Performance-guided Task-specific Optimization for Multirotor Design

This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.

IMU-Preintegrated Radar Factors for Asynchronous Radar-LiDAR-Inertial SLAM

Fixed-lag Radar-LiDAR-Inertial smoothers conventionally create one factor graph node per measurement to compensate for the lack of time synchronization between radar and LiDAR. For a radar-LiDAR sensor pair with equal rates, this strategy results in a state creation rate of twice the individual sensor frequencies. This doubling of the number of states per second yields high optimization costs, inhibiting real-time performance on resource-constrained hardware. We introduce IMU-preintegrated radar factors that use high-rate inertial data to propagate the most recent LiDAR state to the radar measurement timestamp. This strategy maintains the node creation rate at the LiDAR measurement frequency. Assuming equal sensor rates, this lowers the number of nodes by 50 % and consequently the computational costs. Experiments on a single board computer (which has 4 cores each of 2.2 GHz A73 and 2 GHz A53 with 8 GB RAM) show that our method preserves the absolute pose error of a conventional baseline while simultaneously lowering the aggregated factor graph optimization time by up to 56 %.

Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression

Efficient exploration of unknown environments is crucial for autonomous robots, especially in confined and large-scale scenarios with limited communication. To address this challenge, we propose a collaborative exploration framework for a marsupial ground-aerial robot team that leverages the complementary capabilities of both platforms. The framework employs a graph-based path planning algorithm to guide exploration and deploy the aerial robot in areas where its expected gain significantly exceeds that of the ground robot, such as large open spaces or regions inaccessible to the ground platform, thereby maximizing coverage and efficiency. To facilitate large-scale spatial information sharing, we introduce a bandwidth-efficient, task-driven map compression strategy. This method enables each robot to reconstruct resolution-specific volumetric maps while preserving exploration-critical details, even at high compression rates. By selectively compressing and sharing key data, communication overhead is minimized, ensuring effective map integration for collaborative path planning. Simulation and real-world experiments validate the proposed approach, demonstrating its effectiveness in improving exploration efficiency while significantly reducing data transmission.

Semantics-aware Predictive Inspection Path Planning

This paper presents a novel semantics-aware inspection path planning paradigm called "Semantics-aware Predictive Planning" (SPP). Industrial environments that require the inspection of specific objects or structures (called "semantics"), such as ballast water tanks inside ships, often present structured and repetitive spatial arrangements of the semantics of interest. Motivated by this, we first contribute an algorithm that identifies spatially repeating patterns of semantics - exact or inexact - in a semantic scene graph representation and makes predictions about the evolution of the graph in the unseen parts of the environment using these patterns. Furthermore, two inspection path planning strategies, tailored to ballast water tank inspection, that exploit these predictions are proposed. To assess the performance of the novel predictive planning paradigm, both simulation and experimental evaluations are performed. First, we conduct a simulation study comparing the method against relevant state-of-the-art techniques and further present tests showing its ability to handle imperfect patterns. Second, we deploy our method onboard a collision-tolerant aerial robot operating inside the ballast tanks of two real ships. The results, both in simulation and field experiments, demonstrate significant improvement over the state-of-the-art in terms of inspection time while maintaining equal or better semantic surface coverage. A set of videos describing the different parts of the method and the field deployments is available at https://tinyurl.com/spp-videos. The code for this work is made available at https://github.com/ntnu-arl/predictive_planning_ros.

Semantically-driven Deep Reinforcement Learning for Inspection Path Planning

This paper introduces a novel semantics-aware inspection planning policy derived through deep reinforcement learning. Reflecting the fact that within autonomous informative path planning missions in unknown environments, it is often only a sparse set of objects of interest that need to be inspected, the method contributes an end-to-end policy that simultaneously performs semantic object visual inspection combined with collision-free navigation. Assuming access only to the instantaneous depth map, the associated segmentation image, the ego-centric local occupancy, and the history of past positions in the robot's neighborhood, the method demonstrates robust generalizability and successful crossing of the sim2real gap. Beyond simulations and extensive comparison studies, the approach is verified in experimental evaluations onboard a flying robot deployed in novel environments with previously unseen semantics and overall geometric configurations.

CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments

Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. This becomes particularly challenging in perception-degraded subterranean conditions under harsh environmental factors, including darkness, dust, and geometrically self-similar structures. This paper details CompSLAM, a highly resilient and hierarchical multi-modal localization and mapping framework designed to address these challenges. Its flexible architecture achieves resilience through redundancy by leveraging the complementary nature of pose estimates derived from diverse sensor modalities. Developed during the DARPA Subterranean Challenge, CompSLAM was successfully deployed on all aerial, legged, and wheeled robots of Team Cerberus during their competition-winning final run. Furthermore, it has proven to be a reliable odometry and mapping solution in various subsequent projects, with extensions enabling multi-robot map sharing for marsupial robotic deployments and collaborative mapping. This paper also introduces a comprehensive dataset acquired by a manually teleoperated quadrupedal robot, covering a significant portion of the DARPA Subterranean Challenge finals course. This dataset evaluates CompSLAM's robustness to sensor degradations as the robot traverses 740 meters in an environment characterized by highly variable geometries and demanding lighting conditions. The CompSLAM code and the DARPA SubT Finals dataset are made publicly available for the benefit of the robotics community

A Neural Network Mode for PX4 on Embedded Flight Controllers

This paper contributes an open-sourced implementation of a neural-network based controller framework within the PX4 stack. We develop a custom module for inference on the microcontroller while retaining all of the functionality of the PX4 autopilot. Policies trained in the Aerial Gym Simulator are converted to the TensorFlow Lite format and then built together with PX4 and flashed to the flight controller. The policies substitute the control-cascade within PX4 to offer an end-to-end position-setpoint tracking controller directly providing normalized motor RPM setpoints. Experiments conducted in simulation and the real-world show similar tracking performance. We thus provide a flight-ready pipeline for testing neural control policies in the real world. The pipeline simplifies the deployment of neural networks on embedded flight controller hardware thereby accelerating research on learning-based control. Both the Aerial Gym Simulator and the PX4 module are open-sourced at https://github.com/ntnu-arl/aerial_gym_simulator and https://github.com/SindreMHegre/PX4-Autopilot-public/tree/for_paper. Video: https://youtu.be/lY1OKz_UOqM?si=VtzL243BAY3lblTJ.

Embedded Safe Reactive Navigation for Multirotors Systems using Control Barrier Functions

Aiming to promote the wide adoption of safety filters for autonomous aerial robots, this paper presents a safe control architecture designed for seamless integration into widely used open-source autopilots. Departing from methods that require consistent localization and mapping, we formalize the obstacle avoidance problem as a composite control barrier function constructed only from the online onboard range measurements. The proposed framework acts as a safety filter, modifying the acceleration references derived by the nominal position/velocity control loops, and is integrated into the PX4 autopilot stack. Experimental studies using a small multirotor aerial robot demonstrate the effectiveness and performance of the solution within dynamic maneuvering and unknown environments.

Olympus: A Jumping Quadruped for Planetary Exploration Utilizing Reinforcement Learning for In-Flight Attitude Control

Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a jumping legged robot tailored to the gravity of Mars. First, the design requirements are outlined followed by detailing how simulation enabled optimizing the robot's design - from its legs to the overall configuration - towards high vertical jumping, forward jumping distance, and in-flight attitude reorientation. Subsequently, the reinforcement learning policy used to track desired in-flight attitude maneuvers is presented. Successfully crossing the sim2real gap, extensive experimental studies of attitude reorientation tests are demonstrated.

Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots

This paper contributes the Aerial Gym Simulator, a highly parallelized, modular framework for simulation and rendering of arbitrary multirotor platforms based on NVIDIA Isaac Gym. Aerial Gym supports the simulation of under-, fully- and over-actuated multirotors offering parallelized geometric controllers, alongside a custom GPU-accelerated rendering framework for ray-casting capable of capturing depth, segmentation and vertex-level annotations from the environment. Multiple examples for key tasks, such as depth-based navigation through reinforcement learning are provided. The comprehensive set of tools developed within the framework makes it a powerful resource for research on learning for control, planning, and navigation using state information as well as exteroceptive sensor observations. Extensive simulation studies are conducted and successful sim2real transfer of trained policies is demonstrated. The Aerial Gym Simulator is open-sourced at: https://github.com/ntnu-arl/aerial_gym_simulator.