120 results about "Autonomous Navigation System" patented technology

The invention relates to a man-machine hybrid autonomous navigation system in an unknown dynamic environment, which comprises an environment sensing module for acquiring environment information by using sensors such as a laser radar, a camera and an inertial measurement unit and performing data preprocessing; the local target point selection module selects a local target point set meeting requirements in the environment according to the real-time information provided by the environment sensing module; the decision planning module is used for generating an optimal path and a navigation strategy based on mixed training of a deep reinforcement learning algorithm and artificial experience; and the learning optimization module improves the generalization ability of the algorithm through online learning and transfer learning technologies, and realizes effective introduction and strategy adjustment of human experience in combination with the man-machine interaction module. The man-machine interaction module can work in cooperation with the local target point selection module so as to dynamically adjust target point selection according to external input and optimize path planning. According to the method, high decision stability can be kept in an unseen environment, the training cost is reduced, and the method is more suitable for autonomous navigation application in the real world.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a hierarchical reinforcement learning strategy. The unmanned aerial vehicle autonomous navigation system is suitable for a three-dimensional flight task in an unknown environment. The system comprises a state sensing module, a hierarchical strategy network module, a control execution module, a data classification module and a data playback module. The state sensing module extracts obstacle position information based on a deep neural network, and fuses the target, the obstacle position and the flight state to generate a state vector and a time sequence. The hierarchical strategy network adopts a high-layer DQN to generate a navigation intention, and a low-layer LSTM and PPO are combined to output a continuous control action; the control execution module adjusts the attitude of the unmanned aerial vehicle according to the instruction and performs closed-loop correction. The system introduces a double dynamic memory mechanism (DDM), improves strategy training efficiency and stability through experience classification and proportional sampling, and adopts a multi-target award function guide strategy to optimize convergence among task completion, obstacle avoidance safety and flight rationality. The system has good environmental adaptability and generalization ability, and is suitable for autonomous navigation tasks in complex scenes.

The invention relates to a robot path planning method based on multi-sensor fusion and free space topology composition, and belongs to the technical field of robot autonomous navigation. The method comprises the following steps: firstly, fusing laser radar and millimeter wave radar data, constructing multi-modal point cloud data, extracting a three-dimensional obstacle boundary by combining depth and normal vector mutation features, and generating a three-dimensional obstacle map and a free region tree; according to the constructed space model, factors such as energy consumption, dynamic obstacles, path smoothness and the like are integrated, target selection weights are dynamically regulated and controlled, and self-adaptive screening of intermediate navigation targets is achieved. And based on the intermediate navigation target, generating a safe trajectory satisfying dynamic constraints by adopting geometric-dynamic dual-mode fusion modeling, and enhancing the feasibility and robustness of the trajectory through trajectory envelope optimization and pre-execution fault-tolerant control. The method can be widely applied to autonomous navigation systems such as mobile robots and unmanned vehicles, and has good environment adaptability and path execution stability.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on multi-source vision assistance, and relates to the technical field of unmanned aerial vehicle autonomous navigation, and the system comprises the following modules: a terrain recognition module. A camera and a laser radar are used for synchronously collecting environment data, a visual odometer and a three-dimensional point cloud analysis technology are combined, a dynamically updated grating map is constructed, obstacle and topographic features are recognized through point cloud clustering, an improved Dijkstra algorithm is combined with an information entropy evaluation mechanism to plan a global optimal path, a B spline curve is used for eliminating path mutation, and the optimal path is obtained. The method comprises the following steps that: a dynamic speed decision-making module adjusts a flight parameter in real time on the basis of an obstacle distance-response model, and finally realizes trajectory tracking and dynamic deviation correction through closed-loop PID control and visual pose correction, so that full-link closed-loop control of'perception-planning-execution 'is formed, and the method has high precision, strong robustness and real-time response capability.

The invention relates to the technical field of autonomous navigation, in particular to an AUV (Autonomous Underwater Vehicle) multi-sensor fusion autonomous navigation system based on ant colony-particle swarm collaboration.The AUV multi-sensor fusion autonomous navigation system is characterized in that a sensor weight decision module constructs a pheromone matrix based on an ant colony algorithm, retrieves a mapping relation between historical errors and pheromone concentration in real time and dynamically generates a sensor priority sequence; adaptive weight distribution in a data conflict scene is realized; the dynamic path correction module is combined with a particle swarm algorithm and pheromone concentration gradient constraint to balance global path optimality and real-time obstacle avoidance requirements in optical camera obstacle deviation correction; and the closed-loop coupling module is used for converting sonar and geomagnetic residual errors into pheromone concentration correction values and confidence coefficient regulation factors by fusing residual error compensation and path tracking error feedback, so that an error traceability-weight calibration-path optimization closed loop is formed, sensor drift is inhibited, and the long-endurance navigation precision is improved.

Methods and systems related to computer vision for agricultural applications are disclosed herein. A disclosed method for navigating a robot along a crop row, in which each step is computer-implemented by a navigation system for the robot, includes capturing an image of at least a portion of the crop row, labeling, using a segmentation network, a portion of the image with a label, deriving a navigation path from the portion of the image and the label, generating a control signal for the autonomous navigation system to follow the navigation path, and navigating the robot along the crop row using the control signal.

An introduced autonomous aerial vehicle can include multiple cameras for capturing images of a surrounding physical environment that are utilized for motion planning by an autonomous navigation system. In some embodiments, the cameras can be integrated into one or more rotor assemblies that house powered rotors to free up space within the body of the aerial vehicle. In an example embodiment, an aerial vehicle includes multiple upward-facing cameras and multiple downward-facing cameras with overlapping fields of view to enable stereoscopic computer vision in a plurality of directions around the aerial vehicle. Similar camera arrangements can also be implemented in fixed-wing aerial vehicles.

The invention relates to the technical field of agricultural robot navigation, and discloses an agricultural robot autonomous navigation system based on a three-dimensional point cloud model.The agricultural robot autonomous navigation system comprises a three-dimensional point cloud collection module, a data processing center and a navigation control unit, robot path planning and obstacle avoidance are achieved by collecting environment data in real time and constructing a precise three-dimensional map, and the navigation efficiency is improved. The three-dimensional point cloud acquisition module acquires point cloud data and image information in an environment, and the data processing center adopts a multi-source data fusion algorithm, and performs dynamic environment perception and path optimization, so as to improve the robustness and the real-time performance of the navigation system. The system adopts multi-module cooperative control, realizes dynamic self-adaption of global and local paths, improves the flexibility and reliability of robot operation, combines a PID control algorithm with real-time feedback adjustment, accurately controls the advancing track of the robot, and ensures the operation precision and stability.

The invention discloses an unmanned aerial vehicle flight control method for preventing wireless interference, and belongs to the technical field of multi-unmanned aerial vehicle control. The method comprises the following steps: acquiring and preprocessing an SDR signal; performing interference identification on the preprocessed signal and evaluating an interference identification result; a control strategy optimization framework is constructed based on reinforcement learning, and dynamic updating and optimization of flight control parameters are realized; through a multi-mode communication link and a data temporary storage mechanism, real-time monitoring of a task state in an extreme interference scene is ensured. According to the invention, by constructing a multi-sensor fusion autonomous navigation system, high-precision autonomous positioning and dynamic trajectory correction capability are realized; by introducing an intelligent spectrum decision-making mechanism combining software defined radio SDR and machine learning and utilizing fast Fourier transform FFT and reinforcement learning algorithms, full-band interference identification and adaptive frequency hopping communication capabilities are realized.

The invention discloses an air route optimization method and system based on a genetic algorithm and geographic information, and relates to the technical field of intelligent navigation and path planning, and the method comprises the steps: receiving geographic data related to air route planning, and constructing a multi-level spatial index; a waypoint optimization algorithm is adopted, and under the support of spatial indexes, a genetic algorithm based on population diversity adaptive adjustment is utilized to determine a waypoint sequence of the route; for any two adjacent waypoints in the waypoint sequence, adopting a path optimization algorithm, combining geographic information provided by the spatial index, and utilizing a genetic algorithm to generate a corresponding optimal path segment; and carrying out iterative optimization on the optimal path segment, carrying out iterative optimization on the waypoint sequence and the optimal path segment, and integrating an optimization result to the autonomous navigation system after convergence. The spatial information processing efficiency is remarkably improved, high-risk and limited areas are effectively eliminated, and the optimal layout of a global route structure is realized.

The invention discloses a mechanical dog indoor autonomous navigation method based on a pulse neural network, and provides an indoor autonomous navigation system capable of increasing endurance and reducing calculation complexity for visual navigation. Comprises: preparing a data set; establishing a pulse neural network obstacle avoidance network model, and training the network model; establishing a pulse neural network self-planning path network model, and training the network model; taking the corrected picture as the input of a pulse neural network self-planning path network model, running the network model, processing the output of the pulse neural network self-planning path network model into a control instruction of a mechanical dog, and transmitting the control instruction to the mechanical dog through a port; the obstacle image data acquired by the RGB-D camera is called as the input of the spiking neural network obstacle avoidance network model, the spiking neural network obstacle avoidance network model is operated, the output of the spiking neural network obstacle avoidance network model is processed into an obstacle avoidance instruction of a mechanical dog, and the obstacle avoidance instruction is transmitted to the mechanical dog through a port; and repeatedly executing the steps 4-5, and continuously planning a path to complete a navigation task.

The invention discloses a real-time ocean simulation method and system based on sensing data, and relates to the technical field of ocean information engineering and intelligent sailing. A graph neural network is combined with a multi-head attention mechanism to carry out data fusion, importance of different sensors and data time steps is dynamically weighted, and real-time ocean simulation is realized. According to the method, the quality difference and missing conditions of heterogeneous data are adaptively processed, the deviation and noise influence in the fusion process are reduced, meanwhile, physical consistency constraints such as mass conservation and boundary non-penetration are introduced, the physical rationality and smoothness of an output ocean current field are guaranteed, non-physical artifacts are avoided, and the reliability and practicability of a result are enhanced; a streaming updating mechanism allows a graph structure and model parameters to be rapidly and incrementally updated when new data arrives, data processing and assimilation delay are greatly reduced, and nearly real-time ocean current forecasting is realized, which is crucial to a ship autonomous navigation system, and accurate ocean current information can be timely provided to support dynamic path planning and risk avoiding decision making.

The invention discloses a multi-mode perception unmanned operation platform for river and lake water quality improvement and an intelligent decision-making method, and the platform comprises an intelligent control center, and an autonomous navigation system, a multi-parameter water quality detection system, a dual-mode aeration system and an intelligent medicament decision-making system which are connected with the intelligent control center. Autonomous navigation operation of a complex water area is achieved through the autonomous navigation system, the multi-parameter water quality detection system provides a multi-mode sensing sensor array, comprehensive water quality parameter detection can be conducted on the water areas of rivers and lakes, and the dual-mode aeration system conducts aeration according to dissolved oxygen parameters. A working mode instruction intelligently judged by the intelligent control center is executed, aeration treatment is conducted, the intelligent medicament decision-making system dynamically calculates the medicament adding amount according to the water quality parameters, and accurate adding is achieved. According to the system, efficient integrated collaborative operation of autonomous navigation operation, full-dimensional water quality parameter real-time monitoring, dual-mode precise aeration and an on-demand dosing mechanism is realized, and the effect and efficiency of river and lake water quality improvement can be effectively improved.

The invention relates to the technical field of robot control and autonomous navigation, in particular to a bronchoscope robot autonomous control method based on deep reinforcement learning. Autonomous navigation of the bronchoscope robot is performed through a deep reinforcement learning training agent, pose data and picture data are acquired in real time by using an electromagnetic navigation technology and an endoscope camera, then data feature extraction and multi-modal data fusion are performed, the data are input to a reinforcement learning agent, and motion output is obtained through a deep network. And finally, three-degree-of-freedom control of the robot is realized. The method has a wide application prospect, and is particularly suitable for the fields of medical robot navigation, artificial intelligence operation assistance and the like. Position data are acquired through electromagnetic navigation, image data are acquired through an endoscope camera, and observation input of deep reinforcement learning is constructed by using a processing method of feature extraction and attention mechanism fusion.

The invention discloses a detectability quantitative model and a filtering method for an underwater multi-source autonomous navigation system, and belongs to an underwater robot integrated navigation and integrated positioning technology. The method comprises the following steps: firstly, respectively carrying out error modeling on SINS, DVL and USBL sensors based on an SINS / DVL / USBL integrated navigation model; establishing a multi-dimensional detectability quantitative model from four dimensions of coverage rate, accuracy rate, real-time rate and availability rate; and finally, based on the established detectability quantitative model, the detectability of the sensor is incorporated into an adaptive Kalman filtering algorithm so as to optimize the problem of computing resource allocation during data fusion. Compared with a traditional detectability method, the underwater environment interference is considered to establish a multi-dimensional detectability quantitative model, a corresponding algorithm is proposed to dynamically allocate computing resources, and the precision and robustness of the underwater vehicle multi-source autonomous navigation system are further improved.

The invention provides a robot autonomous navigation system based on an AI large model, and relates to the technical field of artificial intelligence AI large models, and the system comprises a man-machine interaction module, a body perception module, a body thinking module, a body execution module, and an interaction unified power supply system. An environment is interactively sensed through a body sensing module, namely a visual (laser radar) sensor, a touch (pressure) sensor and the like of a robot, and a body sensing model is constructed. The AI large model with the thinking module is responsible for processing perception information and performing reasoning and decision making. And the body execution module (robot execution mechanism) is responsible for executing corresponding actions according to decisions, so that the environment perception and task decision capabilities of the robot are effectively improved, and the autonomous navigation capability of the robot in a complex environment is improved.

The invention discloses an underground pipeline robot autonomous navigation system with an obstacle avoidance function, and relates to the technical field of autonomous navigation.Multi-dimensional information of obstacles encountered by an underground pipeline robot in historical operation is collected and transmitted to a space-time diagram neural network ST-GNN for training, and the obstacle risk level is obtained; the method comprises the following steps: classifying paths with identical starting points and ending points into one class, calculating distances and timestamps among all obstacles on the paths to obtain position classes, fitting the position classes by a rectangular region optimization method based on Bezier curve fitting to obtain different risk regions, and the underground pipeline robot analyzes the alternative paths passing through the different risk areas to select the most suitable navigation path. The problems of low passing efficiency and long time consumption caused by the fact that navigation of the underground pipeline robot is influenced by obstacles are solved.

The invention discloses a traffic environment digital twinning method for autonomous navigation of a ship, and the method comprises the steps: extracting static traffic environment information through an electronic chart, and generating a static digital twinning environment; dynamic traffic environment real-time data are obtained through shipborne equipment, and a dynamic digital twin environment is generated; the dynamic and static environments are fused, and coordinate registration is realized through longitude and latitude-to-Mercator projection; planning a multi-scene static path in combination with a historical AIS track and a maritime collision avoidance rule; an MMG model is adopted to simulate ship control motion, and a virtual-real fusion test scene is constructed; constructing a digital twinning environment for a near-shore water area and setting an avoidance strategy; by constructing a high-fidelity digital twin traffic environment, accurate and comprehensive environment sensing data is provided for a ship autonomous navigation system, so that basic support is provided for subsequent autonomous navigation decisions.

The invention discloses a field crop weeding robot autonomous navigation system which is composed of a core module and a sensing module, and the core module is responsible for information receiving, data processing, algorithm operation, control instruction issuing and motion control functions of the field crop weeding robot autonomous navigation system; the core module comprises an autonomous navigation system master controller, a chassis master controller and a remote control terminal; the sensing module is responsible for sensing environment information of the autonomous navigation system of the field crop weeding robot, so that the robot can construct a map of a working environment and realize positioning and repositioning functions; the invention further discloses a control method of the autonomous navigation system of the field crop weeding robot, full-autonomous navigation can be achieved in the field weeding operation process, good navigation stability is achieved, the labor cost is reduced, and meanwhile the operation efficiency of the field crop weeding robot is effectively improved.

The invention provides a ship-assisted autonomous navigation system and method based on a language model, and the system comprises a language input module which is used for collecting an external analog sound signal and converting the external analog sound signal into a language signal; the language model module is connected with the language input module and is used for analyzing the language signal and generating a control instruction; the language output module is used for converting the language information into analog quantity sound signals and outputting the analog quantity sound signals; the navigation control module is connected with the language model module and used for adjusting the course, speed and rudder angle of the ship according to the control instruction; and the shore-based data center is connected with the language model module through the satellite communication module and provides external data support. According to the technical scheme, the ship autonomous navigation system is established based on the language model, real-time communication of navigation information between the ship and the ship / shore end and control over the ship are achieved, the autonomous navigation function is achieved in an auxiliary mode, and the core problems of cross-language communication, real-time response, rule compliance and the like are solved.

The invention discloses an efficient intelligent laser weeding robot, and relates to the field of laser weeding. The problems that an existing laser weeding robot is large in weight and complex in structure are solved. The device comprises a visual identification system (6), a chassis truck (1), a control system (2), a cooling system (3), an autonomous navigation system (4) and a laser assembly (5), the chassis truck (1) comprises four wheel bodies A and a lifting platform (B), the lifting platform (B) is a motor-driven lifting platform, the four wheel bodies A are installed at the four corners of the lifting platform (B) respectively, the laser assembly (5) is installed on the lifting platform (B) in the length direction, and the control system (2) is connected with the control system (2). The control system (2) is installed on the side face of the lifting platform (B), the cooling system (3) is installed on the rear portion of the lifting platform (B), and the autonomous navigation system (4) is installed on the lifting platform (B). Weeds are efficiently eliminated, no pollution is caused in the process, and efficient and intelligent farmland weeding is achieved.

The invention discloses a composite tracking method for an aviation dual-frequency satellite communication phased-array antenna. According to the method, the dual-frequency satellite communication phased-array antenna can be always aligned with high-frequency and low-frequency satellites in the flight process by utilizing navigation data calculated by an autonomous navigation system. The method does not depend on external data, is an independent system, and is easy to maintain and convenient to use. In the implementation process, the high-frequency antenna master control module and the low-frequency antenna drive and control integrated module share navigation data, the working states of opposite ends can be sensed mutually, pointing adjustment can be made in time, high-frequency links and low-frequency links can be backed up mutually, and the reliability of a communication system is improved.

The invention discloses a multi-source autonomous navigation system and method with embedded carrier dynamic characteristics, and the method comprises the steps: building a carrier dynamic model in a dynamic modeling stage; in a filter prediction stage, a Kalman filter driven by dynamics is established, a carrier execution mechanism control signal is periodically read as filter input, and a state vector and a covariance matrix are predicted in each sampling period; a filter updating stage: calculating information of each sensor and a carrier maneuvering strength index, constructing a dynamics consistency judgment quantity, adaptively adjusting an observation variance matrix, updating a filtering state according to observation information of the sensor, and realizing multi-source information fusion; and a fault detection and isolation stage: constructing a multi-source consistency index, and judging the health state of the navigation system. According to the invention, the problem that the performance of a traditional navigation system is reduced when a carrier platform violently moves or is disturbed by external force of the environment is solved, and robust high-precision navigation under GNSS denial, sensor failure or complex dynamic conditions is realized.

The invention relates to the technical field of robot navigation control, and discloses an agricultural inspection robot autonomous navigation system and a control method, the system comprises a data acquisition module, a positioning module, a prediction module, a correction module and a control module; wherein the data acquisition module is used for acquiring image data in an inspection path; the positioning module is used for acquiring a first positioning coordinate and a second positioning coordinate of the inspection robot; the prediction module is used for predicting the shielding condition in the inspection path; the correction module is used for dynamically optimizing the acquisition of the second positioning coordinates; and the control module controls the movement of the inspection robot according to the first positioning coordinate and the second positioning coordinate, and performs relaxation compensation on the inspection path according to the shielding condition. According to the invention, the autonomous navigation capability of the agricultural inspection robot in a complex environment is improved, the positioning precision and navigation accuracy are ensured, and the method has good environmental adaptability and is suitable for large-scale agricultural inspection scenes.

The invention discloses an unmanned aerial vehicle autonomous navigation system based on a rasterized world model, and relates to the technical field of unmanned aerial vehicle control. According to the system, observation information of an unmanned aerial vehicle is input into a trained rasterized world model through an observation acquisition module; through cooperative work of a sequence model, a multi-modal self-encoder, a hidden space dynamics predictor, a multi-modal information prediction head and a grid predictor in a rasterized world model, a cyclic variable containing historical information, a prediction hidden state vector and a prediction local grid map are provided for an agent model. And action decision making is carried out based on the data through an intelligent agent model so as to accurately output the actions of the unmanned aerial vehicle. According to the system, by introducing the rasterized world model, multi-modal observation information can be effectively fused, and a three-dimensional structure of a local environment is predicted in real time, so that the spatial perception capability of an intelligent agent model to a complex environment is enhanced, and the decision-making performance of the intelligent agent model is improved.

Systems and methods for implementing sensory configurations in accordance with certain embodiments of the invention are illustrated. One embodiment includes an imaging system that includes cameras, wherein each camera includes image sensor(s). The imaging system includes video links, wherein each of the video links is configured to: connect a particular camera to a central processor, and carry power, from the central processor to the particular camera. The imaging system includes a memory that stores instructions for processing image data obtained from the cameras. The central processor is configured to execute the instructions to perform a method. The method aggregates the image data for each camera of the plurality of cameras to produce aggregated image data, using at least one mobile industry processor interface (MIPI) aggregator. The method processes the aggregated image data to produce a set of at least one environmental image.

Systems and methods are provided for autonomous navigation based on user intervention. In one implementation, a navigation system for a vehicle may include least one processor. The at least one processor may be programmed to receive images acquired by a camera from an environment of a vehicle; determine a navigational maneuver for the vehicle based on analysis of one or more of the plurality of images; cause the vehicle to initiate the navigational maneuver; receive a user input causing an override to alter the initiated navigational maneuver; determine navigational situation information relating to the vehicle via analysis of the images; determine, based on the navigational situation information, whether the user input is associated with a transient condition; and when the user input is not associated with a transient condition, store the navigational situation information in association with information relating to the user input.

The invention relates to a real-time non-graphical robot autonomous navigation system, and belongs to the field of robots. The system comprises a main control module, an environment sensing module and a hybrid navigation module, the main control module is responsible for man-machine interaction, task flow control and cooperation of other modules, and storing and managing a visual semantic database; the environment sensing module is responsible for controlling the robot to collect image data and depth data under the condition of no prior map, identifying objects in a scene, extracting semantic information of the objects, and integrating identification results into a visual semantic database; and the hybrid navigation module analyzes the instruction sent by the main control module by using a large language model, matches visual memory, determines a navigation target, and completes autonomous navigation of the robot through a hybrid navigation strategy. According to the method, the problem of low navigation task accuracy caused by the sparsity of global information in map-free navigation is solved.

The invention belongs to the technical field of intelligent navigation of agricultural machinery, and particularly relates to an autonomous navigation system of field unmanned transportation equipment after potatoes (such as potatoes and sweet potatoes) are harvested, in particular to a potato operation line identification method based on binocular camera multi-modal information dynamic weighting. Aiming at a complex field environment (soil is loosened and turned over, scattered potato blocks / weeds are mixed, and an original ridge-shaped structure is locally damaged) after the operation of a harvester, a ridge line track required by the driving of a transport vehicle is difficult to stably identify in a scene of strong light overexposure, weak light noisy points and shadow alternation by a traditional visual method. According to the method, an IntelRealSenseD456 active stereoscopic vision binocular camera is carried, RGB images and depth information are fused in real time, and multi-modal data are dynamically weighted based on illumination intensity, so that the robust perception capability of the unmanned transport vehicle on the geometric boundary of the potato ridge is improved, the vehicle is ensured to accurately run along a harvested field ridge operation line, and the situation that the potato blocks are rolled and scattered or deviated from a path is avoided.

The invention discloses an autonomous navigation system and method for an electric power inspection unmanned aerial vehicle, and belongs to the technical field of artificial intelligence and unmanned aerial vehicle control. The system comprises a multi-mode sensing module, a VLM navigation module and an electric power scene adaptation and flight control instruction conversion module, an instruction semantic adaptation unit disassembles a natural language inspection instruction into triple features, and an electric power equipment knowledge base is called to map an equipment type into target equipment features which can be identified by an SPF; the multi-modal feature fusion unit carries out feature fusion to obtain fusion features; the decision output unit combines the fusion features and the triple features and then inputs the combined features and the triple features into a VLM navigation decision model based on SPF frame 2D space grounding logic, and an initial 2D waypoint is output; and the flight control adaptation unit performs flight control instruction conversion based on the 3D displacement vector corresponding to the 2D waypoint. The natural language inspection instruction is mapped into target equipment characteristics which can be identified by the SPF, so that accurate conversion from the natural language inspection instruction to the flight control instruction can be realized.