing the robot’s high-level goals and subgoals, and typically requires reasoning about the locations of people, rooms, and objects in the environment, and their interactions to achieve a goal. One of the prerequisites for optimal task planning that is often overlooked is having an accurate estimate of the actual distance (or time) a robot needs to navigate from one location to another. State-of-the-art motion planning algorithms, though often computationally complex, are designed exactly for this purpose of finding routes through constrained spaces.
In this article, we focus on integrating task and motion planning (TMP) to achieve task-level-optimal planning for robot navigation while maintaining manageable computational efficiency. To this end, we introduce TMP algorithm PETLON (Planning Efficiently for Task-Level-Optimal Navigation), including two configurations with different trade-offs over computational expenses between task and motion planning, for everyday service tasks using a mobile robot. Experiments have been conducted both in simulation and on a mobile robot using object delivery tasks in an indoor office environment. The key observation from the results is that PETLON is more efficient than a baseline approach that pre-computes motion costs of all possible navigation actions, while still producing plans that are optimal at the task level. We provide results with two different task planning paradigms in the implementation of PETLON, and offer TMP practitioners guidelines for the selection of task planners from an engineering perspective.
In recent decades liberal democracies have begun to experiment with sortition—the selection of citizens by lottery for engagement in political or policy discussions. A notable development has been the emergence of randomly selected deliberative mini-publics that can take different forms. These have occurred in the context of wider debates in political theory over the potential of deliberative democracy, and also from a desire to bring citizens into the heart of debates over constitutional and institutional reform. We develop a normative case for sortition focused on how it can contribute in a negative sense—helping to shield the process of selecting officials from forces that could compromise it—and also in a positive sense in how it can deliver more effectively on the ideal of descriptive representation. We then consider empirical evidence of how the most significant examples of sortition today (the citizens’ assemblies) have performed against those normative ideals.
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