Data-Driven MPC for Quadrotors

Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modelling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.Comment: 8 page

Computing Similarity between a Pair of Trajectories

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling

Data-efficient Non-parametric Modelling and Control of an Extensible Soft Manipulator

Data-driven approaches have shown promising results in modeling and controlling robots, specifically soft and flexible robots where developing physics-based models are more challenging. However, these methods often require a large number of real data, and gathering such data is time-consuming and can damage the robot as well. This paper proposed a novel data-efficient and non-parametric approach to develop a continuous model using a small dataset of real robot demonstrations (only 25 points). To the best of our knowledge, the proposed approach is the most sample-efficient method for soft continuum robot. Furthermore, we employed this model to develop a controller to track arbitrary trajectories in the feasible kinematic space. To show the performance of the proposed approach, a set of trajectory-tracking experiments has been conducted. The results showed that the robot was able to track the references precisely even in presence of external loads (up to 25 grams). Moreover, fine object manipulation experiments were performed to demonstrate the effectiveness of the proposed method in real-world tasks. Finally, we compared its performance with common data-driven approaches in seen/useen-before trajectory tracking scenarios. The results validated that the proposed approach significantly outperformed the existing approaches in unseen-before scenarios and offered similar performance in seen-before scenarios

Automatic aerial target detection and tracking system in airborne FLIR images based on efficient target trajectory filtering

Common strategies for detection and tracking of aerial moving targets in airborne Forward-Looking Infrared (FLIR) images offer accurate results in images composed by a non-textured sky. However, when cloud and earth regions appear in the image sequence, those strategies result in an over-detection that increases very significantly the false alarm rate. Besides, the airborne camera induces a global motion in the image sequence that complicates even more detection and tracking tasks. In this work, an automatic detection and tracking system with an innovative and efficient target trajectory filtering is presented. It robustly compensates the global motion to accurately detect and track potential aerial targets. Their trajectories are analyzed by a curve fitting technique to reliably validate real targets. This strategy allows to filter false targets with stationary or erratic trajectories. The proposed system makes special emphasis in the use of low complexity video analysis techniques to achieve real-time operation. Experimental results using real FLIR sequences show a dramatic reduction of the false alarm rate, while maintaining the detection rate

K-Nearest Neighbours Based Classifiers for Moving Object Trajectories Reconstruction

This article presents an exemplary prototype implementation of an Application Programming Interface (API) for incremental reconstruction of the trajectories of moving objects captured by Closed-Circuit Television (CCTV) and High-Definition Television (HDTV) cameras based on KNearest Neighbor (KNN) classifiers. This paper proposes a model-driven approach for trajectory reconstruction based on machine learning algorithms which is more efficient than other approaches for dynamic tracking, such as RGB-D (Red, Green and Red Color model with Depth) images or scale or rotation approaches. The existing approaches typically need a low-level information from the input video stream but the environment factors (indoor light, outdoor light) affect the results. The use of a predefined model allows to avoid this since the data is naturally filtered. Experiments on different input video streams demonstrate that the proposed approach is efficient for solving the tracking of moving objects in input streams in real time because it needs less granular information from the input stream. The research reported here is part of a research program of the Cyber Security Research Centre of London Metropolitan University for real-time video analytics with applicability to surveillance in security, disaster recovery and safety management, and customer insight

Coordinated Turn Trajectory Generation and Tracking Control for Multi-Rotors Operating in Urban Environment

The paper presents an efficient trajectory generation and tracking approach for multi-rotor air vehicles operating in urban environment, which takes into account uncertainties in the urban wind field and in the vehicle's parameters. Generated trajectories are sufficiently smooth, based on the differential flatness of the vehicle's dynamics and optimal in the sense of minimum agility and time. They pass through given set of way points, guarantee flight without a side-slip, and satisfy vehicle's dynamics and actuators constraints. In addition, an algorithm is presented to compute the required power to traverse the generated trajectory. Presented algorithms are implementable in real time using on-board computers. They do not take into account the vehicle's existing flight controller, hence there is no guarantee that the controller will be able to provide acceptable tracking of the generated trajectory, especially in the presence of atmospheric disturbances. To this end, we propose an adaptive augmentation algorithm to improve vehicle's performance by taking into account the effects of disturbances and on-line estimates of vehicle's existing flight controller's gains. The algorithms have been verified by simulations using DJI S1000 octocopter's model

ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM

Real-time six degrees-of-freedom pose estimation with ground vehicles represents a relevant and well-studied topic in robotics due to its many applications such as autonomous driving and 3D mapping. Although some systems already exist, they are either not accurate or they struggle in real-time settings. In this letter, we propose a fast, accurate and modular LiDAR SLAM system for both batch and online estimation. We first apply downsampling and outlier removal, to filter out noise and reduce the size of the input point clouds. Filtered clouds are then used for pose tracking, possibly aided by a pre-tracking module, and floor detection, to ground optimize the estimated trajectory. Efficient multi-steps loop closure and pose optimization, achieved through a g2o pose graph, are the last steps of the proposed SLAM pipeline. We compare the performance of our system with state-of-the-art point cloud-based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The comparison is done evaluating the estimated trajectory displacement using the KITTI (urban driving) and Chilean (underground mine) datasets

Coordinated Turn Trajectory Generation and Tracking Control for Multi-rotors Operating in Urban Environment

The paper presents an efficient trajectory generation and tracking approach for multi-rotor air vehicles operating in urban environment, which takes into account uncertainties in the urban wind field and in the vehicle's parameters. Generated trajectories are sufficiently smooth, based on the differential flatness of the vehicle's dynamics and optimal in the sense of minimum agility and time. They pass through given set of way points, guarantee flight without a side-slip, and satisfy vehicle's dynamics and actuator constraints. In addition, an algorithm is presented to compute the required power to traverse the generated trajectory. Presented algorithms are implementable in real time using on-board computers. They do not take into account the vehicle's existing flight controller, hence there is no guarantee that the controller will be able to provide acceptable tracking of the generated trajectory, especially in the presence of atmospheric disturbances. To this end, we propose an adaptive augmentation algorithm to improve vehicle's performance by taking into account the effects of disturbances and on-line estimates of vehicle's existing flight controller's gains. The algorithms have been verified by simulations using DJI S1000 octocopter's model