Combined task and motion planning is a relevant issue in robotics. In path and motion planning, Rapidly-exploring Random Trees (RRTs) have been proposed as effective methods to efficiently search high-dimensional spaces. On the other hand, the deployment of these techniques to symbolic task planning problems has been partially investigated. In this paper, we explore this issue proposing a method to combine task and motion planning based on RRTs. Our approach relies on a metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and obstacle-free trajectories. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in pick-carry-and-place tasks.

Combining task and motion planning through rapidly-exploring random trees / Caccavale, R.; Finzi, A.. - (2021), pp. 1-6. (Intervento presentato al convegno 10th European Conference on Mobile Robots, ECMR 2021 tenutosi a deu nel 2021) [10.1109/ECMR50962.2021.9568803].

Combining task and motion planning through rapidly-exploring random trees

Caccavale R.
;
Finzi A.
2021

Abstract

Combined task and motion planning is a relevant issue in robotics. In path and motion planning, Rapidly-exploring Random Trees (RRTs) have been proposed as effective methods to efficiently search high-dimensional spaces. On the other hand, the deployment of these techniques to symbolic task planning problems has been partially investigated. In this paper, we explore this issue proposing a method to combine task and motion planning based on RRTs. Our approach relies on a metric space where both symbolic (task) and sub-symbolic (motion) spaces are represented. The associated notion of distance is then exploited by a RRT-based planner to generate a plan that includes both symbolic actions and obstacle-free trajectories. The proposed method is assessed in several case studies provided by a real-world hospital logistic scenario, where an omni-directional mobile robot is involved in pick-carry-and-place tasks.
2021
978-1-6654-1213-1
Combining task and motion planning through rapidly-exploring random trees / Caccavale, R.; Finzi, A.. - (2021), pp. 1-6. (Intervento presentato al convegno 10th European Conference on Mobile Robots, ECMR 2021 tenutosi a deu nel 2021) [10.1109/ECMR50962.2021.9568803].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/865511
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