Learning to assemble objects with a robot swarm

Large populations of simple robots can solve complex tasks, but controlling them is still a challenging problem, due to limited communication and computation power. In order to assemble objects, have shown that a human controller can solve such a task. Instead, we investigate how to learn the assembly of multiple objects with a single central controller. We propose splitting the assembly process in two sub-tasks -- generating a top-level assembly policy and learning an object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution.The resulting system is able to solve assembly tasks with varying object shapes being assembled as shown in multiple simulation scenarios

Impact of energy storage technologies in a distribution grid : An analysis of Key Performance Indicators relating to a local grid’s performance characteristics

The energy system is undergoing a transformation on a never before witnessed scale. The changes are driven by global market forces and technological advancements, improving on a seemingly exponential scale. This in turn has led to the price of both renewables and the accompanying technology decrease over time, making the transition into renewables more economically viable. The drawback of variable renewable energy is that it is variable and dependent on the surrounding environment. Therefore, storing the energy during hours of production, to be used at a later stage when energy demand is higher is becoming ever more important and an attractive option. The purpose of this degree project is to, from a set of performance indicators, evaluate three different energy storage technologies and their respective impact on a distribution grid. The examined storage technologies are: Batteries, Capacitators and a H2 Fuel cell. A literature study was performed in order to find out how grid performance is evaluated, and how the different storage technologies operate. The obtained literature comes from scientific reports, and papers, found by utilizing Mälardalens University library-database. A model representing a Swedish grid with a connection point to the distribution side was created. The model is taken from previous credited work, and customized to fit the operational parameters of a Swedish grid. It was decided that the key indicators for evaluating the state of a grid was to look at the: voltage- and frequency variations, load factor, capacity factor and the overall system efficiency. The simulation is a discrete time simulation that utilizes parameters indicative of one full day of data. The results showed that, from a technological standpoint, the supercapacitor performed better in more categories than the Li-ion battery and H2 fuel cell. However, the Li-ion battery reduced the peaks of the frequency measurements which is a key metric when deciding on grid health. Also, there is the added benefit of the battery and fuel cell of having a longer operational time before the state of charge is depleted. This increases the flexibility of the technology and could therefore be more beneficial in other applications where power supply is more scarce.

Robust 3D SLAM for Mobile Search and Rescue Robots in Challenging Environments

Rescue robot systems operate in highly challenging environments characterized by rough, possibly unknown terrain leading to non-smooth robot motions and poor odometry estimates. To enable autonomous and assisted operation, rescue robots have to localize themselves within the environment and create a map of it by employing a SLAM (Simultaneous Localization and Mapping) approach. Performing 3D laser rangefinder SLAM requires comprehensive sensor coverage of 3D space. As state- of-the-art laser rangefinders only scan a planar slice of the environment, additional motion of the sensor relative to the robot is necessary to capture a 3D scan. Thereby, separate sections of the 3D scan are captured at different times yielding distortions in the 3D scan when the robot is moving. Based on an analysis of existing approaches, this work builds on top of the Google Cartographer approach. Cartographer combines occupancy grid based scan matching with sensor observations of IMU and odometry in a joint optimization problem to resolve the scan distortion. As scan matching accumulates error, the system maintains a pose graph to perform loop closure and reduce the error of the trajectory. For 2D laser rangefinder SLAM, representing the map with Truncated Signed Distance Fields has shown to improve the accuracy of the pose estimate and the map. To allow an exchange of the map representation, a generalized formulation for generic grid maps is proposed. As specific instances of generic grid maps, occupancy grids, Truncated Signed Distance Fields and Euclidean Signed Distance Fields are ana- lyzed. Furthermore, the alignment of the robot trajectory against external references such as the Global Navigation Satellite System is examined. In the experiments comparing the grid map instances for scan matching, the signed distance field (SDF) approaches improve the accuracy of scan matching and thus the resulting trajectory and map. Furthermore, the SDF approaches yield a reduced number of iterations for the optimizations to converge leading to faster scan matching. However, the SDF approaches increase the map update time by an order of magnitude. The approach is evaluated in multiple scenarios showing the capability to handle highly challenging terrain with the corresponding incorrect odometry and high angular accelerations as shown in a scenario from the Robocup 2017 competition. The large scale behavior is validated on a ~ 500 m loop around a building in a dynamic environment. The proposed method detects the loop closure and corrects for the accumulated error

Collision Handling between Rigid and Deformable Bodies with Continuous Penalty Forces

Collision handling has been an active research topic in the area of the physically-based simulation of rigid and deformable bodies for many years. A common approach in interactive environments are discrete penalty forces, computing a repulsion forced based on the penetration at one moment in the time step. They provide low computational costs and good scalability, though they suffer from jitter and instability. Tang et al. [18] improved the approach of discrete penalty forces and introduced 2012 the continuous penalty forces, continuously accumulating penalty forces along the penetration trajectory over the whole time step. Thereby, the jitter and instability issues are reduced. Although, the continuous penalty forces show artifacts especially for enduring contacts, precluding the simulation of sliding contacts. In this thesis, we present a unified system to handle collisions between rigid and deformable bodies with friction. We modify the integration scheme by Bridson et al. [4] to handle rigid and deformable bodies, apply a continuous collision detection [16], handle the detected collisions with continuous penalty forces [18] and apply a penalty-based friction model [23]. We discuss the artifacts arising from the continuous penalty forces algorithm, examine methods to tackle them and apply the new methods to the continuous penalty forces algorithm. Finally, we analyze the results of the continuous penalty forces algorithm in comparison to discrete penalty forces, evaluate our new algorithm to handle the continuous penalty forces artifacts and inspect further improvements

Collision Handling between Rigid and Deformable Bodies with Continuous Penalty Forces

Collision handling has been an active research topic in the area of the physically-based simulation of rigid and deformable bodies for many years. A common approach in interactive environments are discrete penalty forces, computing a repulsion forced based on the penetration at one moment in the time step. They provide low computational costs and good scalability, though they suffer from jitter and instability. Tang et al. 18 improved the approach of discrete penalty forces and introduced 2012 the continuous penalty forces, continuously accumulating penalty forces along the penetration trajectory over the whole time step. Thereby, the jitter and instability issues are reduced. Although, the continuous penalty forces show artifacts especially for enduring contacts, precluding the simulation of sliding contacts. In this thesis, we present a unified system to handle collisions between rigid and deformable bodies with friction. We modify the integration scheme by Bridson et al. 4 to handle rigid and deformable bodies, apply a continuous collision detection 16, handle the detected collisions with continuous penalty forces 18 and apply a penalty-based friction model 23. We discuss the artifacts arising from the continuous penalty forces algorithm, examine methods to tackle them and apply the new methods to the continuous penalty forces algorithm. Finally, we analyze the results of the continuous penalty forces algorithm in comparison to discrete penalty forces, evaluate our new algorithm to handle the continuous penalty forces artifacts and inspect further improvements

3D Point Cloud - Altes Hauptgebäude, TU Darmstadt

3D Point Cloud of the old main building at TU Darmstadt (S1|03, Hochschulstr. 1, 64289 Darmstadt, Germany). The raw data was captured with the Hector Tracker robot (http://www.teamhector.de/our-robot/51-hector-tracker) and processed using the Simultaneous Localization and Mapping approach developed by Kevin Daun based on Google Cartographer. The data set contains 186,896,629 points and the bounding box has a size of 220.2 m x 227.3 m 40.7 m

Simulation of laser-induced incandescence measurements in an anisotropically scattering aerosol through backward Monte Carlo

Peer reviewed: YesNRC publication: Ye

DRZ Living Lab Tracked Robot SLAM Dataset

Data set for the evaluation of SLAM systems in challenging terrains. The data set covers four sequences with challenging terrain, each tracked with a high-performance Qualisys optical motion capture system. The four sequences contain 1) double pitch ramps, which induce a fast pitch motion when traversing 2) a loose woodpile which slips when traversed 3) the RoboCup Rescue League "Maneuvering 3 - Traverse" lane which contains a 2.4m long 30-degree incline 4) the RoboCup Rescue League "Mobility 4 - Elevated Ramps" lane containing a diagonal hill terrain consisting of 60 cm ramps with sloped tops. The sequences are between 59 s and 149 s in duration. Corresponding to the publication: K. Daun, M. Schnaubelt, S. Kohlbrecher and O. von Stryk, "HectorGrapher: Continuous-time Lidar SLAM with Multi-resolution Signed Distance Function Registration for Challenging Terrain," 2021 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), New York City, NY, USA, 2021, pp. 152-159, doi: 10.1109/SSRR53300.2021.9597690 Add to Citavi project by DOI.1.

Pose Prediction for Mobile Ground Robots Evaluation Dataset

This dataset provides ground truth robot trajectories in rough terrain for the evaluation of pose prediction approaches for mobile ground robots. It is composed of six datasets in four different scenarios of the RoboCup Rescue Robot League (RRL): * Continuous Ramps: Series of double ramps * Curb: Three 10 x 10 cm bars on flat ground * Hurdles: Steps of varying heights * Elevated Ramps: Boxes of varying heights with sloped tops Four datasets were created in the Gazebo simulator and two were recorded on a real robot platform in the DRZ Living Lab. Each dataset contains the ground truth robot poses of a path through the arena. In Gazebo, the ground truth poses are provided by the simulator. In the DRZ Living Lab, a high-performance Qualisys optical motion capture system has been used. The data has been recorded using the tracked robot "Asterix". It is a highly mobile platform with main tracks and coupled flippers on the front and back and a chassis footprint of 72 × 52 cm. The data is provided as Bagfiles for ROS and is intended to be used with the package hector_pose_prediction_benchmark. This dataset is published as part of the publication: Oehler, Martin, et al. "Accurate Pose Prediction on Signed Distance Fields for Mobile Ground Robots in Rough Terrain." 2023 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2023. See the provided README for further information

Large Scale 2D Laser SLAM using Truncated Signed Distance Functions

For deployment in previously unknown, unstructured and GPS-denied environments, autonomous mobile rescue robots need to localize themselves in the environment and create a map of it using a simultaneous localization and mapping (SLAM) approach. While most existing lidar-based methods use occupancy grids to represent a map, the use of truncated signed distance functions (TSDFs) is investigated in this paper to improve accuracy and robustness. In contrast to occupancy grids, TSDFs represent the distance to the nearest surface in every grid cell. This enables sub-pixel precision during localization and increases the basin of convergence of scan matching. To enable consistent mapping of large spaces, an efficient branch-and-bound based loop closure detection is applied. The evaluation of the proposed approach with publicly available benchmark data shows that the proposed approach yields improved accuracy in comparison to occupancy grid based methods, while requiring similar runtime. Furthermore, it is demonstrated that the proposed approach is able to map a large scale environment with urban search and rescue elements in real-time