An ontology-based approach towards coupling task and path planning for the simulation of manipulation tasks
Résumé
Simulating complex industrial manipulation tasks (e.g., assembly, disassembly and maintenance tasks) under strong geometric constraints in a virtual environment, requires the joint usage of task and path planning, not only to compute a sequence of primitive actions (i.e., a task plan) at task planning level to identify the order to manipulate different objects (e.g., assembly order), but also to generate and validate motions for each of these primitive actions in a virtual environment by computing valid collision-free paths for these actions at path planning level. Although task and path planning have been respectively welly discussed by artificial intelligence and robotic domain, the link between them still remains an open issue, in particular because path planning for a primitive action often uses purely geometric data. This purely geometric path planning suffers from the classical failures (i.e., high-possibility of failure, high processing time and low path relevance) of automated path planning techniques when dealing with complex geometric models. Thus, it can possibly lead to high computational time of the joint task and path planning process and can probably produce a poor implementation of a task plan. Instead of geometric data, involving higher abstraction level information related to a task to be performed in the path planning of a primitive action could lead to a better relevance of simulations. In this work, we propose an ontology-based approach to generate a specific path planning query for a primitive action, using a well-structured task-oriented knowledge model. This specific path planning query aims at obtaining an increased control on the path planning process of the targeted primitive action.
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