We presentNeural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods neglect capturing these interaction forces, resulting in sparse swarm configuration during flight. Our approach combines a physics-based nominal dynamics model with learned deep neural networks with strong Lipschitz properties. We make use of two techniques to accurately predict the aerodynamic interactions between heterogeneous multirotors: 1) Spectral normalization for stability and generalization guarantees of unseen data and 2) heterogeneous deep sets for supporting any number of heterogeneous neighbors in a permutation-invariant manner without reducing expressiveness. The learned residual dynamics benefit both the proposed interaction-aware multirobot motion planning and the nonlinear tracking control design because the learned interaction forces reduce the modelling errors. Experimental results demonstrate thatNeural-Swarm2is able to generalize to larger swarms beyond training cases and significantly outperforms a baseline nonlinear tracking controller with up to three times reduction in worst-case tracking errors.

Fixed-wing vertical take-off and landing (VTOL) aircraft pose a unique control challenge that stems from complex aerodynamic interactions between wings and rotors. Thus, accurate estimation of external forces is indispensable for achieving high performance flight. In this paper, we present a composite adaptive nonlinear tracking controller for a fixed- wing VTOL. The method employs online adaptation of linear force models, and generates accurate estimation for wing and rotor forces in real-time based on information from a three-dimensional airflow sensor. The controller is implemented on a custom-built fixed-wing VTOL, which shows improved velocity tracking and force prediction during the transition stage from hover to forward flight, compared to baseline flight controllers.

Precise near-ground trajectory control is difficult for multi-rotor drones, due to the complex aerodynamic effects caused by interactions between multi-rotor airflow and the environment. Conventional control methods often fail to properly account for these complex effects and fall short in accomplishing smooth landing. In this paper, we present a novel deep-learning-based robust nonlinear controller (Neural-Lander) that improves control performance of a quadrotor during landing. Our approach combines a nominal dynamics model with a Deep Neural Network (DNN) that learns high-order interactions. We apply spectral normalization (SN) to constrain the Lipschitz constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear feedback linearization controller using the learned model and prove system stability with disturbance rejection. To the best of our knowledge, this is the first DNN-based nonlinear feedback controller with stability guarantees that can utilize arbitrarily large neural nets. Experimental results demonstrate that the proposed controller significantly outperforms a Baseline Nonlinear Tracking Controller in both landing and cross-table trajectory tracking cases. We also empirically show that the DNN generalizes well to unseen data outside the training domain.

It is challenging to analyze the aerial locomotion of bats because of the complicated and intricate relationship between their morphology and flight capabilities. Developing a biologically inspired bat robot would yield insight into how bats control their body attitude and position through the complex interaction of nonlinear forces (e.g., aerodynamic) and their intricate musculoskeletal mechanism. The current work introduces a biologically inspired soft robot called Bat Bot (B2). The overall system is a flapping machine with 5 Degrees of Actuation (DoA). This work reports on some of the preliminary untethered flights of B2. B2 has a nontrivial morphology and it has been designed after examining several biological bats. Key DoAs, which contribute significantly to bat flight, are picked and incorporated in B2's flight mechanism design. These DoAs are: 1) forelimb flapping motion, 2) forelimb mediolateral motion (folding and unfolding) and 3) hindlimb dorsoventral motion (upward and downward movement).

This paper presents a systematic flight controller design based on the mathematics of parametrized manifolds and calculus of variations for the Bat Bot (B2), which possesses many articulated wings. Wing kinematics and morphological properties are crucial in the powered flight of flying vertebrates. The articulated skeleton of these mammals, which contains many degrees of actuation and underactuation, has made it difficult to understand the connection between the bat's flight dynamics and its intricate array of physiological and morphological specializations. B2 is a biomimetic micro aerial vehicle (MAV) that possesses similar morphological properties to a bat in order to duplicate bats powered ballistic motion. In an effort to design the advanced flight control algorithm for B2, this paper reports two major contributions. First, a systematic mathematical framework is introduced that evaluates the holonomically-constrained Lagrangian model of a flapping robot with specified active and passive degrees of freedom (DoF) in order to locate physically feasible and biologically meaningful periodic solutions using optimization. These are parametrized constraint manifolds; the flapping wing dynamics are governed by these manifolds. Second, calculus of variations and the well-recognized method of inverse dynamics are applied in order to synthesize the flight control algorithm for the flapping wings.

This paper addresses the problem of motion planning for fast, agile flight through a dense obstacle field. A key contribution is the design of two families of motion primitives for aerial robots flying in dense obstacle fields, along with rules to stitch them together. The primitives are obtained by solving for the flight dynamics of the aerial robot, and explicitly account for limited agility using time delays. The first family of primitives consists of turning maneuvers to link any two points in space. The locations of the terminal points are used to obtain closed-form expressions for the control inputs required to fly between them, while accounting for the finite time required to switch between consecutive sets of control inputs. The second family consists of aggressive turn-around maneuvers wherein the time delay between the angle of attack and roll angle commands is used to optimize the maneuver for the spatial constraints. A 3-D motion planning algorithm based on these primitives is presented for aircraft flying through a dense forest.