In many situations such as city delivery and wild inspection, quadrotors are often required to follow a predefined reference trajectory. However, these reference trajectories cannot be perfectly safe, resulting in conflicts between tracking the reference precisely, flying safely, and finishing the mission timely. This paper proposes to solve the above problem, by introducing a replanning framework that first generates a topological consistent collision-free initial path and then flexibly optimizes the rejoin point and trajectory duration to generate a smooth and safe local rejoining trajectory. To avoid local trajectory switching in different directions during high-frequency replanning, we propose a topology-preserving path search algorithm based on kinodynamic RRT*. To satisfy dynamic constraints, avoid delays, and achieve a smooth rejoin of the reference trajectory, we propose an optimization-based approach to refine the initial trajectory. The simulation results confirm that our proposed topological consistency and flexible optimization methods can reduce the risk of local trajectory and decrease obstacle avoidance delay for tracking reference trajectory. We also conduct real-world experiments in challenging environments and verify the effectiveness of our method.

In constrained solution spaces with a huge number of homotopy classes, standalone sampling-based kinodynamic planners suffer low efficiency in convergence. Local optimization is integrated to alleviate this problem. In this paper, we propose to thrive the trajectory tree growing by optimizing the tree in the forms of deformation units, and each unit contains one tree node and all the edges connecting it. The deforming proceeds both spatially and temporally by optimizing the node state and edge time durations efficiently. Deforming the unit only changes the tree locally yet improves the overall quality of a corresponding subtree. Further, to consider the computation burden and optimizing level, patterns to deform different tree parts in combination of different deformation units are studied and compared, all showing much faster convergence. The proposed deformation can be easily integrated into different RRT-based kinodynamic planning methods, and numerical experiments show that integrating the spatio-temporal deformation greatly accelerates the convergence and outperforms the spatial-only deformation.

For real-time multirotor kinodynamic planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a bidirectional global sampling process. The incorporation of the local optimization shows significantly improved success rates and less planning time in various types of challenging environments. We further present a refinement module utilizing the same framework as the regional optimizer. It comprehensively investigates the resulting trajectory of the global sampling and improves its smoothness with nearly negligible computation effort. Benchmark results illustrate that our proposed method can better exploit a previous trajectory compared to the state-of-the-art ones. The planning methods are applied to generate trajectories for a quadrotor system in simulation and real-world, and their capability is validated in real-time applications.

For quadrotor trajectory planning, describing a polynomial trajectory through coefficients and end-derivatives both enjoy their own convenience in energy minimization. We name them double descriptions of polynomial trajectories. The transformation between them, causing most of the inefficiency and instability, is formally analyzed in this paper. Leveraging its analytic structure, we design a linear-complexity scheme for both jerk/snap minimization and parameter gradient evaluation, which possesses efficiency, stability, flexibility, and scalability. With the help of our scheme, generating an energy optimal (minimum snap) trajectory only costs 1 µs per piece at the scale up to 1,000,000 pieces. Moreover, generating large-scale energy-time optimal trajectories is also accelerated by an order of magnitude against conventional methods.

In autonomous navigation, sensors suffer from massive occlusion in cluttered environments, leaving a significant amount of space unknown. In practice, treating the unknown space in optimistic or pessimistic ways both set limitations on planning performance. Therefore, aggressiveness and safety cannot be satisfied at the same time. Mimicking human behavior, in this paper, we propose a method based on deep neural network to predict occupancy distribution of unknown space. Specifically, the proposed method utilizes contextual information of environments and prior knowledge to predict obstacle distributions in the occluded space. Our self-supervised learning method use unlabeled and no-ground-truth data and augments the data by simulating navigation trajectories. Our Occupancy Prediction Network is faster than current SOTA scene completion models and is successfully applied to unseen test environments without any refinement. Results show that our predictor leverages the performance of a kinodynamic planner by improving security with no reduction of speed in cluttered environments.