Probabilistic approaches to the calibration problem in multi-robot systems
Probabilistic approaches to the calibration problem in multi-robot systems
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DOI：
10.1007/s10514-018-9744-3
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发表时间：
2018-10-01
影响因子：
3.5
通讯作者：
Chirikjian, Gregory S.
中科院分区：
文献类型：
--
作者：
Ma, Qianli;Goh, Zachariah;Chirikjian, Gregory S.
近年来，对多机器人系统的兴趣迅速增长。这部分是由于这种系统的成本降低，部分是由于它们可以处理的任务的难度增加。多机器人系统通常由多个独立的机器人组成，如移动的机器人或无人机。多机器人系统的运动规划、碰撞检测和调度等问题已成为研究的热点。然而，没有太多的已公布的多机器人系统的校准问题，尽管事实上，它是整个系统以一致和准确的方式操作的先决条件。与传统的手眼和机器人世界校准相比，在多机器人场景中出现了一个相对较新的问题，称为校准问题，其中A，B，C是从传感器测量的时变刚体变换，X，Y，Z是待校准的未知静态变换。先前在不同的应用领域中已经提出了几种求解器，可以同时求解X，Y和Z。然而，所有的求解器都假设数据流之间的确切时间对应关系的先验知识，并且。虽然该假设在某些情况下可能是合理的，但在多机器人系统的应用领域中，其可能使用自组织和异步通信协议，通常不能假设这种对应关系的知识。此外，文献中的现有方法需要良好的初始估计，这并不总是容易或可能获得的。为了解决这个问题，我们提出了两种概率方法，可以解决这个问题，而无需先验知识的时间对应的数据。此外，不需要初始估计来恢复X、Y和Z。这些方法是概率性的，在这个意义上，查看集合，和作为从底层概率密度函数中提取的样本。这就是允许这些方法在没有时间对应的情况下工作的原因。然而，测量误差没有明确建模，因此结果对真实的世界数据中普遍存在的噪声敏感。因此，我们介绍的方法来增加鲁棒性的噪声，包括一个混合的方法，结合传统的求解器与概率方法和迭代方法的细化，以增加鲁棒性的情况下，嘈杂的实验数据。结果表明，该算法对噪声和数据中对应信息的丢失具有较好的鲁棒性。这些方法特别适用于多机器人系统，也适用于机器人技术的其他领域。
Interest in multi-robot systems has grown rapidly in recent years. This is due in part to the reduced cost of such systems and in part to the increased difficulty of the tasks that they can address. A multi-robot system is usually composed of several individual robots such as mobile robots or unmanned aerial vehicles. Many problems have been investigated for multi-robot system such as motion planning, collision checking and scheduling. However, not much has been published previously about the calibration problem for multi-robot systems despite the fact that it is the prerequisite for the whole system to operate in a consistent and accurate manner. Compared to the traditional hand-eye & robot-world calibration, a relatively new problem called the calibration problem arises in the multi-robot scenario, where A, B, C are time-varying rigid body transformations measured from sensors and X, Y, Z are unknown static transformations to be calibrated. Several solvers have been proposed previously in different application areas that can solve for X, Y and Z simultaneously. However, all of the solvers assume a priori knowledge of the exact temporal correspondence among the data streams , and . While that assumption may be justified in some scenarios, in the application domain of multi-robot systems, which may use ad hoc and asynchronous communication protocols, knowledge of this correspondence generally cannot be assumed. Moreover, the existing methods in the literature require good initial estimates that are not always easy or possible to obtain. To address this, we propose two probabilistic approaches that can solve the problem without a priori knowledge of the temporal correspondence of the data. In addition, no initial estimates are required for recovering X, Y and Z. These methods are probabilistic in the sense of viewing the sets , , and as samples drawn from underlying probability density functions. This is what allows these methods to work in the absence of temporal correspondence. However, measurement errors are not explicitly modeled, and so the results are sensitive to the sort of noise that is ubiquitous in real world data. We therefore introduce ways to add robustness to noise, including a hybrid method which combines traditional solvers with the probabilistic methodology and an iterative method for refinement to add robustness in the case of noisy experimental data. It is shown that the new algorithm is robust to both noise and the loss of correspondence information in the data. These methods are particularly well suited for multi-robot systems, and also apply to other areas of robotics in which arises.