In this work, we use iterative Linear Quadratic Gaussian (iLQG) to plan motions for a mobile robot with range sensors in belief space. We address two limitations that prevent applications of iLQG to the considered robotic system. First, iLQG assumes a differentiable measurement model, which is not true for range sensors. We show that iLQG only requires the differentiability of the belief dynamics. We propose to use a derivative-free filter to approximate the belief dynamics, which does not require explicit differentiability of the measurement model. Second, informative measurements from a range sensor are sparse. Uninformative measurements produce trivial gradient information, which prevent iLQG optimization from converging to a local minimum. We densify the informative measurements by introducing additional parameters in the measurement model. The parameters are iteratively updated in the optimization to ensure convergence to the true measurement model of a range sensor. We show the effectiveness of the proposed modifications through an ablation study. We also apply the proposed method in simulations of large scale real world environments, which show superior performance comparing to the state-of-the-art methods that either assume the separation principle or maximum likelihood measurements.
翻译:在这项工作中,我们使用迭代线性夸德里亚高斯扬(iLQG)来规划具有信仰空间射程传感器的流动机器人的动作。我们处理防止iLQG对考虑的机器人系统应用的两种限制。首先,iLQG采用一个不同的测量模型,对射程传感器来说并非如此。我们显示,iLQG只要求信仰动态的可变性。我们提议使用无衍生物过滤器来接近信仰动态,这不需要测量模型的明显差异性能。第二,范围传感器的信息测量数据很少。非信息性测量产生微小的梯度信息,防止iLQG优化将iLQG优化与当地最小值相融合。我们通过在测量模型中引入更多参数来使信息性测量更加密集。这些参数在优化中反复更新,以确保与一个测距传感器的真正测量模型相匹配。我们通过一项增缩研究来显示拟议修改的有效性。我们还在大规模真实世界环境的模拟中应用了拟议方法,该方法显示优于假定的分离原则或最大可能性。