This paper discusses the possibilities of extending and adapting the CHOMP motion planner [1] to work with a non-holonomic vehicle such as an autonomous truck with a single trailer. A detailed study has been done to find out the different ways of implementing these constraints on the motion planner. CHOMP, which is a successful motion planner for articulated robots produces very fast and collision-free trajectories. This nature is important for a local path adaptor in a multi-vehicle path planning for resolving path-conflicts in a very fast manner and hence, CHOMP was adapted. Secondly, this paper also details the experimental integration of the modified CHOMP with the sensor fusion and control system of an autonomous Volvo FH-16 truck and a set of experiments conducted on a real-time environment. Finally, additional simulations were also conducted to compare the performance of the different approaches developed to study the feasibility of employing CHOMP to autonomous vehicles.

A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments.

The European V-Charge project seeks to develop fully automated valet parking and charging of electric vehicles using only low-cost sensors. One of the challenges is to implement robust visual localization using only cameras and stock vehicle sensors. We integrated four monocular, wide-angle, fisheye cameras on a consumer car and implemented a mapping and localization pipeline. Visual features and odometry are combined to build and localize against a keyframe-based three dimensional map. We report results for the first stage of the project, based on two months worth of data acquired under varying conditions, with the objective of localizing against a map created offline.

Whole-body operational space control is a powerful compliant control approach for robots that physically interact with their environment. The underlying mathematical and algorithmic principles have been laid in a large body of published work, and novel research keeps advancing its formulation and variations. However the lack of a reusable and robust shared implementation has hindered its widespread adoption.

Navigation and obstacle avoidance in robotics using planar laser scans has matured over the last decades. They basically enable robots to penetrate highly dynamic and populated spaces, such as people's home, and move around smoothly. However, in an unconstrained environment the two-dimensional perceptual space of a fixed mounted laser is not sufficient to ensure safe navigation. In this paper, we present an approach that pools a fast and reliable motion generation approach with modern 3D capturing techniques using a time-of-flight camera. Instead of attempting to implement full 3D motion control, which is computationally more expensive and simply not needed for the targeted scenario of a domestic robot, we introduce a ldquovirtual laserrdquo. For the originally solely laser-based motion generation the technique of fusing real laser measurements and 3D point clouds into a continuous data stream is 100% compatible and transparent. The paper covers the general concept, the necessary extrinsic calibration of two very different types of sensors, and exemplarily illustrates the benefit which is to avoid obstacles not being perceivable in the original laser scan.

This paper presents work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and modeling are combined with a smooth navigation function to perform on-line path planning in cluttered dynamic environments. The SLIP algorithm, an extension of iterative closest point, combines motion detection from a mobile platform with position estimation. This information is used via probabilistic prediction to estimate a traversal risk function that unifies dynamic and static obstacles. The risk is fed to E* and leads to smooth paths that trade off collision risk versus detours

The E* algorithm is a path planning method capable of dynamic replanning and user-configurable path cost interpolation. It calculates a navigation function as a sampling of an underlying smooth goal distance that takes into account a continuous notion of risk that can be controlled in a fine-grained manner. E* results in more appropriate paths during gradient descent. Dynamic replanning means that changes in the environment model can be repaired to avoid the expenses of complete replanning. This helps compensating for the increased computational effort required for interpolation. We present the theoretical basis and a working implementation, as well as measurements of the algorithm’s precision, topological correctness, and computational effort.

This paper presents a navigation framework which enables multiple mobile robots to attain individual goals, coordinate their actions and work safely and reliably in a highly dynamic environment. We give an overview of the framework architecture, its layering and the subsystems reactive obstacle avoidance, local path planning, global path planning, multi-robot planning and localization. The latter receives particular attention as the localization problem is a key issue for navigation in unmodified and difficult environments. The framework permits a lightweight implementation on a fully autonomous robot. This is the result of a design effort striving for compact representations and computational efficiency. The experimental testbed was the "Robotics" pavilion at the Swiss National Exhibition Expo.02 where ten fully autonomous robots were interacting with more than half a million visitors during a five-month period on 3316 km.

In this paper we address the problem of interpreting sensory data for human-robot interaction, especially when gathered from several robots at the same time. After describing motion tracking in this context, we introduce a general framework for situation representation, and how it simplifies extraction of information suitable for complex machine dialogs. As a concrete implementation thereof, a narrative description of a complex scene in a public exposition is created. We regard issues of interpreting sensor data in an efficient way and discuss the effects of the number of robots on the result of the scene interpretation to show that our approach is not only scalable but also profits from a growing number of robots.

We present the local path planning and obstacle avoidance method used on the autonomous tour-guide robot RoboX. It has proven its value during a 5 month operation of ten such robots in a real-world application, a very crowded exhibition. Three known approaches (DWA, elastic band, NF1) have been integrated into a system that performs smooth motion efficiently, in the sense of computational effort as well as goal-directedness. Apart from modifications to the DWA and the elastic band, we present the formulations that allow this fusion.