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Symbolic planning techniques rely on abstract information about a continuous system to design a discrete planner to satisfy desired high‐level objectives. However, applying the generated discrete commands of the discrete planner to the original system may face several challenges, including real‐time implementation, preserving the properties of high‐level objectives in the continuous domain, and issues such as discontinuity in control signals that may physically harm the system. To address these issues and challenges, the authors proposed a novel hybrid control structure for systems with non‐linear multi‐affine dynamics over rectangular partitions. In the proposed framework, a discrete planner can be separately designed to achieve high‐level specifications. Then, the proposed hybrid controller generates jumpless continuous control signals to drive the system over the partitioned space executing the discrete commands of the planner. The hybrid controller generates continuous signals in real‐time while respecting the dynamics of the system and preserving the desired objectives of the high‐level plan. The design process is described in detail and the existence and uniqueness of the proposed solution are investigated. Finally, several case studies are provided to verify the effectiveness of the developed technique.
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A unified sampling-based approach to integrated task and motion planning
We present a novel method for performing integrated task and motion planning (TMP) by adapting any off-the-shelf sampling-based motion planning algorithm to simultaneously solve for a symbolically and geometrically feasible plan using a single motion planner invocation. The core insight of our technique is an embedding of symbolic state into continuous space, coupled with a novel means of automatically deriving a function guiding a planner to regions of continuous space where symbolic actions can be executed. Our technique makes few assumptions and offers a great degree of flexibility and generality compared to state of the art planners. We describe our technique and offer a proof of probabilistic completeness along with empirical evaluation of our technique on manipulation benchmark problems.
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- Award ID(s):
- 1646417
- PAR ID:
- 10113218
- Date Published:
- Journal Name:
- International Symposium on Robotics Research (ISRR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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