Fig 4 - uploaded by Lukas Schmid
Content may be subject to copyright.
Executed paths after 15 minutes of autonomous exploration in the maze scenario, starting from the blue pose. Although all three methods sample linear paths, our method's capability to refine the tree to maximize global utility produces less jagged paths. This results in a larger area being explored in the same time while covering a distance similar to AEP.
Source publication
The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this letter, we pres...
Similar publications
Structural columns are the crucial load-carrying components of buildings and bridges. Early detection of column damage is important for the assessment of the residual performance and the prevention of system-level collapse. This research proposes an innovative end-to-end micro aerial vehicles (MAVs)-based approach to automatically scan and inspect...
Citations
... It uses a locally generated RRT graph to select the adjacent node with the highest total information gain as the NBV. Similarly, Schmid et al. [76] selects a node from the graph map expanded by an operation inspired by RRT*. The new node is connected to the graph map based on its total information gain ratio to the distance cost. ...
... Visual completeness Bircher et al. [62], Fan et al. [31], Han et al. [67], Heng et al. [68], Hepp et al. [13], Huang et al. [9], Lee et al. [20], Meng et al. [72], Monica and Aleotti [41], Palazzolo and Stachniss [58], Potapova et al. [44], Schmid et al. [76], Shen et al. [78], Song ...
Recent advancements in three-dimensional (3D) scanning technology have broadened its applicability across fields like reverse engineering and autonomous robot navigation, which require a 3D representation of real-world objects. A key aspect of this digitalization process is autonomous view planning. This paper comprehensively reviews autonomous view planning techniques for 3D scanning of unknown or partially known geometries. The objective is to plan optimal scanning paths or views that supplement areas with insufficient or missing data. While the fields of graphics and robotics have approached automatic 3D scanning from different perspectives, they share a common goal of finding optimal views or paths to enhance an object's 3D digital model. This review examines the essential properties of the autonomous view planning process and classifies selected studies based on these aspects, with research trends visualized through a Sankey diagram. Finally, current limitations are discussed, and future directions necessary for practical implementation are explored.
... Primatesta et al. presented kinodynamic constrained UAV motion planning algorithm leveraging the distinctive properties of a Rapidly-exploring Random Tree (RRT) and model predictive control [13]. Schmid et al. proposed RRT * -inspired online informative path planning algorithm which eliminates the local minima and the sub-optimal path convergence problems [14]. Madridano et al. developed a 3D probabilistic roadmap approach that can plan multiple trajectories in case of emergencies [15]. ...
Unmanned air vehicles operate in highly dynamic and unknown environments where they can encounter unexpected and unseen failures. In the presence of emergencies, autonomous unmanned air vehicles should be able to land at a minimum distance or minimum time. Impaired unmanned air vehicles define actuator failures and this impairment changes their unstable and uncertain dynamics; henceforth, path planning algorithms must be adaptive and model-free. In addition, path planning optimization problems must consider the unavoidable actuator saturations, kinematic and dynamic constraints for successful real-time applications. Therefore, this paper develops 3D path planning algorithms for quadrotors with parametric uncertainties and various constraints. In this respect, this paper constructs a multi-dimensional particle swarm optimization and a multi-dimensional genetic algorithm to plan paths for translational, rotational, and Euler angles and generates the corresponding control signals. The algorithms are assessed and compared both in the simulation and experimental environments. Results show that the multi-dimensional genetic algorithm produces shorter minimum distance and minimum time paths under the constraints. The real-time experiments prove that the quadrotor exactly follows the produced path utilizing the available maximum rotor speeds.
... Prior research has predominantly estimated node gains through visibility-based ray tracing propagation, which identifies unknown voxels in the proximity of nodes Selin et al. (2019). However, the study Schmid et al. (2020) has indicated that the time spent on ray tracing estimation for node gains accounts for 95% of the total planning time. ...
Exploring large-scale environments autonomously poses a significant challenge. As the size of environments increases, the computational cost becomes a hindrance to real-time operation. Additionally, while frontier-based exploration planning provides convenient access to environment frontiers, it suffers from slow global exploration speed. On the other hand, sampling-based methods can effectively explore individual regions but fail to cover the entire environment. To overcome these limitations, we present a hierarchical exploration approach that integrates frontier-based and sampling-based methods. It assesses the informational gain of sampling points by considering the quantity of frontiers in the vicinity, and effectively enhances exploration efficiency by utilizing a utility function that takes account of the direction of advancement for the purpose of selecting targets. To improve the search speed of global topological graph in large-scale environments, this paper introduces a method for constructing a sparse topological graph. It incrementally constructs a three-dimensional sparse topological graph by dynamically capturing the spatial structure of free space through uniform sampling. In various challenging simulated environments, the proposed approach demonstrates comparable exploration performance in comparison with the state-of-the-art approaches. Notably, in terms of computational efficiency, the single iteration time of our approach is less than one-tenth of that required by the recent advances in autonomous exploration.
... Manifold Gaussian processes with geodesic kernel functions were utilized to map the topological data and perform real-time IPP using receding horizon approach by leveraging spatial correlations. Schmid et al. [39] developed a RRT* based online IPP model, where a single trajectory tree is continuously expanded to obtain global coverage. Xiao et al. [40] developed a novel technique based on global Kriging variance to optimize the nonmyopic efficiency. ...
... Furthermore, traditional exploration techniques [7][8][9][10] are based on the assumption that the explorer knows their position and attitude in real-time. However, navigation algorithms may not be able to estimate their position or attitude due to sensor errors. ...
... The higher the condition number, the more the color changes from yellow to red. It has been shown in [9] that LiDAR SLAM does not estimate its position and diverges when the robot is between (−30, −7.5) and (−10, 5), as shown in the bottom region of Figure 6. Therefore, we tested the proposed algorithm in the same map environment to verify whether or not the exploration can be successfully completed without navigation divergence. ...
... corridor: a corridor with no obstacles from the bottom, a corridor with complex obstacles, and a corridor with simple obstacle gates. It has been shown in [9] that LiDAR SLAM does not estimate its position and diverges when the robot is between (−30, −7.5) and (−10, 5), as shown in the bottom region of Figure 6. Therefore, we tested the proposed algorithm in the same map environment to verify whether or not the exploration can be successfully completed without navigation divergence. ...
This paper explores the research topic of enhancing the reliability of unmanned mobile exploration using LiDAR SLAM. Specifically, it proposes a technique to analyze waypoints where 3D LiDAR SLAM can be smoothly performed in potential exploration areas and points where there is a risk of divergence in navigation estimation. The goal is to improve exploration performance by presenting a method that secures these candidate regions. The analysis employs a 3D geometric observability matrix and its condition number to discriminate waypoints. Subsequently, the discriminated values are applied to path planning, resulting in the derivation of a final destination path connecting waypoints with a satisfactory SLAM position and attitude estimation performance. To validate the proposed technique, performance analysis was initially conducted using the Gazebo simulator. Additionally, experiments were performed with an autonomous unmanned vehicle in a real-world environment.
... They also focus on strategies to modify plans online for adaptive scenarios. Schmid et al., 2020 propose an optimal motion planning-driven IPP algorithm to overcome the suboptimality of sampling-based methods. ...
Robotics has dramatically increased our ability to gather data about our environments, creating an opportunity for the robotics and algorithms communities to collaborate on novel solutions to environmental monitoring problems. To understand a taxonomy of problems and methods in this realm, we present the first comprehensive survey of decision-theoretic approaches that enable efficient sampling of various environmental processes. We investigate representations for different environments, followed by a discussion of using these presentations to solve tasks of interest, such as learning, localization, and monitoring. To efficiently implement the tasks, decision-theoretic optimization algorithms consider: (1) where to take measurements from, (2) which tasks to be assigned, (3) what samples to collect, (4) when to collect samples, (5) how to learn environment; and (6) who to communicate. Finally, we summarize our study and present the challenges and opportunities in robotic environmental monitoring.
... The authors in [1] propose Cauchy-Schwartz quadratic mutual information to evaluate trajectories during exploration, whereas in [25], a Shannon mutual information formulation in Truncated Signed Distance Fields (TSDF) for active exploration is presented. Also, [26] proposed a 3D Reconstruction Gain based on the TSDF that was later implemented in an online informative path planning algorithm based on RRT * . In this case, the algorithm continuously expands an exploration tree, keeping non-executed parts of newly generated sampled viewpoints alive, proving through experiments to overcome costly RRT computation and addressing local minimum problems. ...
... The drone is equipped with a stereo camera that provides real-time feedback on the simulated environment by building a map using depth images. For the sampling-based path planner, we use an opensource modular framework from [26], adapted to PX4-SITL as in [22]. The framework's modularity allows us to test the variation in the exploration performance by changing the decision-making module with respect to different configurations and settings. ...
... The selected synthetic environment consists of a 40 × 40 m maze-like environment with a maximum flight height of 2.5 m. This scenario is a testbed challenging well-known environment presented in the literature [26], [30]. In addition, under the premise that exploration algorithms will eventually explore the whole environment, the duration of the simulated experiments is limited to 15 min to resemble the average autonomy of a standard UAV system. ...
... It then determines whether to continue extending and searching for the next sample point when the initial and target states are within the graph or can be connected to the graph. Commonly used methods include probabilistic road map (PRM) and rapidly exploring random tree (RRT) [6,7]. However, using this method often results in curvature-discontinuous paths and has limited convergence in dynamic environments. ...
Establishing an accurate and computationally efficient model for driving risk assessment, considering the influence of vehicle motion state and kinematic characteristics on path planning, is crucial for generating safe, comfortable, and easily trackable obstacle avoidance paths. To address this topic, this paper proposes a novel dual-layered dynamic path-planning method for obstacle avoidance based on the driving safety field (DSF). The contributions of the proposed approach lie in its ability to address the challenges of accurately modeling driving risk, efficient path smoothing and adaptability to vehicle kinematic characteristics, and providing collision-free, curvature-continuous, and adaptable obstacle avoidance paths. In the upper layer, a comprehensive driving safety field is constructed, composed of a potential field generated by static obstacles, a kinetic field generated by dynamic obstacles, a potential field generated by lane boundaries, and a driving field generated by the target position. By analyzing the virtual field forces exerted on the ego vehicle within the comprehensive driving safety field, the resultant force direction is utilized as guidance for the vehicle’s forward motion. This generates an initial obstacle avoidance path that satisfies the vehicle’s kinematic and dynamic constraints. In the lower layer, the problem of path smoothing is transformed into a standard quadratic programming (QP) form. By optimizing discrete waypoints and fitting polynomial curves, a curvature-continuous and smooth path is obtained. Simulation results demonstrate that our proposed path-planning algorithm outperforms the method based on the improved artificial potential field (APF). It not only generates collision-free and curvature-continuous paths but also significantly reduces parameters such as path curvature (reduced by 62.29% to 87.32%), curvature variation rate, and heading angle (reduced by 34.11% to 72.06%). Furthermore, our algorithm dynamically adjusts the starting position of the obstacle avoidance maneuver based on the vehicle’s motion state. As the relative velocity between the ego vehicle and the obstacle vehicle increases, the starting position of the obstacle avoidance path is adjusted accordingly, enabling the proactive avoidance of stationary or moving single and multiple obstacles. The proposed method satisfies the requirements of obstacle avoidance safety, comfort, and stability for intelligent vehicles in complex environments.
... Path planning algorithms [1][2][3][4][5] have been extensively studied for various applications in robotics, autonomous vehicles, and other areas requiring efficient navigation in complex environments. In this section, we provide a brief overview of the most relevant existing path planning techniques and highlight their strengths and limitations in comparison to the proposed Adaptive Surface Connectivity Path Planning (ASCPP) algorithm. ...
... In [23], the authors combine the exploring efficiency from a volumetric perspective with consideration of the quality of the observed surface to ensure quality 3D modelling of the structure. Similarly, in [24], the authors present an online sampling-based informative path planner which focuses on growing a solitary RRT-tree to explore and complete coverage of a 3D environment. The surface quality is determined through the formulation of gain function modelled from the weighted score of Truncated Signed Distance Field (TSDF) values of neighbouring constructed surfaces. ...
... However, the framework in [10] was tuned to the best of our capability to ensure an overall better performance in the simulated urban world. Moreover, implementation of the related state-of-art open-source repositories [24,40] with UnrealEngine [41] and AirSim [42] is currently incompatible with our established Gazebo [35] and ROS [34] setup. Fig. 11. Figure 34 presents the total covered surface area determined over the length of the simulation run by the two frameworks. ...
This paper highlights the significance of maintaining and enhancing situational awareness in Urban Search and Rescue (USAR) missions. It focuses specifically on investigating the capabilities of Unmanned Aerial Vehicles (UAV) equipped with limited sensing capabilities and onboard computational resources to perform visual inspections of apriori unknown fractured and collapsed structures in unfamiliar environments. The proposed approach, referred to as First Look Inspect-Explore (FLIE), employs a flexible bifurcated behavior tree that leverages real-time RGB image and depth cloud data. By employing a recursive and reactive synthesis of safe view pose within the inspection module, FLIE incorporates a novel active visual guidance scheme for identifying previously inspected surfaces. Furthermore, the integration of a tiered hierarchical exploration module with the visual guidance system enables the UAV to navigate towards new and unexplored structures without relying on a map. This decoupling reduces memory overhead and computational effort by eliminating the need to plan based on an incrementally built, error-prone global map. The proposed autonomy is extensively evaluated through simulation and experimental verification under various scenarios and compared against state-of-art approaches, demonstrating its performance and effectiveness.
