524 results about "Robot path" patented technology

The invention provides an intelligent inspection robot path optimization method and system based on an edge inference model, and the method comprises the steps: obtaining an environment feature topological graph of a target inspection region, and determining an initial edge inference model; incremental training is carried out on the initial edge reasoning model through the real-time environment perception data flow, and a dynamic reasoning model adaptive to the current environment characteristics is generated; performing priority scoring on each path node in the environment characteristic topological graph based on a dynamic reasoning model, and generating an initial optimization path sequence; triggering a feedback calibration mechanism of the dynamic reasoning model according to the environment perception data flow updated in real time, performing dynamic path node replacement on the initial optimization path sequence, and generating a final inspection path; and controlling the intelligent inspection robot to execute an inspection task according to the final inspection path, and continuously collecting new environment sensing data streams in the inspection task process to update a parameter set of the dynamic reasoning model. According to the invention, the adaptability and reliability of path planning to a complex dynamic environment can be improved.

The invention provides a robot path planning method and system based on fusion of an improved ant colony algorithm and DWA, and the method comprises the steps: building a grid map of the movement of a robot, and generating a global path of the robot in the grid map through employing the ant colony algorithm; transmitting the global path to a DWA dynamic window model to generate a dynamic window; adding unknown static and dynamic obstacles into the grid map, and setting a local target point by using a local target point setting method based on the global path; based on the local target point and the robot movement information, robot speed sampling is carried out in the dynamic window, and a simulation track of robot movement is obtained; evaluating the simulation track by using an obstacle avoidance evaluation function, and if the robot reaches a target point, obtaining an optimal path; and if the robot does not arrive at the target point, regenerating a simulation track for iterative calculation until the robot arrives at the target point. The improved algorithm can effectively improve the performance and adaptability of robot path planning in different environments.

A method for robot path planning using skills extracted from human-taught motion programs applied to a new obstacle environment. A three-dimensional convolutional neural network is used to extract features characterizing an obstacle environment, where the feature vector representation of the obstacles overcomes problems encountered when using point cloud obstacle data. The obstacle feature data and robot path start and goal points are provided to an encoder / decoder neural network system which is trained to extract skills from a database of human-generated motion programs. The encoder / decoder neural network system produces a distribution of waypoints for the current obstacle environment and start / goal points. The distribution of waypoints is used to perform a final collision-free path generation using either a rapidly-exploring random tree (RRT) technique or an optimization-based technique.

A method for operating a robot, including: capturing images of a workspace; capturing data indicative of movement of the robot; capturing LIDAR data as the robot moves within the workspace; generating a map of the workspace based on the LIDAR data; actuating the robot to drive; discriminating between an object on a floor surface along a path of the robot and the floor surface based on the captured images; actuating the robot to drive until determining all areas of the workspace are discovered and included in the map; and executing a cleaning function.

The invention discloses a multi-robot layered space-time optimization path planning method based on conflict resolution, and relates to the technical field of multi-robot path planning, and the method comprises the steps: constructing a semantic topology and a grid hierarchical graph of a multi-robot operation environment, and obtaining a robot preset path; robot path conflicts are detected and classified; calculating corresponding dynamic priorities of the robots with the path conflicts; evaluating the total cost of each conflict resolution strategy of the robot path conflict; obtaining an optimal conflict elimination strategy of each robot according to a principle of minimum total cost; according to the corresponding dynamic priorities of the robots, to-be-processed robots are screened out; and performing path re-planning on the to-be-processed robot based on the optimal conflict elimination strategy of the to-be-processed robot and a preset path. According to the method, the robots with low priorities can be re-planned with the lowest cost, and finally robot conflicts are eliminated.

The invention discloses an inspection robot collaborative planning method and system based on digital twinning, and the method comprises the steps: constructing a virtual simulation environment of a large-scale component through a point cloud modeling technology, and integrating a motion capture system to achieve the real-time mapping of a physical environment and a virtual model; defining a cooperative mechanism of the scanning robot and the positioning robot, providing global coordinates and path guidance by the positioning robot, and correcting position errors in real time by the motion capture system; simulating a dual-machine motion track in the digital twin model, and identifying a potential collision risk through a conflict prediction model; designing a collaborative planning algorithm under coupling constraint, and performing optimization by taking path optimization and scanning visual angle coverage as a joint target; based on a twin intelligent framework with a body, the path and the visual angle of the robot are dynamically adjusted by combining reinforcement learning with real-time feedback, and self-adaptive collaborative planning is achieved. Through double-machine division cooperation and real-time correction, the scanning efficiency and precision of the large-size component can be remarkably improved, and the collision risk is reduced.

The invention provides an intelligent inventory robot path optimization method based on environment modeling and collaborative optimization, and relates to the technical field of intelligent warehouse management, and the method comprises the steps: scanning a warehouse region through an unmanned plane platform, and generating three-dimensional warehouse point cloud data; after point cloud denoising, plane fitting, shelf boundary extraction and channel detection processing, a two-dimensional storage plane graph is generated and transmitted to a robot data base; generating an initial inventory planning path by adopting an A * algorithm, optimizing the initial path through a simulated annealing algorithm, and generating a target inventory planning path; based on the dynamic obstacle influence factors, the RFID signal intensity and the robot residual electric quantity are combined, and a target path is corrected through a mixed algorithm of multi-target optimization and PSO dynamic weight; and the operation end checks the checking progress through the AR equipment and manually adjusts the target path, so that the checking path of the intelligent checking robot can be optimized in real time in a dynamic environment, and the checking efficiency, the coverage rate and the cruising ability of the robot are improved.

A generalized approach and methodology for addressing surface traversal and coverage of a 3 Dimensional (3-D) object receives a 3-D wireframe or similar Cartesian based representation, converts the 3-D representation to a u,v system or mapping. Often employed for texture mapping, u.v grid systems define a two dimensional form of an object, often referred to as “unfolding” of an object. Configurations herein define a u.v grid system directly on the 3-D object for computing a coverage path, typically an aggregation of raster passes to traverse an entire 3-D surface. From a robotic manipulator, a 3D freeform surface, and task constraints, the approach determines whether there exists a feasible continuous motion plan to cover the surface, and if so, produces a uniform coverage path that best satisfies task constraints resulting from the physical object and robot kinematics.

The invention provides an inspection robot path optimization method and system based on dynamic obstacle avoidance, and the method comprises the steps: enabling an inspection robot to carry out the multi-layer convolution processing of gas and wind direction data through obtaining the turbulence data of a gas leakage source, the wind direction data of a meteorological station, and environment reflection feature data in real time, and generating a high-precision gas diffusion prediction map, meanwhile, a transparent obstacle optical contour and an unstructured obstacle geometric parameter are extracted from reflection data, and a three-dimensional space mapping relation with a gas diffusion diagram is established. On the basis of the mapping relation and the preset task priority, path control parameters are dynamically generated, global path node distribution is optimized, and the robot path is made to adapt to the composite risk environment of gas concentration sudden change and obstacle movement in real time. According to the invention, the dynamic obstacle avoidance precision and the safety response speed of the inspection robot path in a complex dangerous environment are improved.

The invention relates to the technical field of industrial automation and intelligent detection, and discloses a multi-mode industrial line inspection robot and system.The robot comprises a crawler saddle, a machine body is installed at the top of the crawler saddle, and mechanical arms are arranged at the driving ends of the two sides of the machine body and used for controlling a detection assembly to make contact with the outer wall of a rail and the outer wall of a pipeline; a cleaning assembly is arranged on the front side of the detection assembly, the cleaning assembly is used for removing dirt on the outer wall of a to-be-detected part before detection, and the system comprises a main control and navigation module used for robot path monitoring, environment perception and dynamic obstacle avoidance and achieving autonomous navigation and path adjustment in a complex environment in combination with GPS and IMU data. Through ground and high-altitude division cooperation and multi-modal detection, all-directional inspection in a complex scene is realized, the applicability, the detection precision and the inspection efficiency are improved, the problem of insufficient applicability of a single scene in the prior art is solved, and an efficient scheme is provided for intelligent maintenance of industrial lines.

The invention discloses a booster station inspection robot path guiding method, and relates to the technical field of inspection robot path guiding, in a booster station inspection process, a dynamic simulation model is established, so that the spatial layout and operation state of corresponding equipment in a booster station can be comprehensively known, and equipment information in the booster station is scanned by using a high-definition camera; therefore, detailed data such as equipment size, position and serial number are obtained, the dynamic simulation model is beneficial to reflecting the operation condition of the booster station in real time, it is ensured that the inspection robot has good adaptability and flexibility in a complex environment, the efficiency and accuracy of the inspection task are further improved, and in the inspection path priority analysis process, the inspection efficiency is improved. By acquiring the inspection influence parameters of the inspection paths, the inspection paths can be scientifically and reasonably selected, effective path allocation is performed on the inspection robot according to the importance and complexity of the paths, and autonomous intelligence of the inspection strategy can be realized.

The invention relates to the technical field of robot scheduling, and discloses an environmental sanitation robot scheduling method, device and equipment based on environmental sanitation task distribution, and the method comprises the steps: collecting the garbage accumulation information and people flow density data of a plurality of urban environmental sanitation regions, and carrying out the task distribution prediction through a space-time convolution network model, an environmental sanitation demand prediction result is obtained; according to the environmental sanitation demand prediction result, creating an urban region division scheme of a plurality of urban environmental sanitation regions and robot initial grouping configuration information; according to the urban area division scheme and the initial grouping configuration information of the robots, dynamic scheduling grouping of the sanitation robots is calculated in combination with the real-time garbage accumulation event data; the path planning and cleaning operation instruction of each environmental sanitation robot is output in groups based on dynamic scheduling of the environmental sanitation robots, intelligent planning of the paths of the environmental sanitation robots is realized, and the collaborative operation efficiency and stability of a multi-environmental sanitation robot system are ensured.

The invention relates to a robot path planning method and device and computer equipment. The method comprises the following steps: acquiring a current obstacle map corresponding to a target area; dividing a non-obstacle area in a current obstacle map based on a robot historical operation track corresponding to the target area to obtain at least two non-obstacle sub-areas; determining a target operation area from the at least two non-obstacle sub-areas, and generating an initial robot path corresponding to the target operation area from the current obstacle map; determining an optimized path point corresponding to the current path point from each candidate path point of which the connecting line with the current path point does not pass through the impassable area, taking the optimized path point corresponding to the current path point as a next current path point, and sequentially obtaining optimized path points respectively corresponding to each current path point; and generating a target robot path corresponding to the target operation area based on each optimized path point. By adopting the method, the accuracy of robot path planning can be improved.

The invention belongs to the field of automatic measurement and lineation of complex castings, and discloses a double-robot high-precision calibration and online optimization method, which comprises the following steps of: based on hand-eye calibration, four-point calibration and the like, respectively calibrating each part of a casting-measurement system-lineation system during system deployment to obtain high-precision system parameter initial values; on the basis of online collection of calibration data in the measurement-analysis-lineation process of a visual tracking system, online optimization of multi-robot system parameters is achieved, optimized robot paths are fed back on the basis of a data control center, and high-precision stable operation of robot lineation is guaranteed.

The invention discloses a sensing optical cable laying robot path planning and obstacle avoidance system and method, and belongs to the technical field of robot path planning. The system comprises an environment sensing module, a data preprocessing module, a path planning module, an obstacle avoidance control module and a post-processing module. According to the method, environment information is acquired and preprocessed through a laser radar and a camera, path planning is performed based on an improved dynamic RRT algorithm, a self-adaptive step length adjustment mechanism is introduced, the step length is dynamically adjusted according to environment complexity, obstacle distance and path curvature, and a smooth path is generated by combining real-time obstacle detection and dynamic re-planning functions. A PID control algorithm is adopted to adjust the speed and the steering angle of the robot to achieve obstacle avoidance, and a post-processing module optimizes an evaluation path. The real-time performance, the path quality and the environment adaptability of the robot in a complex dynamic environment are improved, and efficient, safe and accurate laying of optical cables is ensured.

The invention discloses a self-adaptive Gaussian mixture mechanical arm path planning method considering collision escape, and aims to solve the problems of low sampling efficiency, low convergence speed and poor path quality of mechanical arm path planning in a complex environment. The method mainly comprises the following steps: introducing a Gaussian mixture model for dynamic weight adjustment, wherein the model fuses four sampling strategies of target guidance, obstacle avoidance, uniform exploration and collision escape; the weight of each component is adaptively adjusted by evaluating the number of continuous collision times, the distance from the target and the like, so that blind search and heuristic exploration are dynamically balanced, and the sampling efficiency in a complex environment is remarkably improved. In addition, adjacent point search is accelerated by using two-dimensional spatial index, calculation resource allocation is optimized in combination with an early stop mechanism, and the quality of a final path is ensured through path simplification and smoothing. The result shows that the method has remarkable advantages in the aspects of path sampling efficiency, convergence speed and path smoothness.

The invention discloses a synchronous localization and mapping method based on PID real-time correction fusion vision, and relates to the technical field of automatic control and environmental perception, and the method comprises the steps: collecting the environmental data of a robot in real time, and carrying out the preprocessing; the direction angle of each feature point is extracted and calculated based on the environment data, and path information is calculated; a state space and an action space are defined based on the path information, and a control signal of the robot is adjusted through an SAC-PID controller; and performing visual SLAM by using ORB-SLAM to output a global consistent map. According to the method, the robot path is intelligently optimized and adjusted and dynamically corrected through the SAC-PID controller combined with reinforcement learning, so that the precision and stability of path tracking are improved, a global consistent map is constructed and optimized through the ORB-SLAM technology, the navigation ability of the robot in a complex dynamic environment is further improved, and the navigation efficiency of the robot in a complex dynamic environment is improved. The self-adaptability of the controller is enhanced, the environment perception technology is optimized, and the defects of insufficient control precision and poor map consistency in the prior art are overcome.

The invention provides a deep reinforcement learning kernel emergency robot path planning method introducing a dose factor, and relates to the technical field of robot path planning, the method takes a deep reinforcement learning algorithm as a basis, utilizes an A * algorithm heuristic function, introduces an accumulated radiation dose operator and a distance operator, and obtains the path planning result of the deep reinforcement learning kernel emergency robot. Training to obtain a target network applied to radiation environment path planning; and by using the target network, when the radiation dose and the obstacle are dynamically changed, completing adaptive adjustment of the path of the nuclear emergency robot. According to the method, the interaction capability of the nuclear emergency robot with the environment in the operation process is enhanced, a collision-free self-adaptive operation path with small radiation risk is provided for the nuclear emergency robot, and support is provided for emergency decision making of the nuclear emergency robot.

A path deadlock processing method and apparatus for a mobile robot, and a computing device and a computer-readable storage medium. The method comprises: determining target deadlock loop data, map data corresponding to the target deadlock loop data, and initial mobile robot data corresponding to the map data (202); according to the target deadlock loop data, determining at least two deadlocked mobile robots (204); according to the map data and the initial mobile robot data, using a path deadlock processing strategy determination method to determine an initial path deadlock processing strategy for each deadlocked mobile robot, wherein the path deadlock processing strategy determination method comprises: when it is determined that there is a conflicting robot which has a conflict with any deadlocked mobile robot, calculating an avoidance path of the conflicting robot, and / or, when it is determined that there is an obstacle robot blocking the conflicting robot, calculating an avoidance path of the obstacle robot (206); and determining a target path deadlock processing strategy from among the initial path deadlock processing strategies of the deadlocked mobile robots, and releasing a target deadlock loop according to the target path deadlock processing strategy (208). While taking into consideration an avoidance path of a conflicting robot of each deadlocked mobile robot and an avoidance path of an obstacle robot of the conflicting robot, the present method accurately calculates an initial path deadlock processing strategy for each deadlocked mobile robot, so that, in the case of complex path deadlock, the complex path deadlock can be released.

The invention discloses a robot redundant attitude planning method and system based on position and obstacle constraints, and the method comprises the following steps: generating a planning path of a robot based on a redundant attitude space and a known robot path position point, the planning path being represented by a position space and an attitude space; constructing an approximate key collision plane; calculating a feasible attitude area based on the obstacle information and the redundant attitude space on the approximate key collision plane; estimating a preferential sampling attitude area; continuously sampling in the position space and the attitude space, acquiring a pose node to expand the random search tree until a target node is reached, and acquiring a target path; and iterating the random search tree extension operation to obtain more target paths, and selecting a target path with the lowest total cost from all the obtained target paths. According to the method, the exploration efficiency of redundant attitude planning can be improved, the integrity and safety of the process are ensured, and the method has relatively high applicability.

The invention provides a quadruped robot path optimization method and system, and the method comprises the steps: building an overlapping operation window recognition mechanism through obtaining the state information and topographic data of a robot, generating a risk map based on a conflict probability, and constructing a hierarchical task chain; establishing path-terrain mapping, identifying a vortex region through an energy flow channel and flow field rotation characteristics, and forming a path dominant region; performing task density analysis on the dominant region, and forming a complementary rhythm through peak-valley separation and frequency analysis to generate an equilibrium flow; main and auxiliary path assignment is carried out on the balanced flow, and high-density path reserve is formed through load analysis and compression processing; performing risk detection on the main path to obtain an early warning, triggering reserve release according to the early warning, and performing cross weaving with the main path to form a reinforcing network; a cooperative propagation matrix is constructed based on a reinforcement network, flow field diffusion analysis is carried out to generate discrete path configuration, a convergence center is established, a unified control command is output, and the cooperative path optimization problem under the complex terrain is effectively solved.

The invention discloses a welding robot system based on visual guidance, which comprises an artificial potential field welding robot path planning algorithm, the artificial potential field welding robot path planning algorithm is combined with an ant colony algorithm, and the artificial potential field welding robot path planning algorithm and the ant colony algorithm comprise artificial potential field function simulation, ant colony algorithm simulation and path search, pruning strategy and path optimization mechanism. And the step operation system (GBOS) comprises three-dimensional model importing and welding seam positioning. According to the method, the welding path planning technology of the ant colony algorithm of the artificial potential field function is fused, the welding robot is guided to avoid obstacles by simulating attraction force and repulsive force, the optimal path can be accurately recognized, the coverage rate of the searched path to the environment can reach 74.63%, a good convergence curve can be obtained, and after the pruning strategy is introduced, the optimal path can be obtained. According to the method, the path can be optimized, redundant broken lines or path sections are removed, and optimization of the path length and complexity is achieved, so that the welding time and cost are reduced, and the welding efficiency is improved.

The invention proposes a robot path planning algorithm based on a particle swarm optimization algorithm and a dynamic window method, and relates to the field of control theories and electronic information, and the method comprises the steps: introducing an inertia weight updating strategy based on a state factor, setting adaptive parameters and crossover and mutation operators to improve the global search capability, increase the population diversity, and improve the robot path planning precision. On-line self-adaptive updating of inertia weight and learning factors is realized on line in combination with Q-learning, gene combination modes are enriched through crossover operators, convergence and exploratory performance of the algorithm are improved, an obstacle avoidance strategy of a traditional DWA algorithm is improved, weight parameters of a dynamic window evaluation function are dynamically adjusted according to real-time information of a target and an obstacle, and an obstacle avoidance algorithm is established. According to the method, the global planning is adopted, the path points generated through global planning are adopted as temporary targets, fusion of MOQLCOPSO and the improved DWA algorithm is achieved, local optimum is effectively avoided, the planning efficiency and path safety are improved, and the method is suitable for mobile robot navigation under the complex three-dimensional terrain.

The invention relates to the technical field of elevator control, and discloses an elevator task control method and system based on medical logistics robot scheduling. The method comprises the steps that according to a dispatching task of a medical logistics robot, the robot position and electric quantity information of an available medical logistics robot, a dispatching task to be executed, the floor where an elevator of an available elevator is located, the running direction, whether the elevator is fully loaded or not and the floor where the elevator needs to stop are recognized, and a distribution combination is constructed; an evaluation function is constructed according to the robot path length of each scheduling task in the distribution combination and the time of taking the corresponding available elevator, and a target medical logistics robot and a target available elevator are obtained through scheduling optimization based on the evaluation function; constructing a target control signal according to the target medical logistics robot and the target available elevator; and a scheduling path of the target medical logistics robot is generated, and elevator task control is conducted on the target available elevator according to the scheduling path and the target control signal. The accuracy of elevator task control can be improved.

The invention provides an artificial intelligence-assisted intelligent control decision method, which comprises the steps of obtaining real-time environment perception data, constructing an obstacle group motion intention prediction model by adopting a deep reinforcement learning algorithm, determining intention probability distribution and a prediction time window of an obstacle group, and obtaining a short-term motion trend of the obstacle group; according to prediction results of the trajectory deviation angle deviation and the deviation acceleration peak value, a robot path priority order is determined, and a candidate path set is generated; acquiring a navigation instruction of an optimal path, and adjusting motion parameters of the robot to obtain new environment sensing data; and according to the updated short-term movement trend of the obstacle group, circularly adjusting the path priority order, generating a new navigation instruction, and obtaining a continuously optimized robot movement track.

The invention relates to the technical field of motion planning, and discloses a robot path fairing and synchronizing method based on an Airthoid curve, comprising the following steps: determining a tool nose fairing curve transition length according to a tool nose approximation error and a parameter synchronization strategy; determining a preliminary cutter shaft fairing curve; calculating the transition length of the cutter shaft fairing curve according to the preliminary cutter shaft fairing curve and a parameter synchronization strategy, and determining a final cutter shaft fairing curve; according to the maximum curvature of the final cutter shaft fairing curve and the maximum curvature of the cutter tip fairing curve, curvature parameter synchronization is achieved; and spline parameter synchronization is realized according to the B spline curve corresponding to the remaining straight line segment of the cutter shaft fairing curve and the cutter tip fairing curve. According to the method, the approximation error constraint of the cutter shaft and the cutter tip is realized by utilizing Airtoid curve analysis, and the calculation efficiency is high; parameter synchronization of the fairing curve and the remaining straight line segment is achieved through the remaining straight line segment B spline curve, and it is guaranteed that C3 at the connecting point of the curve segment and the straight line segment is continuous.

The invention discloses a multi-robot control system, and relates to the field of robot path planning. According to the system, in a robot path driving task, candidate path nodes are selected by randomly switching target guidance and a global exploration mode, feasible nodes and path segments are determined by combining historical path nodes, and the path nodes are expanded after collision detection of a static obstacle and a dynamic robot. The system also dynamically adjusts a driving step length based on a load rate, sets a safety margin including load compensation, and realizes multi-machine cooperation through local path topological optimization and distributed decision. According to the method, the multi-robot path conflict and collision risk is effectively reduced, the heavy-load working condition motion stability and the system cooperation efficiency are improved, and the method is suitable for a multi-robot cooperation operation scene.

The invention discloses a robot automatic obstacle avoidance target recognition system and method, and relates to the technical field of autonomous navigation, and the method comprises the steps: obtaining and fusing an obstacle boundary map, a passable region mask and a dynamic target hot area map, and outputting an environment feature map; inputting the environment feature map into a pre-trained deep reinforcement learning model, and generating an obstacle avoidance instruction set according to the real-time state of the robot and a global path planning result; and acquiring actual path data of the robot, comparing the actual path data with an expected path point by point, and if the continuous deviation feature exceeds a preset continuous deviation feature threshold, inputting the environment feature map, the obstacle avoidance instruction set and the continuous deviation feature into the deep reinforcement learning model for updating. According to the invention, the obstacle avoidance capability of the autonomous robot in a dynamic complex environment is improved through multi-modal sensing fusion and a closed-loop dynamic optimization mechanism.

A method for robot path planning uses skills extracted from a motion program of human teaches applied to a new obstacle environment. A three-dimensional convolutional neural network is used to extract features representing an obstacle environment, wherein feature vector representation of an obstacle overcomes problems encountered when point cloud obstacle data is used. Obstacle feature data and robot path start and target points are provided to an encoder / decoder neural network system trained to extract skills from a database of human-generated motion programs. An encoder / decoder neural network system generates a waypoint distribution for a current obstacle environment and a start / target point. The waypoint distribution is used to perform final collision-free path generation using a fast exploring random tree (RRT) technique or an optimization-based technique.

The invention provides a magnetic suspension underwater robot path following control method, a magnetic suspension underwater robot path following control device, magnetic suspension underwater robot path following control equipment and a medium, and relates to the field of underwater robot path following control. The motion control bottleneck of a traditional underwater robot in a complex environment is broken through. In the aspect of dynamic modeling, interaction between magnetic force and fluid resistance is considered, and underwater motion characteristics of the robot are accurately described. A nonlinear control strategy combining the Lyapunov stability theory and sliding mode control is adopted, and a trajectory tracking controller with high robustness is designed to ensure that the robot can stably and efficiently operate along a predetermined trajectory in a complex underwater environment. The maneuverability and the operation precision of the underwater robot are remarkably improved, a more reliable solution is provided for the fields of ocean exploration, resource development, environment monitoring and the like, and huge application potential and market value are shown.