Gradient-based planners are widely used for quadrotor local planning, in which a Euclidean Signed Distance Field (ESDF) is crucial for evaluating gradient magnitude and direction. Nevertheless, computing such a field has much redundancy since the trajectory optimization procedure only covers a very limited subspace of the ESDF updating range. In this paper, an ESDF-free gradient-based planning framework is proposed, which significantly reduces computation time. The main improvement is that the collision term in penalty function is formulated by comparing the colliding trajectory with a collision-free guiding path. The resulting obstacle information will be stored only if the trajectory hits new obstacles, making the planner only extract necessary obstacle information. Then, we lengthen the time allocation if dynamical feasibility is violated. An anisotropic curve fitting algorithm is introduced to adjust higher order derivatives of the trajectory while maintaining the original shape. Benchmark comparisons and real-world experiments verify its robustness and high-performance. The source code is released as ros packages.

Aerial robots are widely deployed, but highly cluttered environments such as dense forests remain inaccessible to drones and even more so to swarms of drones. In these scenarios, previously unknown surroundings and narrow corridors combined with requirements of swarm coordination can create challenges. To enable swarm navigation in the wild, we develop miniature but fully autonomous drones with a trajectory planner that can function in a timely and accurate manner based on limited information from onboard sensors. The planning problem satisfies various task requirements including flight efficiency, obstacle avoidance, and inter-robot collision avoidance, dynamical feasibility, swarm coordination, and so on, thus realizing an extensible planner. Furthermore, the proposed planner deforms trajectory shapes and adjusts time allocation synchronously based on spatial-temporal joint optimization. A high-quality trajectory thus can be obtained after exhaustively exploiting the solution space within only a few milliseconds, even in the most constrained environment. The planner is finally integrated into the developed palm-sized swarm platform with onboard perception, localization, and control. Benchmark comparisons validate the superior performance of the planner in trajectory quality and computing time. Various real-world field experiments demonstrate the extensibility of our system. Our approach evolves aerial robotics in three aspects: capability of cluttered environment navigation, extensibility to diverse task requirements, and coordination as a swarm without external facilities.

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In this paper, we propose a two-step training procedure for source separation via a deep neural network. In the first step we learn a transform (and it's inverse) to a latent space where masking-based separation performance using oracles is optimal. For the second step, we train a separation module that operates on the previously learned space. In order to do so, we also make use of a scale-invariant signal to distortion ratio (SI-SDR) loss function that works in the latent space, and we prove that it lower-bounds the SI-SDR in the time domain. We run various sound separation experiments that show how this approach can obtain better performance as compared to systems that learn the transform and the separation module jointly. The proposed methodology is general enough to be applicable to a large class of neural network end-to-end separation systems.

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We present an optimization-based framework for multicopter trajectory planning subject to geometrical spatial constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial-temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A wide range of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization, and backward differentiation of flatness map. As a result, the proposed framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning frequency and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications and experiments for different tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating highquality solutions while retaining the computation speed against other specialized methods by orders of magnitudes.

Background and Purpose: The mechanisms of brain damage during ultra-early subarachnoid hemorrhage (SAH) have not been well studied. The current study examined the SAH-induced hyperacute brain damage at 4 hours using magnetic resonance imaging and brain histology in a mouse model. Methods: SAH was induced by endovascular perforation in adult mice. First, adult male wild-type mice underwent magnetic resonance imaging T2 and T2* 4 hours after an endovascular perforation or a sham operation and were euthanized to assess brain histology. Second, male and female adult lipocalin-2 knockout mice had SAH. All animals underwent magnetic resonance imaging at 4 hours, and the brains were harvested for brain histology. Results: T2* hypointensity vessels were observed in the brain 4 hours after SAH in male wild-type mice. The numbers of T2*-positive vessels were significantly higher in SAH brains than in sham-operated mice. Brain histology showed thrombosis and erythrocyte plugs in the T2*-positive cerebral vessels which may be venules. The number of T2*-positive vessels correlated with SAH grade and the presence of T2 lesions. Brain thrombosis was also accompanied by albumin leakage and neuronal injury. LCN2 deficient male mice had lower numbers of T2*-positive vessels after SAH compared with wild-type male mice. Conclusions: SAH causes ultra-early brain vessel thrombosis that can be detected by T2* gradient-echo sequence at 4 hours after SAH. LCN2 deficiency decreased the number of T2*-positive vessels.

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