1312 results about "Motion planning" patented technology

The invention discloses a manipulator grabbing planning system and method based on visual identification, and relates to the technical field of robots, the manipulator grabbing planning system comprises a visual perception module, a coordinate conversion module, a motion planning module, a control execution module, a tail end perception module and a system integration and communication module; the visual perception module realizes multi-view target detection, semantic segmentation and three-dimensional pose estimation through a binocular depth camera; the coordinate conversion module is used for converting a target pose under a camera coordinate system into a world coordinate under a mechanical arm base coordinate system; the motion planning module is responsible for generating a mechanical arm grabbing path, optimizing a strategy and supporting generalization migration of a multi-form mechanical arm; the control execution module drives the six-axis cooperative mechanical arm to complete the grabbing action, and vision-force mixed feedback closed-loop control is achieved. The tail end sensing module monitors the grabbing state in real time through a touch sensor and dynamically adjusts a grabbing strategy; and the system integration and communication module is used for realizing real-time data interaction and cooperative work among the modules of the system.

The invention provides an industrial robot motion planning method based on a diffusion model. The method comprises the following steps: firstly, pre-training a point cloud encoder to compress and embed obstacle point cloud under task conditions into a potential space, and constructing a forward kinematics robot state coding algorithm and a path data collision detection module; then, task information is coded based on the pre-trained point cloud encoder, noise adding and noise reduction are carried out on task data based on a Transform diffusion model, and modeling is carried out on data probability distribution of a physical relationship between robot connecting rods; and finally, based on the trained neural network model, combining a small amount of motion feedback, generating an image from random Gaussian noise, and further improving the generation speed and the success rate. Finally, the neural network model can output an optimal path under a specific task condition.

This document describes trajectory planning in a three-dimensional (3D) search space with a space-time artificial potential field. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a parking trajectory algorithm to determine a trajectory by searching in a space-time artificial potential field. The trajectory includes a series of 3D waypoints, including two-dimensional (2D) positional coordinates and time coordinates, to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the trajectory using an assisted-driving or autonomous-driving system. In this way, path searching is performed in the entire 3D search space rather than iterating between two 2D search planes. This allows motion planning for autonomous parking, especially with static and dynamic objects present, to determine an optimal trajectory in a single iteration.

The invention provides a motion control method and system for an intelligent robot, and the method comprises the steps: collecting environment and state data through an intelligent sensor group of a humanoid robot, inputting the environment and state data into a pre-training first neural network model, and obtaining motion prediction data and an environment analysis result; constructing a motion planning model, and performing energy consumption-stability multi-objective optimization on the joint motion track by adopting a preset first algorithm; the central controller generates a joint position, speed and torque reference trajectory based on an optimization result; and the local second controller of each joint locally adjusts the reference trajectory within the prediction time domain according to the real-time feedback. According to the invention, multiple sensors are combined with the mixed attention neural network to realize environment and self state intelligent perception, and the problem of multi-sensor data fusion time sequence dependence is solved; through energy consumption-stability multi-objective optimization, the complex environment movement efficiency is remarkably improved; the central controller and the local controller work cooperatively, and in combination with an edge computing architecture, the communication delay is reduced, and the system response speed is improved.

The invention relates to the technical field of body-equipped intelligent robots, in particular to a multi-mode body-equipped intelligent robot control method and device, and the method comprises the steps: synchronously collecting visual, auditory, tactile, force sense and body perception information, and unifying the information to the same time-space reference through a cross-mode time-space stamp alignment mechanism; hierarchical feature extraction and fusion are carried out on the multi-modal information, and unified multi-modal scene state representation is generated; reasoning a decision based on the representation by using a body agent framework, and outputting a control instruction; motion planning and control, visual servo tracking in a non-contact stage and dynamic parameter correction in a contact stage are executed according to instructions; optimizing the multi-modal strategy network through an incremental strategy distillation mechanism based on the interactive data flow; the problem of space-time asynchronization of multi-modal sensing information is solved through a cross-modal space-time stamp alignment mechanism.

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 provides a robot adaptive motion planning method and system based on compliance control, the method adopts admittance control as a compliance algorithm, the compliance is stronger compared with the traditional rigid control, and different compliance degrees can be selected according to the environment; combining inverse kinematics, trajectory interpolation and environmental parameters to construct an optimal reset trajectory under the constraint of a joint space safety region; by sensing external acting force and dynamically adjusting control parameters, the flexibility of the robot in a complex working environment is improved; a chassis-mechanical arm joint kinematics model is provided, and the problem that the working space of a traditional mechanical arm is limited is solved.

A system includes a camera, a robot, and a management system. The camera is configured to capture one or more images of a material handling environment. The material handling environment normally includes one or more items. The robot is at least in part configured to move along a travel path relative to the items, and the robot is configured to pick the items. The management system is configured to monitor the material handling environment through the images from the camera. The management system is configured to determine dynamically multiple picking solutions for the items in real-time while the robot moves along the travel path.

The invention provides an unmanned surface vehicle cooperative formation control method, which comprises the following steps of: acquiring static and dynamic environment data of a sea in real time through a plurality of arranged sensors, respectively performing time synchronization, coordinate conversion, data denoising and data fusion on the acquired data to obtain a total data set after fusion, constructing a three-dimensional grid map, and constructing an unmanned surface vehicle cooperative formation control system. Dividing the environment into fixed grid units; a real-time optimal path is generated for the unmanned ship formation through a motion planning module, obstacle avoidance is carried out based on an obstacle avoidance mechanism module, and it is ensured that the unmanned ships can gradually return to a preset path after obstacle avoidance; based on the path tracking and control module, the unmanned ship can stably follow the adjusted path. According to the invention, comprehensive environmental perception and motion planning module design is realized, the unmanned ship can adapt to a complex environment, the obstacle avoidance mechanism module design improves the flexibility of obstacle avoidance, and the path tracking and control module design ensures that the unmanned ship can stably follow a path and does not generate overlarge course deviation due to the change of an external environment.

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 discloses a double-mechanical-arm cooperative motion planning method based on deep reinforcement learning, and the method comprises the steps: building a workbench simulation environment, and enabling double mechanical arms to carry out cooperative motion planning and testing in the environment; a deep reinforcement learning model of the double-mechanical-arm motion planning system is established, the model makes a decision according to the current state of the double mechanical arms in each time step so as to output a motion instruction of the double mechanical arms, and after the state of the double mechanical arms is updated, a reward value for executing the motion instruction is obtained through calculation; generating an expert track and storing the expert track in an experience playback pool as an expert sample; the states, the action instructions and the reward values formed by different time steps are stored in an experience playback pool in a grouping mode to serve as experience samples; and regularly extracting samples from the experience playback pool for batch training and updating model parameters. The invention further provides a system, electronic equipment and a storage medium. According to the method, the double-mechanical-arm cooperative movement task with the tail end posture constraint can be efficiently completed in a limited space.

The invention discloses a mechanical arm natural language instruction control system and method based on a large language model, and belongs to the field of intelligent manufacturing. Aiming at the limitation that traditional mechanical arm control depends on pre-programming and a static rule library, a dynamic mapping mode from a natural language instruction to an atomic action sequence is designed, an atomic skill library including detection, grabbing, moving, placement and other operations is constructed, and semantic analysis and a multi-mode cooperation technology are combined, so that the atomic action sequence is obtained. And support is provided for man-machine cooperation of a flexible assembly task. The method specifically comprises the steps that a DeepSeek-Distil-Llam-8B large model and a LoRA fine tuning technology are adopted, and a natural language instruction is converted into an executable atomic action sequence; based on a transfer learning optimized YOLOv8 target detection technology and a binocular vision positioning technology, a sensing module adaptive to an assembly scene is constructed and is fused with a mechanical arm motion planning module, and positioning grabbing of parts and tools is achieved. And an interactive interface is built by combining a voice-to-text large model and a Gradio front-end framework, so that the convenience of man-machine interaction is improved. By optimizing large model reasoning and motion planning cooperation efficiency, response delay from instructions to execution is reduced, and an efficient and extensible solution is provided for man-machine cooperation in intelligent manufacturing.

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 surgical robot motion control, and discloses a surgical robot motion planning method based on a diffusion model, and the method comprises the steps: obtaining obstacle point cloud data, an initial posture and a target posture in a surgical environment; encoding the obstacle point cloud data into a potential space, and combining the encoded information with the initial attitude and the target attitude into a condition code; a Transform structure of an encoder is adopted to replace a U-Net structure in a traditional diffusion model, and the diffusion model is trained through forward diffusion and reverse denoising processes; in training, using a comprehensive loss function to optimize model performance, combining configuration space loss, geometric task space loss and collision loss, and introducing physical constraints to ensure that the trajectory conforms to kinematic characteristics and avoid collision; and in the operation task, generating a motion track based on the trained diffusion model. According to the invention, the motion planning efficiency and safety of the surgical robot in a complex environment can be improved.

In various examples, a gradient-based motion planner evaluates a cost function corresponding to routes for a machine and an obstacle to jointly update the routes. The cost function may include terms to penalize deviation from an initial route predicted for the obstacle and acceleration or jerk for the obstacle. The routes for the machine and the obstacle that are updated may be selected using motion classes that characterize relative motion between a route for the machine and a route for the obstacle. A motion class may be based at least on an angular distance between the machine and the agent and free-end homotopy, where members of the class execute the same relative motion with respect to other agents while being continuously transformable to any other member of the class. The members of the class may have the same start point and different end points.

The invention provides a real-time motion planning system based on dynamic environment perception, and aims to solve the limitation of the prior art in a dynamic environment. According to the system, environment data are captured and processed in real time through a highly integrated sensor fusion technology, and an accurate environment model is generated. And in combination with a deep learning algorithm, the system can predict environment change and realize quick response. In addition, the system adopts an advanced dynamic path adjustment algorithm to ensure path optimization and safe navigation of the machine in a complex environment. Particularly, a multi-machine collaborative planning mechanism is introduced, and efficient collaboration and avoidance among machine groups are realized through a distributed consistency algorithm and multi-agent reinforcement learning. On the whole, the system obviously improves the application potential of the machine in a variable environment, enhances the adaptability and stability of the machine, and opens up a new possibility for the development of the intelligent machine equipment technology.

The invention relates to the technical field of artificial intelligence welding, and discloses an automatic welding robot based on artificial intelligence and a welding system thereof. The system comprises a welding feature acquisition module, a process parameter generation module, a real-time regulation and control module and a motion planning module. The welding characteristic acquisition module is used for acquiring three-dimensional contour data, material component information and welding seam geometric parameters of a workpiece to be welded, and generating a welding characteristic spectrum through characteristic fusion; a process parameter generation module retrieves a matching template from a welding knowledge base according to the parameters, and outputs a reference welding process parameter combination in combination with an environment temperature and humidity compensation coefficient; the real-time regulation and control module dynamically corrects the reference parameters to generate an optimization instruction according to the molten pool form, thermal radiation distribution and electric arc voiceprint characteristics in the welding process; and the motion planning module calculates a motion track, attitude parameters and a speed curve of the welding execution mechanism according to the optimization instruction to form a robot control instruction set. The system can improve the intelligent level of welding, guarantees stable welding quality, and is suitable for welding scenes of multiple manufacturing industries.

The invention discloses an operation and maintenance manipulator intelligent control method and system based on visual identification, and relates to the technical field of intelligent manipulator control, and the method comprises the steps: collecting RGB image data and depth image data of an operation and maintenance operation area, and obtaining a standardized image matrix and a mapping relation matrix; inputting the standardized image matrix into an improved ResNet residual network model, generating a comprehensive feature descriptor, and calculating a spatial position coordinate and an attitude angle of the target equipment based on the mapping relation matrix; based on the current joint angle state of the manipulator, an improved Jacobian matrix inverse kinematics algorithm is used for solving a target angle sequence of each joint, a preset operation mode library is matched based on the comprehensive feature descriptor, and a grabbing force parameter and a motion speed parameter are determined; and converting the target angle sequence into a control instruction, and sending the control instruction to each joint driver of the manipulator to drive the manipulator to complete action planning. According to the invention, full-process automation from environment perception to task execution is realized.

The invention provides an optimization method for whole-body motion planning of a mobile manipulator, and relates to the technical field of motion planning of the manipulator, and the method comprises the steps: determining environment features and a position gradient when a joint of the mobile manipulator changes based on a depth image shot by the mobile manipulator; performing strategy planning on the motion state and the motion action of the mobile mechanical arm through an actor-commentator network subjected to reinforcement learning training to obtain an expected speed of an end effector in the mobile mechanical arm; position constraint conditions used for obstacle avoidance of the movable mechanical arm are constructed based on the position gradient, and joint constraint parameters used for constraining the decision variables are constructed; and optimizing the decision variable based on a quadratic programming solver determined by the position constraint condition and the joint constraint parameter to obtain a planning result of the joint speed. According to the method and the device, the problem of limitation in the aspects of planning task completion, environment perception, constraint expression, obstacle avoidance and motion coordination during motion planning of the mobile mechanical arm is solved.

The present disclosure provides an example method that includes: (a) obtaining context data descriptive of an environment surrounding an autonomous vehicle, the context data based on map data and perception data; (b) generating, by a proposer and based on the context data: (i) a plurality of candidate trajectories, and (ii) a plurality of actor forecasts for a plurality of actors in the environment; (c) generating, by a ranker and based on the context data, the plurality of candidate trajectories, and the plurality of actor forecasts, a ranking of the plurality of candidate trajectories; and (d) controlling a motion of the autonomous vehicle based on a candidate trajectory selected based on the ranking of the plurality of candidate trajectories, wherein the proposer comprises a first machine-learned model and the ranker comprises a second machine-learned model, and wherein the first machine-learned model and the second machine-learned model use a common backbone architecture.

A trajectory for an autonomous machine may be evaluated for safety based at least on determining whether the autonomous machine would be capable of occupying points of the trajectory in space-time while still being able to avoid a potential future collision with one or more objects in the environment through use of one or more safety procedures. To do so, a point of the trajectory may be evaluated for conflict based at least on a comparison between points in space-time that correspond to the autonomous machine executing the safety procedure(s) from the point and arrival times of the one or more objects to corresponding position(s) in the environment. A trajectory may be sampled and evaluated for conflicts at various points throughout the trajectory. Based on results of one or more evaluations, the trajectory may be scored, eliminated from consideration, or otherwise considered for control of the autonomous machine.

The present disclosure provides an example method for obtaining labeled trajectories. The example method can include obtaining log data describing a trajectory of a vehicle traveling through an environment. The example method can include determining a suboptimal condition associated with the trajectory. The example method can include generating label data that characterizes the suboptimal condition along one or more constraint dimensions of a motion planner of the autonomous vehicle control system. The example method can include generating a training example for training the one or more machine-learned models of the autonomous vehicle control system to decrease a probability of the autonomous vehicle control system inducing the suboptimal condition.

The invention discloses a quadruped robot autonomous decision control system based on multi-modal perception, and belongs to the technical field of robot motion, and the system specifically comprises the steps: collecting visual, force and sound information through multiple sensors of a camera, a force sensor and a microphone array, and carrying out the preprocessing; according to the processed information, a diagonal gait, leg track and motion path planning scheme is generated by adopting a motion planning strategy; generating a specific control instruction to drive the robot to move, and monitoring and adjusting in real time; after the task is completed, the motion performance of the robot is evaluated, and a motion planning and control method is optimized according to an evaluation result; the autonomous motion ability and adaptability of the quadruped robot in a complex environment are improved.

The invention discloses a quadruped robot motion generation method under sparse terrain based on terrain reconstruction, and relates to the technical field of quadruped robot motion generation. Training a motion planning controller with a terrain perception memory mechanism based on multi-modal perception input; the elevation map of the terrain around the current quadruped robot is displayed and estimated from the body sensing information and the visual observation information of the airborne depth camera through a visual terrain reconstruction module; then, feature fusion is carried out on elevation map reconstruction results of the ontology perception information and the visual observation information, and comprehensive perception representation is obtained; and taking the comprehensive perception representation as input, constructing a corresponding reward function, and giving the expected moment of force of each joint of the quadruped robot in a complex discrete terrain with a sparse passable area in an end-to-end manner. The self gait can be adjusted in real time or in advance, the flexibility, adaptability and reliability of self movement in complex discrete terrains are improved, and the application of the quadruped robot is expanded.

The invention discloses an automatic driving motion planning method and system driven by a large language model, and the method comprises the steps: obtaining a current vehicle state based on a perception function library, and obtaining observation information; acquiring recall information based on the observation information; obtaining cognitive information based on the recall information; providing cognitive information to the large language model, generating an advanced driving strategy combination, and planning a driving track according to a reasoning result and the advanced driving strategy combination; the driving track is arranged in an analog simulation environment to be executed, and feedback is obtained; and selecting to circularly execute the driving track based on the feedback result, or reflecting the driving track. The method has the beneficial effects that the data generalization ability is improved, lifetime learning is realized, the collaboration flexibility and expandability of the system are improved, and the adaptability and reliability of the system in the real world are enhanced.

This document describes motion planning with variable grid resolution for graph-search-based planning. An example system includes a processor that obtains an initial pose, a goal pose, and an obstacle map for an environment. The processor uses a motion-planning algorithm to determine a path or trajectory using two or more grid resolutions for a graph-based search. The path includes a series of waypoints, including two-dimensional positional coordinates (and time coordinates if a trajectory), to navigate from the initial pose towards the goal pose. Operation of the host vehicle is then controlled to maneuver along the path using an assisted-driving or autonomous-driving system. In this way, motion planning is performed for the entire path but uses a coarser grid resolution for the portion nearer the goal pose. This allows motion planning for autonomous parking, especially in environments that include static and dynamic objects, to be handled in a more computationally-efficient manner.

In various examples, systems and methods are disclosed relating to training machine learning models using human demonstration of segments of a task, where other segments of the task are performed by a planning method, such as a Task and Motion Planning (TAMP) system. A method may include segmenting a task to be performed by a robot into segments, determining a first set of instructions of a plurality of sets of instructions for operating the robot to perform a first objective of a first segment, determining that the plurality of sets of instructions is inadequate to perform a second objective of a second segment, receiving from a user device a second set of instructions for operating the robot for the second segment following an end of the first segment, and updating a machine learning model for controlling the robot using the second set of instructions for the second segment.

A method is for training a base model for object detection, trajectory prediction, and / or motion planning of a vehicle. The method includes providing a training data set of image data, with each piece of image data having information about at least one driving scene from a point of view of the vehicle, and providing a knowledge graph including domain-specific knowledge of the at least one driving scene. The method further includes optionally partitioning the image data into a plurality of image sections, and generating information matrices corresponding to the image sections by assigning domain-specific knowledge about the at least one driving scene extracted from the knowledge graph and / or directly from the image data to the plurality of image sections of the image data. The method also includes training the base model based on the information matrices.

The structures and algorithms described herein employ look ahead motion planning, in which motion planning for a least two goals is performed before a robot executes the resulting motion plans, and the ability to transition between the preceding one of the motion plans to a subsequent (e.g. following) one of the motions plans is assessed. Thus, the system can determine whether a robot will get trapped (e.g., blocked by another robot) at the end of a first motion plan, preventing, limiting or delaying execution of a second motion plan. Detection of such a condition can cause one or more remedial actions can be taken, for example generating a new, revised or replacement first motion plan. Other remedial action can moving another robot, performing motion planning for the other robot, to alleviate a blocking condition and / or determining a new order for the goals.

The invention discloses a robot dynamic obstacle avoidance trajectory planning method, which comprises the following steps: determining the position of a workpiece by taking a global camera as guidance, and acquiring a global path from a starting point to a target point by taking a Bi-RRT algorithm as a global planner in a motion planning process; when a moving obstacle is detected in the operation area, a depth camera on a robot manipulator is turned on to capture depth information of a local environment in real time; an obstacle in a local environment is simplified into an AABB bounding box, a mechanical arm connecting rod of the robot is simplified into space line segments, and whether the mechanical arm connecting rod collides with the obstacle or not is pre-judged by judging whether all the line segments intersect with the bounding box or not; and when the mechanical arm connecting rod collides with the obstacle, local motion planning is carried out based on the SAC algorithm, and a corresponding obstacle avoidance action instruction is generated. According to the method, the global optimal path can be quickly planned, and accurate dynamic obstacle avoidance can be realized. Correspondingly, the invention further provides a robot dynamic obstacle avoidance trajectory planning system, electronic equipment and a storage medium.