规划师
杠杆(统计)
可扩展性
计算机科学
概括性
约束(计算机辅助设计)
任务(项目管理)
运动规划
机器人
钥匙(锁)
水准点(测量)
对象(语法)
运动(物理)
人工智能
分布式计算
工程类
系统工程
数据库
地理
心理治疗师
大地测量学
机械工程
计算机安全
心理学
作者
Neil T. Dantam,Zachary Kingston,Swarat Chaudhuri,Lydia E. Kavraki
标识
DOI:10.1177/0278364918761570
摘要
We present a new constraint-based framework for task and motion planning (TMP). Our approach is extensible, probabilistically complete, and offers improved performance and generality compared with a similar, state-of-the-art planner. The key idea is to leverage incremental constraint solving to efficiently incorporate geometric information at the task level. Using motion feasibility information to guide task planning improves scalability of the overall planner. Our key abstractions address the requirements of manipulation and object rearrangement. We validate our approach on a physical manipulator and evaluate scalability on scenarios with many objects and long plans, showing order-of-magnitude gains compared with the benchmark planner and improved scalability from additional geometric guidance. Finally, in addition to describing a new method for TMP and its implementation on a physical robot, we also put forward requirements and abstractions for the development of similar planners in the future.
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