Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments
- URL: http://arxiv.org/abs/2303.14874v1
- Date: Mon, 27 Mar 2023 01:50:45 GMT
- Title: Optimal task and motion planning and execution for human-robot
multi-agent systems in dynamic environments
- Authors: Marco Faroni, Alessandro Umbrico, Manuel Beschi, Andrea Orlandini,
Amedeo Cesta, Nicola Pedrocchi
- Abstract summary: We propose a combined task and motion planning approach to optimize sequencing, assignment, and execution of tasks.
The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task.
We demonstrate the approach effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic.
- Score: 54.39292848359306
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Combining symbolic and geometric reasoning in multi-agent systems is a
challenging task that involves planning, scheduling, and synchronization
problems. Existing works overlooked the variability of task duration and
geometric feasibility that is intrinsic to these systems because of the
interaction between agents and the environment. We propose a combined task and
motion planning approach to optimize sequencing, assignment, and execution of
tasks under temporal and spatial variability. The framework relies on
decoupling tasks and actions, where an action is one possible geometric
realization of a symbolic task. At the task level, timeline-based planning
deals with temporal constraints, duration variability, and synergic assignment
of tasks. At the action level, online motion planning plans for the actual
movements dealing with environmental changes. We demonstrate the approach
effectiveness in a collaborative manufacturing scenario, in which a robotic arm
and a human worker shall assemble a mosaic in the shortest time possible.
Compared with existing works, our approach applies to a broader range of
applications and reduces the execution time of the process.
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