Planning paths that maximize information gain for robotic platforms has wide-ranging applications and significant potential impact. To effectively adapt to real-time data collection, informative path planning must be computed online and be responsive to new observations. In this work, we present IA-TIGRIS (Incremental and Adaptive Tree-based Information Gathering Using Informed Sampling), which is an incremental and adaptive sampling-based informative path planner designed for real-time onboard execution. Our approach leverages past planning efforts through incremental refinement while continuously adapting to updated belief maps. We additionally present detailed implementation and optimization insights to facilitate real-world deployment, along with an array of reward functions tailored to specific missions and behaviors. Extensive simulation results demonstrate IA-TIGRIS generates higher-quality paths compared to baseline methods. We validate our planner on two distinct hardware platforms: a hexarotor unmanned aerial vehicle (UAV) and a fixed-wing UAV, each having different motion models and configuration spaces. Our results show up to a 38% improvement in information gain compared to baseline methods, highlighting the planner's potential for deployment in real-world applications. Project website: ia-tigris.github.io.

Exploration is a critical challenge in robotics, centered on understanding unknown environments. In this work, we focus on structured indoor environments, which often exhibit predictable, repeating patterns. Conventional frontier-based exploration approaches have difficulty leveraging this predictability, relying on simple heuristics such as ‘closest first’ for exploration. More recent deep learning-based methods predict unknown regions of the map for information gain computation, but these approaches are often sensitive to the predicted map quality or fail to account for sensor coverage. To overcome these issues, our key insight is to jointly reason over what the robot can observe and its uncertainty to calculate probabilistic information gain. We introduce MapEx, a new exploration framework that uses predicted maps to form probabilistic sensor model for information gain estimation. MapEx generates multiple predicted maps based on observed information, and takes into consideration both the computed variances of predicted maps and estimated visible area to estimate the information gain of a given viewpoint. Experiments on the real-world KTH dataset showed on average 12.4% improvement than representative map-prediction based exploration and 25.4% improvement than nearest frontier approach. Website: https://mapex-explorer.github.io/

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Website: pipe-planner.github.io

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization. PyPose's architecture is tidy and well-organized, it has an imperative style interface and is efficient and user-friendly, making it easy to integrate into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and 2nd-order optimizers, such as trust region methods. Experiments show that PyPose achieves more than 10× speedup in computation compared to the state-of-the-art libraries. To boost future research, we provide concrete examples for several fields of robot learning, including SLAM, planning, control, and inertial navigation.

Detect-and-Avoid (DAA) capabilities are critical for safe operations of unmanned aircraft systems (UAS). This paper introduces, AirTrack, a real-time vision-only detect and tracking framework that respects the size, weight, and power (SWaP) constraints of sUAS systems. Given the low Signal-to-Noise ratios (SNR) of far away aircraft, we propose using full resolution images in a deep learning framework that aligns successive images to remove ego-motion. The aligned images are then used downstream in cascaded primary and secondary classifiers to improve detection and tracking performance on multiple metrics. We show that AirTrack outperforms state-of-the art baselines on the Amazon Airborne Object Tracking (AOT) Dataset. Multiple real world flight tests with a Cessna 182 interacting with general aviation traffic and additional near-collision flight tests with a Bell helicopter flying towards a UAS in a controlled setting showcase that the proposed approach satisfies the newly introduced ASTM F3442/F3442M standard for DAA. Empirical evaluations show that our system has a probability of track of more than 95% up to a range of 700m. [Video]11Video: https://youtu.be/bMw5nUGL5GQ

We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-wave infrared sensing of crew and vehicle assets amidst prescribed wildland fire environments. While such a dataset is crucial for safety monitoring in wildland fire applications, to the authors' awareness, no such dataset focusing on assets near fire is publicly available. Presumably, this is due to the barrier to entry of collaborating with fire management personnel. We present two related data subsets: WIT-UAS-ROS consists of full ROS bag files containing sensor and robot data of UAS flight over the fire, and WIT-UAS-Image contains hand-labeled long-wave infrared (LWIR) images extracted from WIT-UAS-ROS. Our dataset is the first to focus on asset detection in a wildland fire environment. We show that thermal detection models trained without fire data frequently detect false positives by classifying fire as people. By adding our dataset to training, we show that the false positive rate is reduced significantly. Yet asset detection in wildland fire environments is still significantly more challenging than detection in urban environments, due to dense obscuring trees, greater heat variation, and overbearing thermal signal of the fire. We publicize this dataset to encourage the community to study more advanced models to tackle this challenging environment. The dataset, code and pretrained models are available at https://github.com/castacks/WIT-UAS-Dataset.

Pilots operating aircraft in non-towered terminal airspace rely on their situational awareness and prior knowledge to predict the future trajectories of other agents. These predictions are conditioned on the past trajectories of other agents, agent-agent social interactions and environmental context such as airport location and weather. This paper provides a dataset, TrajAir, that captures this behaviour in non-towered terminal airspace around a regional airport. We also present a baseline socially-aware trajectory prediction algorithm, TrajAirNet, that uses the dataset to predict the trajectories of all agents. The dataset is collected for 111 days over 8 months and contains ADS-B transponder data along with the corresponding METAR weather data. The data is processed to be used as a benchmark with other publicly available social navigation datasets. To the best of the authors' knowledge, this is the first 3D social aerial navigation dataset, thus introducing social navigation for autonomous aviation. TrajAirNet combines state-of-the-art modules in social navigation to provide predictions in a static environment with a dynamic context. Both the TrajAir dataset and TrajAirNet prediction algorithm are open-source. [Dataset]11Dataset: https://theairlab.org/trajair/ [Code]22Codebase: https://github.com/castacks/trajairnet [Video]33Video: https://youtu.be/e1AQXrxB2gw

Informative path planning is an important and challenging problem in robotics that remains to be solved in a manner that allows for wide-spread implementation and real-world practical adoption. Among various reasons for this, one is the lack of approaches that allow for informative path planning in high-dimensional spaces and non-trivial sensor constraints. In this work we present a sampling-based approach that allows us to tackle the challenges of large and high-dimensional search spaces. This is done by performing informed sampling in the high-dimensional continuous space and incorporating potential information gain along edges in the reward estimation. This method rapidly generates a global path that maximizes information gain for the given path budget constraints. We discuss the details of our implementation for an example use case of searching for multiple objects of interest in a large search space using a fixed-wing UAV with a forward-facing camera. We compare our approach to a sampling-based planner baseline and demonstrate how our contributions allow our approach to consistently out-perform the baseline by 18.0%. With this we thus present a practical and generalizable informative path planning framework that can be used for very large environments, limited budgets, and high dimensional search spaces, such as robots with motion constraints or high-dimensional configuration spaces. [Code]aaCodebase: https://github.com/castacks/tigris [Video]bbVideo: https://youtu.be/bMw5nUGL5GQ

Energy management is a critical aspect of risk assessment for Uncrewed Aerial Vehicle (UAV) flights, as a depleted battery during a flight brings almost guaranteed vehicle damage and a high risk of human injuries or property damage. Predicting the amount of energy a flight will consume is challenging as routing, weather, obstacles, and other factors affect the overall consumption. We develop a deep energy model for a UAV that uses Temporal Convolutional Networks to capture the time varying features while incorporating static contextual information. Our energy model is trained on a real world dataset and does not require segregating flights into regimes. We illustrate an improvement in power predictions by 29% on test flights when compared to a state-of-the-art analytical method. Using the energy model, we can predict the energy usage for a given trajectory and evaluate the risk of running out of battery during flight. We propose using Conditional Value-at-Risk (CVaR) as a metric for quantifying this risk. We show that CVaR captures the risk associated with worst-case energy consumption on a nominal path by transforming the output distribution of Monte Carlo forward simulations into a risk space. Computing the CVaR on the risk-space distribution provides a metric that can evaluate the overall risk of a flight before take-off. Our energy model and risk evaluation method can improve flight safety and evaluate the coverage area from a proposed takeoff location. The video and codebase are available at: [Video]a| [Code]b

A high-quality estimate of wind fields can potentially improve the safety and performance of Unmanned Aerial Vehicles (UAVs) operating in dense urban areas. Computational Fluid Dynamics (CFD) simulations can help provide a wind field estimate, but their accuracy depends on the knowledge of the distribution of the inlet boundary conditions. This paper provides a real-time methodology using a Particle Filter (PF) that utilizes wind measurements from a UAV to solve the inverse problem of predicting the inlet conditions as the UAV traverses the flow field. A Gaussian Process Regression (GPR) approach is used as a surrogate function to maintain the real-time nature of the proposed methodology. Real-world experiments with a UAV at an urban test-site prove the efficacy of the proposed method. The flight test shows that the 95% confidence interval for the difference between the mean estimated inlet conditions and mean ground truth measurements closely bound zero, with the difference in mean angles being between -3.7° and 1.3° and the difference in mean magnitudes being between -0.2 m/s and 0.0 m/s.Video : https://youtu.be/U4XdYgSJRZM.