Path planning for optimal cooperative navigation
Path planning for optimal cooperative navigation
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最优协作导航的路径规划
DOI：
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发表时间：
2016
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通讯作者：
Andrew T. Smith
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作者：
Adam J. Rutkowski;Jamie E. Barnes;Andrew T. Smith
对于许多导航场景，众所周知，导航状态估计的准确性取决于所走的路径，特别是在无法访问全球定位系统(GPS)等外部导航辅助设备的情况下。在这项工作中，我们提出了一种路径规划方法，试图最小化从已知初始位置到期望目标位置的两辆自主车辆的导航不确定性。对于这项研究中考虑的场景，每辆车都有一个车载里程表，用于测量位置和航向的相对变化。这些车辆还配备了传感器，用于测量车辆之间的距离。导航状态估计是使用佐治亚理工学院平滑和映射(GTSAM)库实现的集中批处理系数图方法获得的。在以前关于这个问题的工作中，候选车辆的轨迹是从一类被称为伪之字形的受限轨迹中选择的。对这一类中所有可能的轨迹进行详尽的搜索后发现，与每辆车直接行驶到目标位置的情况相比，最终位置的不确定性可以减少到原来的5倍。在我们这里介绍的工作中，轨迹不再局限于这样一个有限的类别。取而代之的是，每条路径都是由一组可以放置在任何地方的路点构建的。我们设计了一种方法，采用任意一组路点位置并调整它们的位置，以使得到的路径满足旅行时间和机动性限制。对于每条车辆路径，只使用3个路点，并对路点位置应用简单的随机搜索优化算法，最终位置不确定性可以减少3倍。因此，与直线路径(即最差情况)相比，位置不确定性总共减少了15倍。此外，结果还表明，每辆车的最终位置不确定性可能不同。此外，导航确定性并不一定随着旅行时间的增加而提高。因此，旅行时间应被视为自由参数(即时间约束不一定是活动约束)。对于大多数实际的非线性优化问题，不能保证所得到的结果是全局最优的。然而，非常清楚的是，路径规划可以用来显著减少所考虑的情景的导航不确定性。
For many navigation scenarios, it is known that the accuracy of navigation state estimates depends on the path traveled, particularly when access to external navigation aids such as the Global Positioning System (GPS) is not available. In this work, we present a path planning method that attempts to minimize the navigation uncertainty of a pair of autonomous vehicles traveling from known initial locations to desired goal locations. For the scenario considered in this study, each vehicle has an onboard odometer for measuring relative changes in position and heading. The vehicles also have sensors for measuring the range between the vehicles. Navigation state estimates are obtained using a centralized batch factor graph approach implemented with the Georgia Tech Smoothing and Mapping (GTSAM) library. In previous work on this problem, candidate vehicle trajectories were chosen from a restricted class of trajectories referred to as pseudo-zigzagging. An exhaustive search of all possible trajectories in this class revealed that the final position uncertainty could be reduced by a factor of 5 when compared with the case of each vehicle traveling straight to its goal location. In the work we present here, the trajectories are no longer restricted to such a limited class. Instead, each path is constructed from a small set of waypoints that may be placed anywhere. We have devised a method that takes an arbitrary set of waypoint locations and adjusts their locations so that the resulting path meets travel time and maneuverability constraints. Using just 3 waypoints for each vehicle path, and applying a simple random search optimization algorithm to the waypoint locations, the final position uncertainty can be reduced by another factor of 3. Thus, we achieve a total factor of 15 reduction in position uncertainty when compared to the straight path (i.e. worst) case. Also, the results indicate that the final position uncertainty can be different for each vehicle. Furthermore, the navigation certainty does not necessarily improve with increased travel time. Thus, travel time should be considered a free parameter (i.e. the time constraint is not necessarily an active constraint). As is the case for most practical nonlinear optimization problems, there is no guarantee that the results obtained in this work are globally optimal. Nevertheless, it is quite clear that path planning can be used to significantly reduce navigation uncertainty for the scenario under consideration.