372 results about "Flight controller" patented technology

Systems and apparatus are provided for a high-power microwave sensor using an unmanned aerial vehicle. The unmanned aerial vehicle may include a central body, at least one electric motor, and a barometric pressure feedthrough. The central body may include a first enclosure housing a plurality of low voltage components, a voltage feedthrough connector, and a second enclosure housing a plurality of high voltage components. The low voltage components may include a first signal processing system, a first radio transceiver, a flight controller, a second signal processing system, a second radio transceiver, and a navigation system. The high voltage components may include a plurality of electronic speed controllers, a power distribution module, an input power interface, and a plurality of high power filters. The components of each enclosure may be segregated such that the high voltage components do not create electromagnetic interference with the low voltage components.

Systems and apparatus are provided for a high-power microwave sensor using an unmanned aerial vehicle. The unmanned aerial vehicle may include a central body, at least one electric motor, and a barometric pressure feedthrough. The central body may include a first enclosure housing a plurality of low voltage components, a voltage feedthrough connector, and a second enclosure housing a plurality of high voltage components. The low voltage components may include a first signal processing system, a first radio transceiver, a flight controller, a second signal processing system, a second radio transceiver, and a navigation system. The high voltage components may include a plurality of electronic speed controllers, a power distribution module, an input power interface, and a plurality of high power filters. The components of each enclosure may be segregated such that the high voltage components do not create electromagnetic interference with the low voltage components.

The invention discloses an unmanned aerial vehicle dynamic path control method based on multi-source sensing fusion, and relates to the technical field of unmanned aerial vehicle path control, and the method comprises the following steps: collecting original sensing data; performing interpolation resampling on the data acquired by the visual perception unit to realize time alignment; inputting the multi-modal sensing data after time alignment into a time interpolation sensing fusion network model; estimating system delay from sensing data acquisition to control instruction output, constructing a feedforward prediction model based on a current state, predicting a future state, and further generating a delay compensation control signal; the delay compensation control signal is input into a flight controller, attitude, speed and acceleration instructions are calculated and output, the unmanned aerial vehicle is driven to execute a path control task, and parameter adaptive adjustment is performed based on flight control feedback information; according to the method, the space-time accuracy of path estimation under the high-dynamic maneuvering condition is enhanced.

The invention relates to the technical field of automatic obstacle avoidance, and provides an automatic obstacle avoidance method and system for a low-altitude aircraft. The method comprises the following steps: acquiring flight state information in real time; acquiring obstacle information through an environment sensing module; based on the flight state information and the obstacle information, calculating a relative position and a relative speed with the obstacle, and determining a potential collision risk area; if the potential collision risk exists, based on a preset obstacle avoidance strategy library, an obstacle avoidance strategy is retrieved in combination with a risk area, and an obstacle avoidance control instruction is generated; and transmitting the control instruction to a flight controller, and controlling the aircraft to execute automatic obstacle avoidance. The technical problems that an existing low-altitude aircraft is lagged in obstacle avoidance response in a complex environment and difficult to accurately avoid obstacles in real time are solved, and the technical effects that collision risk dynamic pre-judgment is achieved through multi-source information fusion and intelligent strategy matching, and the autonomous obstacle avoidance success rate and flight safety of the low-altitude aircraft are improved are achieved.

The invention discloses an unmanned helicopter control method based on a fuzzy neural network extended state observer, and belongs to the technical field of transverse unmanned helicopter flight control, and the structure of a transverse unmanned helicopter flight controller comprises an FNNESO-RBSC controller, a control strategy and a transverse unmanned helicopter flight dynamics model; the FNNESO-RBSC controller calculates transverse, longitudinal, course and vertical channel control quantities according to system input and output information, the control quantities are distributed to all control surfaces of the transverse unmanned helicopter through a control strategy and act on a controlled object of the transverse unmanned helicopter, object state response of the transverse unmanned helicopter is changed under the action of aerodynamic force, and the control surfaces of the transverse unmanned helicopter are controlled. And the target flight state is controlled. According to the method, the fuzzy neural network is adopted to transform the extended state observer, and the adaptability of the extended state observer is improved, so that the unknown total disturbance needing to be compensated in the backstepping control law is more effectively obtained.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention relates to the field of artificial intelligence and automatic control, and discloses an AI dynamic planning method for a fan adaptive inspection path. The method comprises the following steps: establishing a fan digital twin model containing key points and training an AI model; the unmanned aerial vehicle calls an AI model to calculate the accurate pose of the blade in real time; constructing a multi-target dynamic planning model fusing flight safety, inspection quality and efficiency according to the pose; carrying out online solving by an airborne unit, and dynamically generating an optimal path for the current attitude of the blade; and the path is instantly issued to a flight controller to drive the unmanned aerial vehicle to autonomously fly to collect data so as to complete an intelligent closed-loop inspection task. By fusing AI visual perception, dynamic planning and closed-loop control technologies, full-automatic inspection of the fan blade in any shutdown state is realized, the problems that a preset route is not applicable and the quality of manual inspection is not uniform are solved, and the adaptability, reliability and safety of inspection are remarkably improved.

The invention relates to an unmanned aerial vehicle power management and information monitoring system, and the system comprises a power management and information monitoring device which is connected among an unmanned aerial vehicle power supply, an unmanned aerial vehicle flight controller, and an unmanned aerial vehicle load set. Wherein the power management and information monitoring equipment is internally integrated with a unified power management module used for receiving electric energy of an unmanned aerial vehicle power supply and providing one or more transformed stable power supplies for an unmanned aerial vehicle load set; and the information and control concentrator module is used as an information and control relay among the unmanned aerial vehicle flight control, the unmanned aerial vehicle load set and the sensor. The invention aims to help to solve the problems of complex flight control interface and overweight operation burden caused by direct data and control interaction between numerous airborne equipment and a flight controller, and low system integration degree and insufficient reliability caused by decentralized management of a power supply in an existing unmanned aerial vehicle system.

A system, computer readable storage medium and method for controlling a trajectory of coordinated time-varying maneuvers of a geometric formation of unmanned vehicles is disclosed. The system includes unmanned vehicles, each configured with communication circuitry to communicate between the vehicles. A subset of the unmanned vehicles function as leader vehicles, with the remaining vehicles functioning as follower vehicles for leader-follower maneuvering. The system further includes an actuator suite configured to adjust the direction and orientation of each vehicle, a sensor suite for stabilization and navigation, and a flight controller for maintaining stable maneuvering, even in the presence of actuator faults and sensor deception attacks. Processing circuitry is configured with a reinforcement learning neural network that includes identifier, actor, and critic radial basis function neural networks to estimate movement, adjust control actions, and assess vehicle performance based on feedback signals, including corrupted signals from the sensor suite due to deception attacks.

The invention relates to the technical field of unmanned aerial vehicle control, in particular to an unmanned aerial vehicle accurate attitude control method fusing multiple sensors and a flight controller thereof. The method comprises the following steps: acquiring the position and attribute characteristic data of an unmanned aerial vehicle through multiple sensors; determining the offset degree of the statistical period and the offset direction of the unmanned aerial vehicle according to the position difference between the unmanned aerial vehicle position and the planned route in the previous statistical period, the unmanned aerial vehicle direction and the planned route angle; attitude angles are obtained through attitude calculation, the attitude angles form a fluctuation curve, the fluctuation degree is analyzed, and the attitude stability degree of the unmanned aerial vehicle is obtained; the deviation of the unmanned aerial vehicle relative to the initial position is obtained according to the fluctuation curve slope of the yaw angle; comparing the direction with the offset direction to determine a correction angle; and calculating a three-axis error signal through the correction angle, and inputting the three-axis error signal into a PID (Proportion Integration Differentiation) controller for control. A more accurate attitude angle is obtained, so that the control precision is improved.

The invention relates to an unmanned aerial vehicle autonomous exploration method for indoor / underground unknown space. The method comprises the following steps: S1, acquiring a pose of an unmanned aerial vehicle and a depth image provided by a depth camera, and constructing an ESDF environment map; s2, performing boundary search in the constructed environment map, and generating high-quality candidate viewpoints by adopting a mixed viewpoint method based on an existing boundary; s3, formulating a more reasonable boundary exploration sequence by adopting a novel boundary priority strategy and an isolated small region detection method; s4, recording a global path of each time by adopting a historical perception strategy, and calculating the motion smoothing cost to obtain an expected global exploration path; and S5, according to path planning, an LKH algorithm is adopted to solve the traversal problem of a global boundary viewpoint, an optimal traversal sequence is obtained, and an A * algorithm is utilized to search a feasible local path from the current position of the unmanned aerial vehicle to a first candidate viewpoint in a map. And S6, adopting a third-order uniform B spline curve, and introducing a penalty function to optimize the trajectory to obtain an expected trajectory. And S7, the expected flight path is sent to a flight controller of the unmanned aerial vehicle, and the unmanned aerial vehicle flies according to the flight path by applying a PD controller.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a multi-rotor unmanned aerial vehicle interception control system based on reinforcement learning. The system comprises an image acquisition module used for acquiring a target image of a target unmanned aerial vehicle; the IBVS controller is used for extracting a target position in the target image, calculating an image error, constructing and training a reinforcement learning model, constructing a current state as input of the reinforcement learning model based on the image error and the state information of the multi-rotor unmanned aerial vehicle, obtaining an expected attitude output by the reinforcement learning model, and outputting the expected attitude to the multi-rotor unmanned aerial vehicle. Calculating an error between the current attitude and the expected attitude to generate a control instruction for intercepting the trajectory of the target unmanned aerial vehicle; and the flight controller is used for receiving the control instruction, converting the control instruction into a pulse width modulation signal and outputting the pulse width modulation signal to motors to adjust the rotating speed of each motor and execute an interception task. The method can dynamically adapt to target movement in a complex environment, and realizes real-time and efficient interception of the target unmanned aerial vehicle.

A system and a method for controlling a propulsor assembly of an electric aircraft. The system may include an electric motor, wherein the electric motor may include a rotor and a stator, a propulsor driven by the electric motor configured to propel an electric aircraft. The propulsor may include a propeller. The propeller may include a blade. The system may include a cyclic control assembly configured to deflect the blade. The cyclic control assembly may include an actuator and a push rod mechanically connected to the actuator and the propulsor. The system may include a flight controller. The flight controller may be configured to receive sensor datum from at least a sensor communicatively connected to the electric aircraft, generate a deflection command as a function of the sensor datum and actuate the cyclic control assembly as a function of the deflection command.

A modular unmanned aerial vehicle (UAV) system comprises a body module, a rotor module, and a wing module. The body module includes a flight controller and a power distribution device. The body module is releasably attachable to the rotor module or the wing module, and the body module is releasably attachable to the rotor module. The rotor module includes one or more motors and electronic speed controllers (ESCs), while the wing module includes a wing having a flap, elevator, aileron, or rudder. Various UAV configurations can be formed from the body module, the rotor module, and the wing module. Each configuration includes different advantages for flight time, distance, battery life, and payload capacity. A UAV can be configured to a particular configuration to optimize parcel delivery.

The invention discloses a flight controller and method fusing PID control and a neural network, and a storage medium, and the method comprises the steps: obtaining the real-time online flight state data of an aircraft, inputting the real-time online flight state data into a basis function, and obtaining the basis function data irrelevant to a wind field, the basis function being a pre-trained aerodynamic neural network irrelevant to the wind field; obtaining a linear coefficient related to the wind field through real-time online self-adaptive adjustment; multiplying the basis function data irrelevant to the wind field obtained in real time and on line by the linear coefficient relevant to the wind field to obtain residual force; converting the residual force into the power of the aircraft and feeding back the power to a PID controller to realize the control of the aircraft; according to the method, various interference factors are decoupled into non-real-time and real-time factors, control over various uncertain nonlinear complex processes can be effectively achieved, the robustness and fault tolerance of a flight control system are improved, and better control performance is achieved.

Systems and apparatus are provided for a high-power microwave sensor using an unmanned aerial vehicle. The unmanned aerial vehicle may include a central body, at least one electric motor, and a barometric pressure feedthrough. The central body may include a first enclosure housing a plurality of low voltage components, a voltage feedthrough connector, and a second enclosure housing a plurality of high voltage components. The low voltage components may include a first signal processing system, a first radio transceiver, a flight controller, a second signal processing system, a second radio transceiver, and a navigation system. The high voltage components may include a plurality of electronic speed controllers, a power distribution module, an input power interface, and a plurality of high power filters. The components of each enclosure may be segregated such that the high voltage components do not create electromagnetic interference with the low voltage components.

The invention relates to the technical field of unmanned aerial vehicle control and management, in particular to an unmanned aerial vehicle parameter consistency configuration method based on template management, which adopts a structured parameter configuration template in a JSON format, comprises four blocks of metadata, parameter definition, constraint rules and digital signatures, establishes an MAVLink communication link with an unmanned aerial vehicle flight controller through a template management terminal, and provides an unmanned aerial vehicle parameter consistency configuration method. And executing an automatic configuration process based on a finite-state machine, and ensuring parameter logic consistency by using a difference comparison and constraint verification mechanism. According to the method, temporary storage, batch writing and atomic submission of parameter changes are achieved through the atomization transaction submission protocol, the integrity of the configuration process is guaranteed, the digital signature auditing log containing the timestamp, the device identifier, the template hash and the change details is generated after configuration is completed, whole-process tracing is supported, and the method is high in practicability and easy to popularize. Headless mode operation, parameter white list control and hardware abstraction layer adaptation are supported, and high automation, standardization and safety of unmanned aerial vehicle parameter configuration can be achieved.

The invention provides an unmanned aerial vehicle photovoltaic real-time inspection system and method based on an end-edge-cloud architecture, and belongs to the field of photovoltaic power station operation and maintenance. Comprising an end device which directly interacts with an environment or a user and collects data and comprises an unmanned aerial vehicle end, a flight controller, a pan-tilt camera end and a mobile phone end; the edge computing platform is realized by adopting an embedded platform carried above the unmanned aerial vehicle end, and is used for reading end equipment data, executing fuzzy judgment based on a visible light image and two-stage defect detection based on an infrared image, and uploading an image detection result to the cloud server; and the cloud server is responsible for data storage and message pushing, receiving the image result uploaded by the embedded platform and pushing the image result to the mobile phone terminal. According to the invention, an end-edge-cloud collaborative architecture facing a power station is provided, the diagnosis and decision delay is greatly shortened, the rapid identification and positioning of the defects of the photovoltaic equipment are realized, and the high-precision identification of the defects is realized by adopting a two-stage detection algorithm based on target identification and defect classification.

There is disclosed a multirotor UAV in which electric motors are arranged to drive propellors under the control of an on-board flight controller. The UAV has a plurality of sensors and a first processor for providing sensor signals to the flight controller in normal mode of in-flight operation. The UAV has a three-dimensional depth scanner and a further data processor for processing the three-dimensional depth scanner, wherein the further data processor is selectively activated to process the three-dimensional depth scanner data in a second mode of in-flight operation when fling in close proximity to an external structure. The further data processor creates a three-dimensional map of the external structure for control of the flight of the UAV proximate thereto. The UAV can operate in a GPS-denied and / or compass-denied mode. One or more sensor can detect a profile of an external structure within a predetermined vicinity of the UAV. A depth sensor can detect a visible feature in the environment. The control system is operable to log the visible feature as reference point and to determine yaw readings for use by the flight controller relative to said reference point. The control system comprising can receive the outputs of the distance scanning sensor and the location determining system and can define a mapping boundary around a current position of the UAV. The mapping boundary is of a defined size that is smaller than the scanning range of the position scanning sensor. The data processor generates a map of any surfaces within the mapping boundary and the flight controller is arranged to output control instructions for control of the propulsive rotors based upon the map.

The invention relates to the technical field of braking systems, in particular to an electro-hydrostatic braking system of an unmanned aerial vehicle. The electro-hydrostatic brake system of the unmanned aerial vehicle is only provided with two sets of hydraulic pipelines; wherein one set comprises a first hydraulic power unit, a first pipeline, a first normally open solenoid valve, a second pipeline and a first hydraulic braking module; the other set comprises a second hydraulic power unit, a third pipeline, a second normally open solenoid valve, a fourth pipeline and a second hydraulic brake module; when the first hydraulic power unit breaks down, the flight controller controls the first normally-open electromagnetic valve to be closed and controls the normally-closed electromagnetic valve to be opened, so that oil is supplied to the first hydraulic braking module through the second hydraulic power unit. When the second hydraulic power unit breaks down, the flight controller controls the second normally-open electromagnetic valve to be closed and controls the normally-closed electromagnetic valve to be opened, so that oil is supplied to the second hydraulic braking module through the first hydraulic power unit. The air load weight of the unmanned aerial vehicle is reduced, so that the effective load of the unmanned aerial vehicle is improved.

The invention belongs to the technical field of digital twinning and semi-physical simulation, and discloses an unmanned aerial vehicle semi-physical simulation test platform and method based on digital twinning, and the simulation test platform comprises a physical unmanned aerial vehicle which is a real unmanned aerial vehicle and flies in a real target environment; wherein a first flight controller, a camera, a laser radar, an airspeed meter, a GPS positioning module, an IMU module and an airborne computer are carried on the physical unmanned aerial vehicle; the GPS positioning module and the IMU module are respectively connected with the first flight controller, and the first flight controller, the camera and the laser radar are respectively connected with the airborne computer; the physical host is deployed with a twin domain and comprises a twin environment and a twin unmanned aerial vehicle; the twin environment is a simulation environment the same as a real target environment, and the twin unmanned aerial vehicle is a digital simulation model of the physical unmanned aerial vehicle; the second flight controller is connected to the physical host and used for controlling the twin unmanned aerial vehicle; and the airborne computer and the physical host carry out information interaction through a wireless network.

The invention discloses an arm two-degree-of-freedom transformation quadrotor flight control method and device. The method comprises the following steps: S1, carrying out structural design on an arm two-degree-of-freedom transformation quadrotor; s2, deducing a kinetic model and a kinematic model of the two-degree-of-freedom transformation quadrotor of the arm according to the two-degree-of-freedom transformation quadrotor structure of the arm; s3, deducing a control distribution model of the two-degree-of-freedom deformation quadrotor of the arm according to the steps S1 and S2; s4, analyzing the singularity problem of a control distribution model according to the control distribution model of the two-degree-of-freedom deformation quadrotor of the arm derived in the step S3, and designing a special control distribution model for the singularity; and S5, designing a pose quaternion nonsingular terminal sliding mode flight controller according to the dynamical model, the kinematic model, the control distribution model and the special control distribution model of the arm two-degree-of-freedom deformation quadrotor. The method has good control and stabilization effects on the two-degree-of-freedom deformation four rotors of the arms.

A system for cross-channel communication for effectors in an electric aircraft is presented. The system may include a flight component of an electric aircraft. The system may include a plurality of effectors, wherein the plurality of effectors may be configured to control the flight component. The system may include a plurality of flight controllers communicatively connected to the plurality of effectors, wherein the plurality of flight controllers may be configured to receive an input, generate a command as a function of the input and transmit the command to the plurality of effectors. The system may include a plurality of networks communicatively connected to the plurality of effectors and the plurality of flight controllers, wherein the plurality of networks may be configured to receive the command from the plurality of flight controllers and transmit the command to the plurality of effectors.

The invention discloses an unmanned aerial vehicle anti-wind disturbance controller and a control method, and belongs to the technical field of unmanned aerial vehicle flight control. The method runs on an independent processing module which is connected in series between a main flight controller and an electronic speed regulator of the unmanned aerial vehicle, a dynamic reference is constructed by collecting motor current in real time, deviation is calculated, and wind disturbance equivalent disturbance torque is estimated in a prospective mode through a third-order linear extended state observer (LESO) with bandwidth parameterization design; in combination with a'user operation state + flight mode 'two-dimensional intelligent decision compensation strategy, a'look-ahead wind resistance + attitude backup' dual-redundancy channel is matched, so that the wind disturbance is compensated in advance, and the flight stability in a complex scene is ensured. A corresponding controller adopts a standardized independent hardware design, the adaptability is high, the integration cost is low, and the method can be widely applied to high-precision operation scenes of various multi-rotor unmanned aerial vehicles.

The invention discloses an unmanned aerial vehicle adaptive energy efficiency prediction and optimization management method and system and a medium, and the method comprises the steps: constructing a physical-data dual-drive AI prediction model according to a fusion electrochemical model or an empirical formula as a physical constraint before a task starts; bMS real-time data, historical flight logs and charging and discharging records serve as input of an AI prediction model, the current battery state is obtained according to the input, and an initial flight path with the optimal global energy consumption is initially predicted based on the current battery state and the environment situation; in the flight process, the current battery state and environment situation are continuously updated, the intelligent task and flight control module continuously monitors multiple trigger states, and when one trigger state meets a preset dynamic adjustment trigger condition, a dynamic adjustment decision is executed, and an optimization instruction is generated and issued to a flight controller for execution; according to the management method, through global and closed-loop energy efficiency optimization, unnecessary energy consumption is effectively reduced, and the cruising ability is remarkably improved.

The invention provides a fully-distributed unmanned aerial vehicle robust formation control method based on minimum information amount, and the method comprises the following steps: employing a second-order system as a kinematic model of an unmanned aerial vehicle, and building a state space model of an unmanned aerial vehicle system; establishing a state space model of the virtual leader system to describe a global flight path vector of the unmanned aerial vehicle; describing a communication network of the unmanned aerial vehicle group by using a directed graph; designing an output-based adaptive distributed observer to estimate virtual leader system information required by each unmanned aerial vehicle; based on the output information of the observer, constructing an asymptotic internal model to process uncertain parameters in the virtual leader system; an asymptotic internal model is combined with a dynamic state feedback controller, an unmanned aerial vehicle robust formation flight controller is constructed, and the unmanned aerial vehicle robust formation flight controller and an output-based adaptive distributed observer jointly control robust formation flight of a full-distributed unmanned aerial vehicle group. And the robustness and the distributed performance of the formation flight of the unmanned aerial vehicles are improved.

The invention discloses a protocol conversion device and method for a manned aircraft control rudder. Aiming at the problem that the existing general control rudder is difficult to be compatible with the mainstream flight control protocol of the manned aircraft, the technical scheme of the invention can convert the control rudder protocol into the SBUS / PPM protocol. Firstly, the control rudder serves as the control rudder of the aircraft and inputs a control signal into the protocol conversion unit through the USB line, then the protocol conversion unit reads in the control signal through the USB Host interface, and the microprocessor analyzes the signal, extracts the numerical value of each channel and carries out linear mapping. And the microprocessor simultaneously performs SBUS / PPM coding according to the mapped numerical values and outputs a signal, wherein each byte of an SBUS / PPM coding frame is realized through a timer. And finally, the flight controller automatically selects and analyzes the SBUS / PPM signals according to requirements.

An unmanned aerial device and an operation method thereof are provided. The unmanned aerial device includes a plurality of optical radars, a visual sensing module, an edge operator, and a flight controller. The plurality of optical radars generate a plurality of ranging data. The visual sensing module executes a simultaneous localization and mapping to generate obstacle distance data, six-axis acceleration data, and spatial coordinate data. The edge operator performs spatial coordinate conversion on the obstacle distance data, the six-axis acceleration data, and the spatial coordinate data. The flight controller controls the unmanned aerial vehicle to perform obstacle avoidance operation according to the plurality of ranging data, the obstacle distance data, the six-axis acceleration data, and the spatial coordinate data.

A method may include receiving, from a ground station and at an aircraft computing system onboard the aircraft, a definition of a primary route, the primary route representing a trajectory, for the aircraft, from an origin location to a destination location, causing the aircraft to autonomously traverse the primary route using a flight controller, the flight controller configured to automatically control a set of flight control surfaces of the aircraft, while autonomously traversing the primary route, receiving an input from an inceptor, automatically mapping a direction and magnitude of the input to a predetermined flight maneuver, computing a definition of a modified route, the modified route initiating the predetermined flight maneuver, validating the modified route, including determining whether the modified route intersects known obstacles, and in response to the validation succeeding, causing the aircraft to autonomously traverse the modified route using the flight controller.

In some embodiments, an electric aircraft may include a plurality of flight components including a plurality of control surfaces; a plurality of lift propulsors; at least a thrust propulsor; and a plurality of electric motors configured to power the plurality of propulsors; a flight controller, communicatively connected to a pilot input and flight components. In some embodiments, a flight controller may be configured to receive control datum from a pilot input; and generate an output datum as a function of the control datum.