Lorenz Wellhausen

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Wellhausen

First Name

Lorenz

ORCID

0000-0001-5148-754X

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Publications 1 - 10 of 27

Reliable Real-time Change Detection and Mapping for 3D LiDARs
Wellhausen, Lorenz; Dubé, Renaud; Gawel, Abel; et al. (2017)
2017 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR)
A common scenario in Search and Rescue robotics is to map and patrol a disaster site to assess the situation and plan potential missions of rescue teams. Particular importance has to be given to changes in the environment as these may correspond to critical events like building collapses, movement of objects, etc. This paper presents a change detection pipeline for LiDAR-equipped robots to assist humans in detecting those changes. The local 3D point cloud data is compared to an octree-based occupancy map representation of the environment by computing the Mahalanobis distance to the closest voxel in the map. The thresholded distance is processed by a clustering algorithm to obtain a set of change candidates. Finally, outliers in these sets are filtered using a random forest classifier. Changes are continuously mapped during a sortie based on their classification score and number of occurrences. Changes are reported in real time during robot operation.
Efficient Gait Selection for Quadrupedal Robots on the Moon and Mars
Kolvenbach, Hendrik; Bellicoso, Dario; Jenelten, Fabian; et al. (2018)
We present the outcome of a study on the energetic expenditure of quadrupedal gaits in the gravitational scenarios of Earth, Mars and the Moon. The study was performed in simulation on a fully controlled 30kg-class robot. We compared the mechanical power required for locomotion by using a static walking gait, dynamic gaits without full flight phases (trot, dynamic lateral walk) and dynamic gaits with full flight phases (running trot, pronk) at velocities up to 1 m/s. Additionally, we conducted a field test which compared the energetic expenditure and ground contact forces of a trot and running trot on a sandy terrain against the laboratory environment and the simulation results.Generally, gaits with full flight phases become increasingly efficient in reduced gravity scenarios. The study revealed that a running trot outperforms the gaits without full flight phases at forward velocities of 0.55 m/s on Mars and 0.4 m/s on the Moon. Executing a trot on the real robot showed that the energetic expenditure is 1.2-1.4 times higher on a coarse, heterogeneous sand compared to the lab environment. The field test revealed that the point feet design is not optimal for gaits with full flight phases on compressible soil due to high contact forces and increased ground penetration, which leads to stuck situations.
Perceptive Whole Body Planning for Multi-legged Robots in Confined Spaces
Buchanan, Russell; Wellhausen, Lorenz; Bjelonic, Marko; et al. (2021)
Journal of Field Robotics
Learning robust autonomous navigation and locomotion for wheeled-legged robots
Lee, Joonho; Bjelonic, Marko; Reske, Alexander; et al. (2024)
Science Robotics
Autonomous wheeled-legged robots have the potential to transform logistics systems, improving operational efficiency and adaptability in urban environments. Navigating urban environments, however, poses unique challenges for robots, necessitating innovative solutions for locomotion and navigation. These challenges include the need for adaptive locomotion across varied terrains and the ability to navigate efficiently around complex dynamic obstacles. This work introduces a fully integrated system comprising adaptive locomotion control, mobility-aware local navigation planning, and large-scale path planning within the city. Using model-free reinforcement learning (RL) techniques and privileged learning, we developed a versatile locomotion controller. This controller achieves efficient and robust locomotion over various rough terrains, facilitated by smooth transitions between walking and driving modes. It is tightly integrated with a learned navigation controller through a hierarchical RL framework, enabling effective navigation through challenging terrain and various obstacles at high speed. Our controllers are integrated into a large-scale urban navigation system and validated by autonomous, kilometer-scale navigation missions conducted in Zurich, Switzerland, and Seville, Spain. These missions demonstrate the system's robustness and adaptability, underscoring the importance of integrated control systems in achieving seamless navigation in complex environments. Our findings support the feasibility of wheeled-legged robots and hierarchical RL for autonomous navigation, with implications for last-mile delivery and beyond.
Map-optimized probabilistic traffic rule evaluation
Wellhausen, Lorenz; Jacob, Mithun G. (2016)
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge
Tranzatto, Marco; Mascarich, Frank; Bernreiter, Lukas; et al. (2022)
Field Robotics
Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems, as underground settings present key challenges that can render robot autonomy hard to achieve. This problem has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, we present the CERBERUS system-of-systems, as a unified strategy for subterranean exploration using legged and flying robots. Our proposed approach relies on ANYmal quadraped as primary robots, exploiting their endurance and ability to traverse challenging terrain. For aerial robots, we use both conventional and collision-tolerant multirotors to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, we developed a complementary multimodal sensor-fusion approach, utilizing camera, LiDAR, and inertial data for resilient robot pose estimation. Individual robot pose estimates are refined by a centralized multi-robot map-optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path-planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication among team agents and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station and wireless “breadcrumb” nodes deployed by the legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge’s Tunnel and Urban Circuit events, along with the current limitations and the lessons learned for the benefit of the community.
Safe Robot Navigation via Multi-Modal Anomaly Detection
Wellhausen, Lorenz; Ranftl, René; Hutter, Marco (2020)
IEEE Robotics and Automation Letters
Navigation in natural outdoor environments requires a robust and reliable traversability classification method to handle the plethora of situations a robot can encounter. Binary classification algorithms perform well in their native domain but tend to provide overconfident predictions when presented with out-of-distribution samples, which can lead to catastrophic failure when navigating unknown environments. We propose to overcome this issue by using anomaly detection on multi-modal images for traversability classification, which is easily scalable by training in a self-supervised fashion from robot experience. In this work, we evaluate multiple anomaly detection methods with a combination of uni- and multi-modal images in their performance on data from different environmental conditions. Our results show that an approach using a feature extractor and normalizing flow with an input of RGB, depth and surface normals performs best. It achieves over 95% area under the ROC curve and is robust to out-of-distribution samples.
Deep Measurement Updates for Bayes Filters
Pankert, Johannes; Minniti, Maria Vittoria; Wellhausen, Lorenz; et al. (2022)
IEEE Robotics and Automation Letters
Measurement update rules for Bayes filters often contain hand-crafted heuristics to compute observation probabilities for high-dimensional sensor data, like images. In this work, we propose the novel approach Deep Measurement Update (DMU) as a general update rule for a wide range of systems. DMU has a conditional encoder-decoder neural network structure to process depth images as raw inputs. Even though the network is trained only on synthetic data, the model shows good performance at evaluation time on real-world data. With our proposed training scheme primed data training , we demonstrate how the DMU models can be trained efficiently to be sensitive to condition variables without having to rely on a stochastic information bottleneck. We validate the proposed methods in multiple scenarios of increasing complexity, beginning with the pose estimation of a single object to the joint estimation of the pose and the internal state of an articulated system. Moreover, we provide a benchmark against Articulated Signed Distance Functions(A-SDF) on the RBO dataset as a baseline comparison for articulation state estimation.
Advances in real-world applications for legged robots
Bellicoso, C. Dario; Bjelonic, Marko; Wellhausen, Lorenz; et al. (2018)
Journal of Field Robotics
Elevation Mapping for Locomotion and Navigation using GPU
Miki, Takahiro; Wellhausen, Lorenz; Grandia, Ruben; et al. (2022)
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Perceiving the surrounding environment is crucial for autonomous mobile robots. An elevation map provides a memory-efficient and simple yet powerful geometric representation of the terrain for ground robots. The robots can use this information for navigation in an unknown environment or perceptive locomotion control over rough terrain. Depending on the application, various post processing steps may be incorporated, such as smoothing, inpainting or plane segmentation. In this work, we present an elevation mapping pipeline leveraging GPU for fast and efficient processing with additional features both for navigation and locomotion. We demonstrated our mapping framework through extensive hardware experiments. Our mapping software was successfully deployed for underground exploration during DARPA Subterranean Challenge and for various experiments of quadrupedal locomotion.

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Lorenz Wellhausen

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