Andreas Breitenmoser

Voronoi coverage of non-convex environments with a group of networked robots

This paper presents a solution to decentralized Voronoi coverage in non-convex polygonal environments. We show that complications arise when existing approaches to Voronoi coverage are applied for deploying a group of robots in non-convex environments. We present an algorithm that is guaranteed to converge to a local optimum. Our algorithm combines classical Voronoi coverage with the Lloyd algorithm and the local path planning algorithm TangentBug to compute the motion of the robots around obstacles and corners. We present the algorithm and prove convergence and optimality. We also discuss experimental results from an implementation with five robots.

Image and animation display with multiple mobile robots

In this article we present a novel display that is created using a group of mobile robots. In contrast to traditional displays that are based on a fixed grid of pixels, such as a screen or a projection, this work describes a display in which each pixel is a mobile robot of controllable color. Pixels become mobile entities, and their positioning and motion are used to produce a novel experience. The system input is a single image or an animation created by an artist. The first stage is to generate physical goal configurations and robot colors to optimally represent the input imagery with the available number of robots. The run-time system includes goal assignment, path planning and local reciprocal collision avoidance, to guarantee smooth, fast and oscillation-free motion between images. The algorithms scale to very large robot swarms and extend to a wide range of robot kinematics. Experimental evaluation is done for two different physical swarms of size 14 and 50 differentially driven robots, and for simulations with 1,000 robot pixels.

Reciprocal collision avoidance for multiple car-like robots

In this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.

Noise characterization of depth sensors for surface inspections

In the context of environment reconstruction for inspection, it is important to handle sensor noise properly to avoid distorted representations. A short survey of available sensors is realize to help their selection based on the payload capability of a robot. We then propose uncertainty models based on empirical results for three models of laser rangefinders: Hokuyo URG-04LX, UTM-30LX and the Sick LMS-151. The methodology, used to characterize those sensors, targets more specifically different metallic materials which often give distorted images due to reflexion. We also evaluate the impact of sensor noise on surface normal vector reconstruction and conclude with observations about the impact of sunlight and reflexions.

Highly Compact Robots for Inspection of Power Plants

This paper reports on a 3 years applied research project dealing with several inspection scenarios of power plants and resulting in many robot prototypes and 3 main achievements. The project is a common effort between industry and university, including ALSTOM together with researchers from the 2 Swiss Federal Institutes of Technology. The goal is to generate knowledge and transfer technology to the industry in the field of robot inspection. The method is to collect commercial scenarios, study them, make some exploratory prototypes, select the best scenarios and develop enhanced prototypes for the most promising applications. The 3 most evolved robots are MagneBike for steam chest, AirGapCrawler for generators and TubeCrawler for boiler tube. The supporting technologies that have been developed are adhesion, locomotion, system integration and localization.

Distributed Coverage Control on Surfaces in 3D Space

This paper addresses the problem of deploying a group of networked robots on a non-planar surface embedded in 3D space. Two distributed coverage control algorithms are presented that both provide a solution to the problem by discrete coverage of a graph. The first method computes shortest paths and runs the Lloyd algorithm on the graph to obtain a centroidal Voronoi tessellation. The second method uses the Euclidean distance measure and locally exchanges mesh cells between approximated Voronoi regions to reach an optimal robot configuration. Both methods are compared and evaluated in simulations and in experiments with five robots on a curved surface.

A monocular vision-based system for 6D relative robot localization

The objective of this paper is the full 6D relative localization of mobile devices, and direct robot-robot localization in particular. We present a novel relative localization system that consists of two complementary modules: a monocular vision module and a target module with four active or passive markers. The core localization algorithm running on the modules determines the marker positions in the camera image and derives the relative robot pose in 3D space. The system is supported by a prediction mechanism based on regression. The modules are tested successfully in experiments with a quadrotor helicopter as well as on a team of two e-puck robots performing a coverage task. The relative localization system provides accuracies of a few centimeters in position and up to a few degrees in orientation. Furthermore, the system is lightweight, with low complexity and system requirements, which enables its application to a wide range of mobile robot platforms.

DisCoverage for non-convex environments with arbitrary obstacles

DisCoverage is a distributed strategy for frontier-based multi-robot exploration. The robots coordinate by a partition of the environment, and choose their target points by optimizing a locally decomposable objective function. In [9] DisCoverage for convex regions was proposed. In this work, we extend DisCoverage to support arbitrary non-convex real-world environments with obstacles. Therefore, we introduce a transformation of non-convex environments to robot centric star-shaped domains. This results in a general solution with broader applications for exploration and path planning. Simulations as well as experiments with real robots demonstrate the exploration progress.

Tensor-voting-based navigation for robotic inspection of 3D surfaces using lidar point clouds

This paper describes a solution to robot navigation on curved 3D surfaces. The navigation system is composed of three successive subparts: a perception and representation, a path planning, and a control subsystem. The environment structure is modeled from noisy lidar point clouds using a tool known as tensor voting. Tensor voting propagates structural information from points within a point cloud in order to estimate the saliency and orientation of surfaces or curves found in the environment. A specialized graph-based planner establishes connectivities between robot states iteratively, while considering robot kinematics as well as structural constraints inferred by tensor voting. The resulting sparse graph structure eliminates the need to generate an explicit surface mesh, yet allows for efficient planning of paths along the surface, while remaining feasible and safe for the robot to traverse. The control scheme eventually transforms the path from 3D space into 2D space by projecting movements into local surface planes, allowing for 2D trajectory tracking. All three subparts of our navigation system are evaluated on simulated as well as real data. The methods are further implemented on the MagneBike climbing robot, and validated in several physical experiments related to the scenario of industrial inspection for power plants.

Adaptive Multi–Robot Coverage of Curved Surfaces

This paper presents two adaptive coverage algorithms for the deployment of multiple robots into discrete partitions over curved surfaces. The algorithms compute a metric tensor field locally on the surface in order to shape the robots’ partitions in position, size, orientation and aspect ratio according to the present anisotropy. The coverage algorithms are further incorporated into a hybrid coverage method for the complete coverage of surface areas. Each robot iteratively deploys and adapts the partition, then subsequently sweeps its assigned area. The algorithms are demonstrated in simulations on different mesh models, including meshes reconstructed from real laser point cloud data.