In the last years, many researchers started to consider teach-and-repeat approaches for reliable autonomous navigation. The paradigm, in all its proposed forms, is deeply rooted in the idea that the robot should autonomously follow a route that has been demonstrated by a human during a teach phase. However, human demonstrations are often inefficient in terms of execution time or may cause premature wear of the robot components due to jittery behavior or strong accelerations. In this paper, we propose the concept of teach, optimize and repeat, which introduces a trajectory optimization step between the teach and repeat phases. To address this problem, we further propose LexTOR, a constrained trajectory optimization method for teach and repeat problems, where the constraints are defined according to user preferences. At its core, LexTOR optimizes both the execution time and the trajectory smoothness in a lexicographic sense. The experiments show that LexTOR is very effective, both qualitatively and quantitatively, in terms of execution time, smoothness, accuracy and bound satisfaction.

In mobile robot navigation, cost functions are a popular approach to generate feasible, safe paths that avoid obstacles and that allow the robot to get from its starting position to the goal position. Alternative ways to navigate around the obstacles typically correspond to different local minima in the cost function. In this paper we present a highly effective approach to overcome such local minima and to quickly propose a set of alternative, topologically different and optimized paths. We furthermore describe how to maintain a set of optimized trajectory alternatives to reduce optimization efforts when the robot has to adapt to changes in the environment. We demonstrate in experiments that our method outperforms a state-of-the-art approach by an order of magnitude in computation time, which allows a robot to use our method online during navigation. We furthermore demonstrate that the approach of using a set of qualitatively different trajectories is beneficial in shared autonomy settings, where a user operating a wheelchair can quickly switch between topologically different trajectories.

The typically restricted field of view of visual sensors often imposes limitations on the performance of localization and simultaneous localization and mapping (SLAM) approaches. In this paper, we propose and analyze the combination of an RGB-D camera with two planar mirrors to split the field of view such that it covers both front and rear view of a mobile robot. We describe how to estimate the extrinsic calibration parameters of the modified sensor using a standard parametrization and a reduced one that exploits the properties of the setup. Our experimental evaluation on real-world data demonstrates the robustness of the calibration procedure. Additionally, we show that our proposed sensor modification substantially improves the accuracy and the robustness in a simultaneous localization and mapping task.

Platforms with holonomic drives are particularly interesting due to their maneuvering capabilities. Robots used for transportation tasks usually have a non-circular footprint. In this work, we present a navigation strategy for a holonomic mobile robot with anyshape footprint. Our technique introduces an efficient navigation method based on a strategy that makes use of discrete and continuous techniques. We introduce compact discrete intervals to represent the free space for computing fast-to-update plans. Based on these, we provide a continuous motion generation approach to generate smooth motions that are fast to compute. We evaluated our approach by running simulated experiments and by using a real holonomic L-shaped robot. Our experiments demonstrate that our technique can be carried out online and is able to smoothly drive the robot to its goal locations even in dynamic environments.

Automation of logistics tasks for small lot sizes and flexible production processes requires intuitive and easy-to-use systems that allow non-expert shop floor workers to naturally instruct transportation systems. To this end, we present a novel laser-based scheme for teach-and-repeat of mobile robot trajectories that relies on scan matching to localize the robot relative to a taught trajectory, which is represented by a sequence of raw odometry and 2D laser data. This approach has two advantages. First, it does not require to build a globally consistent metrical map of the environment, which reduces setup time. Second, the direct use of raw sensor data avoids additional errors that might be introduced by the fact that grid maps only provide an approximation of the environment. Real-world experiments carried out with a holonomic and a differential drive platform demonstrate that our approach repeats trajectories with an accuracy of a few millimeters. A comparison with a standard Monte Carlo localization approach on grid maps furthermore reveals that our method yields lower tracking errors for teach-and-repeat tasks.

In this paper, we present improved spline fitting techniques with the application of trajectory teaching for mobile robots. Given a recorded reference trajectory, we apply non-linear least-squares optimization to accurately approximate the trajectory using a parametric spline. The fitting process is carried out without fixed correspondences between data points and points along the spline, which improves the fit especially in sharp curves. By using a specific path model, our approach requires substantially fewer free parameters than standard approaches to achieve similar residual errors. Thus, the generated paths are ideal for subsequent optimization to reduce the time of travel or for the combination with autonomous planning to evade obstacles blocking the path. Our experiments on real-world data demonstrate the advantages of our method in comparison with standard approaches.

Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.

This paper presents a novel approach to kino-dynamic trajectory generation for non-circular omnidirectional platforms that can be combined with existing path planners. We use quintic Bézier splines to specify position and orientation of the holonomic robot for every point in time. To fully exploit the capabilities of the holonomic robot we propose a novel path representation. It allows for continuous variation of path shapes in the spectrum between straight-line paths with turns on the spot and smooth paths with independent rotations and translations. Using this representation our method optimizes trajectories according to a user-defined cost function, considering the constraints of the platform. This way, it generates fast and efficient trajectories in an anytime fashion. The experiments carried out on an industrial robot show that our approach generates highly efficient and smooth motion trajectories that can be tracked with high precision and predictability. Furthermore, the system operates in real-world environments containing unmapped obstacles and narrow passages.

This paper presents novel, highly efficient approaches for updating Euclidean distance maps and Voronoi diagrams represented on grid maps. Our methods employ a dynamic variant of the brushfire algorithm to update only those cells that are actually affected by changes in the environment. In experiments in different environments we show that our update strategies for distance maps and Voronoi diagrams require substantially fewer cell visits and significantly less computation time compared to previous approaches. Furthermore, the dynamic Voronoi diagram also improves on previous work by correctly dealing with non-convex obstacles such as building walls. We also present a dynamic variant of a skeletonization-based approach to Voronoi diagrams that is especially robust to noise. All of our algorithms consider actual Euclidean distances rather than grid steps. An open source implementation is available online.

This paper presents an approach to time-optimal kinodynamic motion planning for a mobile robot. A global path planner is used to generate collision-free straight-line paths from the robot's position to a given goal location. With waypoints of this path, an initial trajectory is generated which defines the planned position of the robot over time. A velocity profile is computed that accounts for constraints on the velocity and acceleration of the robot. The trajectory is refined to minimize the time needed for traversal by an any-time optimization algorithm. An error-feedback controller generates motor commands to execute the planned trajectory. Quintic Be¿zier splines are used to allow for curvature-continuous joins of trajectory segments, which enables the system to replan trajectories in order to react to unmapped obstacles. Experiments on real robots are presented that show our system's capabilities of smooth, precise, and predictive motion.