Boris Lau

Kinodynamic motion planning for mobile robots using splines

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 robots 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 Bézier 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 systems capabilities of smooth, precise, and predictive motion.

Efficient grid-based spatial representations for robot navigation in dynamic environments

In robotics, grid maps are often used for solving tasks like collision checking, path planning, and localization. Many approaches to these problems use Euclidean distance maps (DMs), generalized Voronoi diagrams (GVDs), or configuration space (c-space) maps. A key challenge for their application in dynamic environments is the efficient update after potential changes due to moving obstacles or when mapping a previously unknown area. To this end, this paper presents novel algorithms that perform incremental updates that only visit cells affected by changes. Furthermore, we propose incremental update algorithms for DMs and GVDs in the configuration space of non-circular robots. These approaches can be used to implement highly efficient collision checking and holonomic path planning for these platforms. Our c-space representations benefit from parallelization on multi-core CPUs and can also be integrated with other state-of-the-art path planners such as rapidly-exploring random trees. In various experiments using real-world data we show that our update strategies for DMs and GVDs require substantially less cell visits and computation time compared to previous approaches. Furthermore, we demonstrate that our GVD algorithm deals better with non-convex structures, such as indoor areas. All our algorithms consider actual Euclidean distances rather than grid steps and are easy to implement. An open source implementation is available online.

Improved updating of Euclidean distance maps and Voronoi diagrams

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 [1].

Online generation of kinodynamic trajectories for non-circular omnidirectional robots

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

Tracking groups of people with a multi-model hypothesis tracker

People in densely populated environments typically form groups that split and merge. In this paper we track groups of people so as to reflect this formation process and gain efficiency in situations where maintaining the state of individual people would be intractable. We pose the group tracking problem as a recursive multi-hypothesis model selection problem in which we hypothesize over both, the partitioning of tracks into groups (models) and the association of observations to tracks (assignments). Model hypotheses that include split, merge, and continuation events are first generated in a data-driven manner and then validated by means of the assignment probabilities conditioned on the respective model. Observations are found by clustering points from a laser range finder given a background model and associated to existing group tracks using the minimum average Hausdorff distance. Experiments with a stationary and a moving platform show that, in populated environments, tracking groups is clearly more efficient than tracking people separately. Our system runs in real-time on a typical desktop computer.

Range-Based People Detection and Tracking for Socially Enabled Service Robots

With a growing number of robots deployed in populated environments, the ability to detect and track humans, recognize their activities, attributes and social relations are key components for future service robots. In this article we will consider fundamentals towards these goals and present several results using 2D range data.We first propose a learning method to detect people in sensory data based on a set of boosted features. The method largely outperforms the state of the art that typically relies on hand-tuned classifiers. Then, we present a person tracking approach based on the detection and fusion of leg tracks. To deal with the frequent occlusion and self-occlusion of legs, we extend a Multi-Hypothesis Tracking (MHT) approach by the ability to explicitly reason about and deal with adaptive occlusion probabilities. Finally, we address the problem of tracking groups of people, a first step towards the recognition of social relations. We further extend the MHT approach by a multiple model hypothesis stage able to reflect split/merge events in group formation processes. The proposed extension is mathematically elegant, runs in real-time and further allows to accurately estimate the number of people in each group. The article concludes with prospects and suggestions for future research.

An accurate and efficient navigation system for omnidirectional robots in industrial environments

Enhanced logistics is widely regarded as a key technology to increase flexibility and cost efficiency of today’s factories. For example, fully autonomous transport vehicles aim to gradually replace conveyor belts, guided vehicles, and manual labor. In this context, especially omnidirectional robots are appealing thanks to their advanced maneuvering capabilities. In industrial applications, however, accuracy as well as safety and efficiency are key requirements for successful navigation systems. In this paper, we present an accurate navigation system for omnidirectional robots. Our system includes dedicated modules for mapping, localization, trajectory generation and robot control. It has been designed for accurate execution by devising smooth, curvature continuous trajectories, by planning appropriate velocities and by accounting for platform and safety constraints. In this way, it completely utilizes the maneuvering capabilities of omnidirectional robots and optimizes trajectories with respect to time of travel. We present extensive experimental evaluations in simulation and in changing real-world environments to demonstrate the robustness and accuracy of our system.

Improved non-linear spline fitting for teaching trajectories to mobile robots

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.