The multi-drone waypoint traversal has significant potential for aerial robot swarms in various applications. However, it still faces challenges including low time efficiency, susceptibility to local minima, poor resilience to external disturbances, high computational complexity, and high communication burden. To address these issues, we propose a two-stage swarm planning framework by integrating an offline global trajectory generator and an online distributed local trajectory planner. This approach not only ensures time-optimality but also enhances resistance to external disturbances. Specifically, a complementary progress constraint (CPC)-based global trajectory planning method is first presented to generate globally optimal reference trajectories. Then, by taking these trajectories as global guidance, a local planner is designed to guarantee collision-free traversal. In the local planner, we present a distributed local re-planning algorithm by embedding the positional constraints constructed by Voronoi diagrams into the model predictive contouring control (MPCC). The drones only exchange their position information, significantly reducing the communication load. Additionally, the Voronoi-based spatial constraints allow the swarms to eliminate the collision risk caused by asynchronous communication. To reduce onboard computational resource requirements, the local planner adopts the real-time iteration (RTI) technique, executing the optimization only once per control cycle. Both simulation and real-world experiments demonstrate that our approach outperforms state-of-the-art methods in terms of waypoint tracking accuracy, safety, and global time optimality.

Whole-body planning is critical for enabling robots to navigate effectively in complex and dense environments. Traditional obstacle-based planning methods methods often restrict the representation of both robots and obstacles to simple convex polyhedra. This limitation may fail to adequately address intricate geometries of real-world obstacles involved in constructing compact convex polyhedral envelopes around more intricate obstacle shapes found in such environments. In this paper, we propose an approximate convex decomposition (ACD) based method to generate convex polyhedral maps that effectively represent the non-convex shapes of robots as assemblies of multiple convex objects. Furthermore, we propose a differentiable convex polyhedron collision evaluation method to facilitate collision detection. Extensive experiments demonstrate that our method not only enhances the accuracy of collision detection in cluttered environments but also expands the potential applications of robotics in complex scenarios.

ABSTRACT The severe radial distortion of ultra‐wide field of view (FOV) fisheye camera results in poor model fitting and challenges in calibration board detection. In this paper, a novel calibration method for ultra‐wide FOV fisheye cameras is proposed based on improved camera model and SE(3) image pre‐correction. Initially, a method to extend the maximum fitting FOV of the camera model to over 180 degrees is proposed. Subsequently, a calibration board detection approach is proposed using SE(3) image pre‐correction. Specifically, image pre‐correction is incorporated into the camera calibration process, utilizing SE(3) to define the pre‐correction plane. Calibration boards are detected within the pre‐corrected images, enhancing the reliability, accuracy and speed of board detection in distorted images, consequently increasing the control point's maximum FOV. Lastly, the improved camera model and SE(3) image pre‐correction are integrated into a feedback‐based camera calibration system for ultra‐wide FOV fisheye cameras. Operating with real‐time or offline video streams as input, this system autonomously selects calibration key frames, optimizes camera parameters and calibration board poses in real‐time. Simulation and real‐world experiments verify the effectiveness of the proposed method, leading to a 62% increase in the achievable maximum FOV.

17-point algorithm is a popular method in relative pose estimation of multi-cameras. However, the role of overlap in 17-point algorithm remains unexplored. And the relaxed way in solving constrained normal equation leads to sub-optimal results. Both of them influence accuracy of the estimated pose. In this paper, we theoretically analyze the influence of overlap and the solvability of 17-point algorithm. In addition, we show that the abuse of overlap can harm accuracy in practice. In light of these findings, we propose an improved 17-point algorithm, which avoids using overlaps and derives a simple way to solve normal equation on manifold. Both simulations and real world data experiments demonstrate the proposed one outperforms the traditional 17-point algorithm in term of accuracy.

Efficient interest point detection and description in images play a crucial role in many tasks such as multi-robot SLAM and collaborative localization. To facilitate fast detection and generate compact descriptions on edge devices, we introduce EdgePoint, a lightweight neural network. We design a new detection loss UnfoldSoftmax to improve inference speed. Futhermore, we propose Ortho-Alignment loss combined with LocalPCA compression to learn compact 32-dimensional descriptors. To enable efficient storage or communication, we also quantize the generated descriptors into integral values. We perform EdgePoint on various datasets, and show that it surpasses SuperPoint in performance while utilizing only 1% of the parameters and achieving up to more than 10 times faster inference speed. By applying descriptor quantization, the requirements for storage and communication can be reduced by up to 97% without performance decreasing.

This paper addresses the consistency issue of multi-robot distributed cooperative localization. We introduce a consistent distributed cooperative localization algorithm conducting state estimation in a transformed coordinate. The core idea involves a linear time-varying coordinated transformation to render the propagation Jacobian independent of the state and make it suitable for a distributed manner. This transformation is seamlessly integrated into a server-based distributed cooperative localization framework, in which each robot estimates its own state while the server maintains the cross-correlations. The transformation ensures the correct observability property of the entire framework. Moreover, the algorithm accommodates various types of robot-to-robot relative measurements, broadening its applicability. Through simulations and real-world dataset experiments, the proposed algorithm has demonstrated better performance in terms of both consistency and accuracy compared to existing algorithms.

In this paper, we propose a novel mapping-planning framework for autonomous quadrotor navigation. First, a polyhedron-based mapping algorithm is presented to fully exploit the information of the onboard sensor data. Polyhedra are generated to approximate the segmented clusters of occupied voxels. Then, customized data structures are designed to extract information for motion planning in real time. With complete knowledge of the shape, position, and number of the observed obstacles, we can conveniently generate smooth trajectories with sufficient obstacle clearance along the most desired direction. Before searching for the initial path, a local topological graph is constructed to keep the path expanding in the most favorable topology class. The following path search is segmented based on the graph vertices, which allows fast convergence. The refined trajectory is obtained after smoothing, and large deviations are penalized in the formulated optimization problem to preserve the original clearance. Finally, we analyze and validate the proposed framework through extensive simulations and real-world quadrotor flights.

In this paper, we revisit the inconsistency problem of EKF-based cooperative localization (CL) from the perspective of system decomposition. By transforming the linearized system used by the standard EKF into its Kalman observable canonical form, the observable and unobservable components of the system are separated. Consequently, the factors causing the dimension reduction of the unobservable subspace are explicitly isolated in the state propagation and measurement Jacobians of the Kalman observable canonical form. Motivated by these insights, we propose a new CL algorithm called KD-EKF which aims to enhance consistency. The key idea behind the KD-EKF algorithm involves perform state estimation in the transformed coordinates so as to eliminate the influencing factors of observability in the Kalman observable canonical form. As a result, the KD-EKF algorithm ensures correct observability properties and consistency. We extensively verify the effectiveness of the KD-EKF algorithm through both Monte Carlo simulations and real-world experiments. The results demonstrate that the KD-EKF outperforms state-of-the-art algorithms in terms of accuracy and consistency.

Suspension operation reliability is one of the most significant performance indexes that concerns maneuvering stability and drive safety. In this paper, in order to guarantee good suspension reliability, an adaptive fault detection and isolation scheme is proposed for quarter-car active suspension systems with parametric and nonlinear uncertainties. To realize fault diagnosis for active suspensions, an adaptive fault detection estimator and several fault isolation estimators are designed to generate state residuals. Corresponding adaptive thresholds are developed to help judge the occurrence and type of possible faults. In the process of constructing state estimators, the uncertain parameter is updated online so that good sensitivity of fault detection can be achieved. Illustrative simulation is carried out to validate the effectiveness of the fault diagnosis scheme, and results indicate that the proposed method is sensible to sudden faults and maintain robustness to model uncertainties appearing in suspension systems.

In this paper, we study H/sub /spl infin// control for multirate sampled-data system with time-delay. In allusion to the problems of time-delay and multirate sampling, we describe the system with a time-varying jump system characterization. Then basing on the technique of continuous lifting, the hybrid, linear, time-varying, jump system is isometrically mapped to a discrete-time, linear, time-varying system with infinite-dimensional input-output spaces. Finally by making use of the definition of the system function for a discrete-time, time-varying system and the property of symplectic pair, the infinite-dimensional system is transformed to an equivalent discrete-time system with finite-dimensional input-output spaces which can be disposed with well developed H/sub /spl infin// control theory. As a result, the proposed design method can solve H/sub /spl infin// control problem for multirate sampled-data system with time-delay.