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Provably constant-time planning and replanning for real-time grasping objects off a conveyor belt
The International Journal of Robotics Research ( IF 7.5 ) Pub Date : 2021-06-28 , DOI: 10.1177/02783649211027194
Fahad Islam 1 , Oren Salzman 2 , Aditya Agarwal 1 , Maxim Likhachev 1
Affiliation  

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick-and-place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. In addition to the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast-moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time, it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose a planning framework that meets these requirements by providing provable constant-time planning and replanning guarantees. To this end, we first introduce and formalize a new class of algorithms called constant-time motion planning (CTMP) algorithms that guarantee to plan in constant time and within a user-defined time bound. We then present our planning framework for grasping objects off a conveyor belt as an instance of the CTMP class of algorithms. We present it, provide its analytical properties, and perform an experimental analysis both in simulation and on a real robot.



中文翻译:

可证明的恒定时间规划和重新规划,用于实时抓取传送带上的物体

在仓库和制造环境中,操作平台经常部署在传送带上以执行取放任务。由于传送带上的物体在移动,机器人捡起它们的时间有限。这带来了对快速可靠的运动规划器的需求,这些运动规划器可以提供可证明的实时规划保证,这是现有算法无法提供的。除了规划效率之外,操纵任务的成功在很大程度上依赖于感知系统的准确性,而感知系统通常是嘈杂的,尤其是在远距离感知目标对象的情况下。对于快速移动的传送带,机器人在开始执行其运动之前不能等待一个完美的估计。为了能够及时到达对象,它必须尽早开始移动(依赖于最初的噪声估计)并根据感知的姿态更新动态调整其运动。我们提出了一个规划框架,通过提供可证明的恒定时间规划和重新规划保证来满足这些要求。为此,我们首先介绍并形式化一类新的算法,称为恒定时间运动规划 (CTMP) 算法,该算法保证在恒定时间和用户定义的时间范围内进行规划。然后,我们展示了用于从传送带上抓取物体的规划框架,作为 CTMP 类算法的一个实例。我们介绍它,提供它的分析特性,并在模拟和真实机器人上进行实验分析。我们提出了一个规划框架,通过提供可证明的恒定时间规划和重新规划保证来满足这些要求。为此,我们首先介绍并形式化一类新的算法,称为恒定时间运动规划 (CTMP) 算法,该算法保证在恒定时间和用户定义的时间范围内进行规划。然后,我们展示了用于从传送带上抓取物体的规划框架,作为 CTMP 类算法的一个实例。我们介绍它,提供它的分析特性,并在模拟和真实机器人上进行实验分析。我们提出了一个规划框架,通过提供可证明的恒定时间规划和重新规划保证来满足这些要求。为此,我们首先介绍并形式化一类新的算法,称为恒定时间运动规划 (CTMP) 算法,该算法保证在恒定时间和用户定义的时间范围内进行规划。然后,我们展示了用于从传送带上抓取物体的规划框架,作为 CTMP 类算法的一个实例。我们介绍它,提供它的分析特性,并在模拟和真实机器人上进行实验分析。我们首先介绍并形式化一类新的算法,称为恒定时间运动规划 (CTMP) 算法,该算法保证在恒定时间和用户定义的时间范围内进行规划。然后,我们展示了用于从传送带上抓取物体的规划框架,作为 CTMP 类算法的一个实例。我们介绍它,提供它的分析特性,并在模拟和真实机器人上进行实验分析。我们首先介绍并形式化一类新的算法,称为恒定时间运动规划 (CTMP) 算法,该算法保证在恒定时间和用户定义的时间范围内进行规划。然后,我们展示了用于从传送带上抓取物体的规划框架,作为 CTMP 类算法的一个实例。我们介绍它,提供它的分析特性,并在模拟和真实机器人上进行实验分析。

更新日期:2021-06-29
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