Task planning and motion control with temporal logic specifications
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Universidade Federal de Minas Gerais
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This paper proposes a task planning and motion control framework that generates task plans for a linear temporal logic specification (LTL), which are then executed using a task-space constrained motion controller and a local task planner that overcomes local minima. We propose a new encoding for task specifications, directly in the task-space, as constraints of a mixed-integer linear program that can be used with off-the-shelf LTL linear encoding. We apply our framework to plan and execute trajectories for a free-flying robot and show that the task plan is accomplished without collisions, even in the presence of unexpected moving obstacles that are not considered in the planning phase, while control signal constraints are satisfied. To evaluate the local minima avoidance, we compare the local task planner with a sampling-based motion planner, and the results show a smoother trajectory with a faster execution and less total planning time when using our framework. Last, our framework scaled well with a longer LTL specification, as opposed to automata-based frameworks that usually suffer with the curse of the dimensionality.
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Motion planning , Navigation , Simulation , Encoding , Planning , Trajectory , Quadratic programming, Motor Control , Task Planning , Temporal Logic , Temporal Logic Specifications , Collision , Specific Tasks , Local Minima , Urban Planning , Path Planning , Curse Of Dimensionality , Localization Task , Mixed Integer Linear Programming , Planning Time , Planning Framework , Constrained Control , Model System , Optimization Problem , Optimal Control , Control Input , Boolean Operators , Temporal Operators , Global Task , Robot Pose , Low-level Control , Global Plan , Rigid Transformation , Collision-free Trajectory , Minimum Bounding Box , Higher Level Of Abstraction , Collision Detection, Nowadays, robots can be found inside and outside factories in many guises. Mobile robots and manipulators, autonomous cranes, and unmanned aerial vehicles have been applied in many fields: factories, building design, construction, delivery, mapping, agriculture, and public safety. These tasks have something in common: they may have position and orientation constraints. Also, it is highly desirable to have tools that enable task specification in a high-level, intuitive way. In this context, the space of rigid motions appears as a suitable abstraction for the task definition because humans usually think in terms of object poses (position and orientation) when performing a task. Thus, we propose a robotic task planning and motion control (TPMC) framework in the space of rigid motions by extending and combining task-planners and constrained motion controllers.
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https://ieeexplore.ieee.org/document/10341494