Physics-based Motion Planning with Temporal Logic Specifications
This work addresses the challenge of autonomous robot motion planning with object manipulation for robotics, but it appears incremental as it builds on existing LTL and sampling-based methods.
The paper tackles the problem of enabling robots to plan motions that satisfy complex temporal logic tasks, particularly when objects obstruct goals, by proposing an approach that combines LTL planning with ontologies and physics-based motion planning, resulting in a method demonstrated with didactic examples for a mobile robot in simple scenarios.
One of the main foci of robotics is nowadays centered in providing a great degree of autonomy to robots. A fundamental step in this direction is to give them the ability to plan in discrete and continuous spaces to find the required motions to complete a complex task. In this line, some recent approaches describe tasks with Linear Temporal Logic (LTL) and reason on discrete actions to guide sampling-based motion planning, with the aim of finding dynamically-feasible motions that satisfy the temporal-logic task specifications. The present paper proposes an LTL planning approach enhanced with the use of ontologies to describe and reason about the task, on the one hand, and that includes physics-based motion planning to allow the purposeful manipulation of objects, on the other hand. The proposal has been implemented and is illustrated with didactic examples with a mobile robot in simple scenarios where some of the goals are occupied with objects that must be removed in order to fulfill the task.