Journal: IEEE Transactions on Field Robotics

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IEEE

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2997-1101

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2024 - 20257
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Publications 1 - 7 of 7
  • Informed, Constrained, Aligned: A Field Analysis on Degeneracy-aware Point Cloud Registration in the Wild
    Item type: Journal Article
    Tuna, Turcan; Nubert, Julian; Pfreundschuh, Patrick; et al. (2025)
    IEEE Transactions on Field Robotics
    The iterative closest point (ICP) registration algorithm has been a preferred method for light detection and ranging (LiDAR)-based robot localization for nearly a decade. However, even in modern simultaneous localization and mapping (SLAM) solutions, ICP can degrade and become unreliable in geometrically ill-conditioned environments. Current solutions primarily focus on utilizing additional sources of information, such as external odometry, to either replace the degenerate directions of the optimization solution or add additional constraints in a sensor-fusion setup afterward. In response, this work investigates and compares new and existing degeneracy mitigation methods for robust LiDAR-based localization and analyzes the efficacy of these approaches in degenerate environments for the first time in the literature at this scale. Specifically, this work investigates i) the effect of using active or passive degeneracy mitigation methods for the problem of ill-conditioned ICP in LiDAR degenerate environments and ii) the evaluation of truncated singular value decomposition (TSVD), inequality constraints (Ineq. Con.), and linear/nonlinear Tikhonov regularization for the application of degenerate point cloud registration for the first time. Furthermore, a sensitivity analysis for the least-squares minimization step of the ICP problem is carried out to better understand how each method affects the optimization and what to expect from each method. The results of the analysis are validated through multiple real-world robotic field and simulated experiments. The analysis demonstrates that active optimization degeneracy mitigation is necessary and advantageous in the absence of reliable external estimate assistance for LiDAR-SLAM, and soft-constrained methods can provide better results in complex ill-conditioned scenarios with heuristic fine-tuned parameters. The code and data used in this work are made publicly available to the community.
  • Field Deployment of BiodivX Drones in the Amazon Rainforest for Biodiversity Monitoring
    Item type: Journal Article
    Geckeler, Christian; Kirchgeorg, Steffen; Strunck, Georg; et al. (2025)
    IEEE Transactions on Field Robotics
    Tropical rainforests are among the most biodiverse ecosystems on Earth and also among the most threatened by anthropogenic pressures such as deforestation and climate change. Understanding human impact and the efficacy of conservation and preservation efforts requires scalable and comprehensive biodiversity monitoring solutions. As a winning finalist of the XPRIZE Rainforest Competition, ETH BiodivX collected biodiversity data from 100 ha of rainforest in the Amazon, in 24 h. A suite of complementary data types were captured, from remote sensing maps and close-up images to surface and water environmental DNA (eDNA), along with canopy rafts that collect specimens, close-up images, and bioacoustic recordings. A distributed mesh communication network allows for a persistent link to the drone, up to the edges of the competition area. Optimized workflows allow for a full RGB and digital surface model (DSM) after only one and a half hours. The captured DSM was then used to collect surface eDNA fully autonomously, and using the communication network, surface eDNA was collected at distances up to 1.4 km from the base station. Preprocessed multispectral satellite remote sensing provides indicators of water locations, which were then sampled for water eDNA. The canopy rafts can act as communication nodes or data collection stations, providing long-term bioacoustic recordings, insect images, and specimens. By utilizing a commercial drone platform with modular payloads for diverse tasks, the solutions are robust and easy to use. These field-proven systems mark a major step toward scalable biodiversity monitoring, including in some of the world’s most remote and biodiverse regions: tropical rainforests.INDEX TERMS Biodiversity assessments, canopy raft, drones, environmental DNA (eDNA), environmental monitoring, insect trap, remote sensing.
  • Toward Autonomous Excavation Planning
    Item type: Journal Article
    Terenzi, Lorenzo; Hutter, Marco (2024)
    IEEE Transactions on Field Robotics
    Excavation plans are essential in construction projects, dictating the dirt disposal strategy and excavation sequence based on the final geometry and machinery available. While most construction processes rely heavily on coarse sequence planning and local execution planning driven by human expertise and intuition, fully automated planning tools are notably absent from the industry. This article introduces a fully autonomous excavation planning system. Initially, the site is mapped, followed by user selection of the desired excavation geometry. The system then invokes a global planner to determine the sequence of poses for the excavator, ensuring complete site coverage. For each pose, a local excavation planner decides how to move the soil around the machine, and a digging planner subsequently dictates the sequence of digging trajectories to complete a patch. We showcased our system by autonomously excavating the largest pit documented so far, achieving an average digging cycle time of roughly 30 s
  • Scale-Invariant Specifications for Human-Swarm Systems
    Item type: Journal Article
    Meyer, Joel; Prabhakar, Ahalya; Pinosky, Allison; et al. (2024)
    IEEE Transactions on Field Robotics
    We present a method for controlling a swarm using its spectral decomposition—that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain—guaranteeing scale invariance with respect to the number of agents both for computation and the operator tasked with controlling the swarm. We use ergodic control, decentralized across the network, for implementation. In the DARPA OFFSET program field setting, we test this interface design for the operator using the swarm tactics and offensive mission planning (STOMP) interface—the same interface used by Raytheon BBN throughout the duration of the OFFSET program. In these tests, we demonstrate that our approach is scale-invariant—the user specification does not depend on the number of agents; it is persistent—the specification remains active until the user specifies a new command; and it is in real time—the user can interact with and interrupt the swarm at any time. Moreover, we show that the spectral/ergodic specification of swarm behavior degrades gracefully as the number of agents goes down, enabling the operator to maintain the same approach as agents become disabled or are added to the network. We demonstrate the scale-invariance and dynamic response of our system in a field-relevant simulator on a variety of tactical scenarios with up to 50 agents. We also demonstrate the dynamic response of our system in the field with a smaller team of agents. Finally, we make the code for our system available.
  • Reinforcement Learning-Based Bucket Filling for Autonomous Excavation
    Item type: Journal Article
    Egli, Pascal; Terenzi, Lorenzo; Hutter, Marco (2024)
    IEEE Transactions on Field Robotics
    This article presents a bucket-filling controller for autonomous excavation. The key innovation of this controller is that it can react to the encountered soil conditions and adapt the excavation behavior online without the explicit knowledge of soil properties while respecting machine limitations to avoid stalling. At the same time, the controller takes into account the current terrain elevation and adheres to a maximum-depth constraint to achieve a desired design. The controller is trained entirely in simulation with reinforcement learning (RL). A simple analytical soil model based on the fundamental equation of Earth moving (FEE) is used to simulate ground interactions. To learn an appropriate excavation strategy for a wide variety of scenarios, soil parameters, as well as other properties of the environment, are randomized extensively during training. We test and evaluate the controller on a 12-ton excavator with a conventional two-stage hydraulic system in a wide range of different soil conditions. In addition, we show the excavation of a complete trench by integrating the controller into an autonomous excavation planning system. The experiments demonstrate that the controller can robustly adapt the excavation trajectory based on the encountered conditions and shows competitive performance compared to a professional machine operator.
  • RoadRunner—Learning Traversability Estimation for Autonomous Off-Road Driving
    Item type: Journal Article
    Frey, Jonas; Patel, Manthan; Atha, Deegan; et al. (2024)
    IEEE Transactions on Field Robotics
    Autonomous navigation at high speeds in off-road environments necessitates robots to comprehensively understand their surroundings using onboard sensing only. The extreme conditions posed by the off-road setting can cause degraded image quality as well as limited sparse geometric information available from light detection and ranging (LiDAR) sensing when driving at high speeds. In this work, we present RoadRunner, a novel framework capable of predicting terrain traversability and elevation directly from camera and LiDAR sensor inputs. RoadRunner enables reliable autonomous navigation by fusing sensory information and generates contextually informed predictions about the geometry and traversability of the terrain while operating at low latency. In contrast to existing methods, which rely on classifying handcrafted semantic classes and using heuristics to predict traversability costs, our method directly predicts traversability. It is trained on labels that can be automatically generated in hindsight in a self-supervised fashion. The RoadRunner network architecture builds upon advances from the autonomous driving domain, which allow us to embed LiDAR and camera information into a common bird’s eye-view perspective. Training is enabled by utilizing an existing traversability estimation stack to generate training data in hindsight in a scalable manner from real-world off-road driving datasets. Furthermore, RoadRunner improves the system latency by a factor of ~4, from 500 to 140 ms, while improving the accuracy for traversability costs and elevation map predictions. We demonstrate the effectiveness of RoadRunner in enabling safe and reliable off-road navigation at high speeds in multiple real-world driving scenarios through unstructured desert environments.
  • Steerable High Jumping Tensegrity Robot for Space Exploration
    Item type: Journal Article
    Kolt Green, Jonathan Jacob; Bozinovski, Dario; Tischhauser, Fabian; et al. (2025)
    IEEE Transactions on Field Robotics
    The growing interest in exploring other planets calls for innovative robotic systems capable of deploying to and traversing challenging space environments. While wheeled rovers have traditionally fulfilled this role, they face limitations, including configuration dependence (e.g., requiring an upright orientation), susceptibility to impacts, and difficulty overcoming obstacles larger than their wheel radius. Tensegrity-based robotics presents a promising alternative for future rovers. These lightweight, compliant structures offer compactability, adjustable stiffness, and the ability to absorb impacts without damage. Moreover, their unique form factor naturally protects scientific payloads. Recent research has explored tensegrity robots for rolling-based locomotion, with increasing interest in leveraging their structures for jumping-based movement. However, achieving hardware capable of high jumps greater than the robot’s body length and directional jumping control for steerable jumping remains a challenge. This work introduces a tensegrity robot that utilizes structural deformation for jumping locomotion. Through first-principles analyses, simulations, lab experiments, and field tests in a planetary analog environment, we demonstrate a robot capable of vertical jumps of 1.18m (1.93 body lengths), directional jumps covering horizontal distances up to 0.59m (0.97 body lengths), and surviving falls from heights of 21.5m (35.2 body lengths). The robot can also reduce its occupied volume by more than 4x without sustaining damage. Results herein highlight the potential of jumping tensegrity robots as robust, versatile platforms for next-generation space exploration.
Publications 1 - 7 of 7