Navigation Of Aerial Robots Research Articles

Efficient Focus Autoencoders for Fast Autonomous Flight in Intricate Wild Scenarios

The autonomous navigation of aerial robots in unknown and complex outdoor environments is a challenging problem that typically requires planners to generate collision-free trajectories based on human expert rules for fast navigation. Presently, aerial robots suffer from high latency in acquiring environmental information, which limits the control strategies that the vehicle can implement. In this study, we proposed the SAC_FAE algorithm for high-speed navigation in complex environments using deep reinforcement learning (DRL) policies. Our approach consisted of a soft actor–critic (SAC) algorithm and a focus autoencoder (FAE). Our end-to-end DRL navigation policy enabled a flying robot to efficiently accomplish navigation tasks without prior map information by relying solely on the front-end depth frames and its own pose information. The proposed algorithm outperformed existing trajectory-based optimization approaches at flight speeds exceeding 3 m/s in multiple testing environments, which demonstrates its effectiveness and efficiency.

RRVPE: A Robust and Real-Time Visual-Inertial-GNSS Pose Estimator for Aerial Robot Navigation

Self-localization and orientation estimation are the essential capabilities for mobile robot navigation. In this article, a robust and real-time visual-inertial-GNSS(Global Navigation Satellite System) tightly coupled pose estimation (RRVPE) method for aerial robot navigation is presented. The aerial robot carries a front-facing stereo camera for self-localization and an RGB-D camera to generate 3D voxel map. Ulteriorly, a GNSS receiver is used to continuously provide pseudorange, Doppler frequency shift and universal time coordinated (UTC) pulse signals to the pose estimator. The proposed system leverages the Kanade Lucas algorithm to track Shi-Tomasi features in each video frame, and the local factor graph solution process is bounded in a circumscribed container, which can immensely abandon the computational complexity in nonlinear optimization procedure. The proposed robot pose estimator can achieve camera-rate (30 Hz) performance on the aerial robot companion computer. We thoroughly experimented the RRVPE system in both simulated and practical circumstances, and the results demonstrate dramatic advantages over the state-of-the-art robot pose estimators.

Twinning coordination action for spreading excellence in Aerial Robotics

Twinning coordination action for spreading excellence in Aerial Robotics AeRoTwin is a twinning coordination action for spreading excellence in aerial robotics. The project’s overall goal is to decrease networking gaps and deficiencies between UNIZG-FER and internationally-leading counterparts in the EU by significantly enhancing the S and T capacity of the Laboratory for Robotics and Intelligent Control Systems (LARICS) at UNIZG-FER. The strategic research domains are cooperative robotic missions, aerial robot navigation and aerial robot configurability.

PRGFlow: Unified SWAP‐aware deep global optical flow for aerial robot navigation

Global optical flow estimation is the foundation stone for obtaining odometry which is used to enable aerial robot navigation. However, such a method has to be of low latency and high robustness whilst also respecting the size, weight, area and power (SWAP) constraints of the robot. A combination of cameras coupled with inertial measurement units (IMUs) has proven to be the best combination in order to obtain such low latency odometry on resource-constrained aerial robots. Recently, deep learning approaches for visual inertial fusion have gained momentum due to their high accuracy and robustness. However, an equally noteworthy benefit for robotics of these techniques are their inherent scalability (adaptation to different sized aerial robots) and unification (same method works on different sized aerial robots). To this end, we present a deep learning approach called PRGFlow for obtaining global optical flow and then loosely fuse it with an IMU for full 6-DoF (Degrees of Freedom) relative pose estimation (which is then integrated to obtain odometry). The network is evaluated on the MSCOCO dataset and the dead-reckoned odometry on multiple real-flight trajectories without any fine-tuning or re-training. A detailed benchmark comparing different network architectures and loss functions to enable scalability is also presented. It is shown that the method outperforms classical feature matching methods by 2 × under noisy data. The supplementary material and code can be found at http://prg.cs.umd.edu/PRGFlow.

Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation

Classical haptic teleoperation systems heavily rely on operators’ intelligence and efforts in aerial robot navigation tasks, thereby posing significantly users’ workloads. In this paper, a novel shared control scheme is presented facilitating a multirotor aerial robot haptic teleoperation system that exhibits autonomous navigation capability. A hidden Markov model filter is proposed to identify the intention state of operator based on human inputs from haptic master device, which is subsequently adopted to derive goal position for a heuristic sampling based local path planner. The human inputs are considered as commanded velocity for a trajectory servo controller to drive the robot along the planned path. In addition, vehicle velocity is perceived by the user via haptic feedback on master device to enhance situation awareness and navigation safety of the user. An experimental study was conducted in a simulated and a physical environment, and the results verify the effectiveness of the novel scheme in safe navigation of aerial robots. A user study was carried out between a classical haptic teleoperation system and the proposed approach in the identical simulated complex environment. The flight data and task load index (TLX) are acquired and analyzed. Compared with the conventional haptic teleoperation scheme, the proposed scheme exhibits superior performance in safe and fast navigation of the multirotor vehicle, and is also of low task and cognitive loads.

ITC: Infused Tangential Curves for Smooth 2D and 3D Navigation of Mobile Robots †

Navigation is an indispensable component of ground and aerial mobile robots. Although there is a plethora of path planning algorithms, most of them generate paths that are not smooth and have angular turns. In many cases, it is not feasible for the robots to execute these sharp turns, and a smooth trajectory is desired. We present ‘ITC: Infused Tangential Curves’ which can generate smooth trajectories for mobile robots. The main characteristics of the proposed ITC algorithm are: (1) The curves are tangential to the path, thus maintaining continuity, (2) The curves are infused in the original global path to smooth out the turns, (3) The straight segments of the global path are kept straight and only the sharp turns are smoothed, (4) Safety is embedded in the ITC trajectories and robots are guaranteed to maintain a safe distance from the obstacles, (5) The curvature of ITC curves can easily be controlled and smooth trajectories can be generated in real-time, (6) The ITC algorithm smooths the global path on a part-by-part basis thus local smoothing at one point does not affect the global path. We compare the proposed ITC algorithm with traditional interpolation based trajectory smoothing algorithms. Results show that, in case of mobile navigation in narrow corridors, ITC paths maintain a safe distance from both walls, and are easy to generate in real-time. We test the algorithm in complex scenarios to generate curves of different curvatures, while maintaining different safety thresholds from obstacles in vicinity. We mathematically discuss smooth trajectory generation for both 2D navigation of ground robots, and 3D navigation of aerial robots. We also test the algorithm in real environments with actual robots in a complex scenario of multi-robot collision avoidance. Results show that the ITC algorithm can be generated quickly and is suitable for real-world scenarios of collision avoidance in narrow corridors.

Design Optimization of Sparse Sensing Array for Extended Aerial Robot Navigation in Deep Hazardous Tunnels

In this letter, the design optimization and localization with sparse sensing array of range-based sensors are presented. This optimization focuses on an optimized sparse sensing array for localization and autonomous navigation in tunnel environments with right rectangular prism geometry and the autonomous ascent and descent in vertical cylindrical shafts. An optimization problem is conceived to optimize sensor configuration to traverse both tunnels and shafts with constraints from the field of the view of the camera system and the mechanical layout. The optimized sensor configuration solved using a genetic algorithm performed on average 21 times better than configurations that were placed heuristically. The optimized design and the localization algorithm are implemented on SWIRL: Surveyor with intelligent rotating lens. The proposed localization method with sparse sensing results in at least four-fold reductions in the weight and eight-fold reduction in power consumption of the sensing system compared to existing ones. SWIRL was autonomously flown in a series of experiments, consisting of tests in shaft and tunnels in both indoor mock-up and field environment. Last, in an endurance test in a mock-up shaft, SWIRL achieved over 35 min of autonomous flight using the proposed sensing placement and scheme, making extended aerial robot navigation and inspection of deep hazardous tunnels possible.

Open-sector rapid-reactive collision avoidance: Application in aerial robot navigation through outdoor unstructured environments

A new reactive collision avoidance method for navigation of aerial robots (such as unmanned aerial vehicles (UAVs)) in unstructured urban/suburban environments is presented. Small form-factor aerial robots, such as quadcopters, often have limited payload capacity, flight time, processing power, and sensing capabilities. To enhance the capabilities of such vehicles without increasing weight or computing power, a reactive collision avoidance method based on open sectors is described. The method utilizes information from a two-dimensional laser scan of the environment and a short-term memory of past actions and can rapidly circumvent obstacles in outdoor urban/suburban environments. With no map required, the method enables the robot to react quickly and navigate even when the enivornment changes. Furthermore, the low computational requirement of the method allows the robot to quickly react to unknown obstacles that may be poorly represented in the scan, such as trees with branches and leaves. The method is validated in simulation results and through physical experiments on a prototype quadcopter system, where results show the robot flying smoothly around obstacles at a relatively high speed (3 m/s).

An architecture for robust UAV navigation in GPS‐denied areas

AbstractThis article presents a software architecture for safe and reliable autonomous navigation of aerial robots in GPS‐denied areas. The techniques employed within key modules from this architecture are explained in detail, such as a six‐dimensional localization approach based on visual odometry and Monte Carlo localization, or a variant of the Lazy Theta* algorithm for motion planning. The aerial robot used to demonstrate this approach has been extensively tested over the past 2 years for localization and state estimation without any external positioning systems, autonomous local obstacle avoidance, and local path planning among other tasks. This article describes the architecture and main algorithms used to achieve these goals to build a robust autonomous system.

Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

Integration of image de-blurring in an aerial Mono-SLAM

In this article, we discuss the possibility of integrating image de-blurring techniques in an aerial simultaneous localization and mapping by a single camera (monocular simultaneous localization and mapping (Mono-SLAM)). We use an integrated aerial virtual environment together with a six-degree-of-freedom aircraft flight simulator to show the effectiveness of the approach to generate three-dimensional flight trajectories via integration of image de-blurring in the associated loop of the Mono-SLAM. The objective is to increase the overall performance of a flying mission over an unknown area by means of a vision-only method. The integrated aerial virtual environment produces and collects real-time pictures from a nadir-looking vision sensor mounted on the vehicle. Our MATLAB GUI-based toolbox helps user to investigate an offline Mono-SLAM with a predefined de-blurring method integrated with an estimator which extracts navigational parameters. The system efficient architecture allows effective virtual experiments in a completely unknown environment, without using preloaded maps or predefined features. Simulation outcomes demonstrate the feasibility of navigation of aerial robots in inaccessible environments. Different case studies support the conclusion; nonetheless, we observe a number of nonlinearities from de-blurring filters even for a general aviation aircraft.