15 results about "Robot planning" patented technology

A controller is provided for manipulating an object using a robotic arm with a gripper. The controller includes: a Large Language Model (LLM) planner configured to acquire the state and the task description, and generate a sequence of motion commands to operate the robotic arm with the gripper based on the task description, the current observations, and historical information including historical actions and historical observations from previous steps. The controller also includes: a robot interface configured to generate and send commands to operate the robotic arm in response to the sequence of motions from the LLM planner; and an LLM evaluator configured to acquire the state and the current observations, analyze the historical information including historical actions and historical observations from previous steps, evaluate missing information required to complete the task, evaluate new information acquired by the sensors, estimate the task execution state after the robotic arm operates the generated sequence of motions, and output updated information to the LLM planner.

Techniques for controlling a robot includes receiving a task and motion planning (TAMP)-domain language input comprising one or more symbolic elements and one or more continuous elements, generating a TAMP-domain representation based on the TAMP-domain language input, TAMP-domain language data, and one or more subgraph classification rules, generating a robot plan based on the TAMP-domain representation, a state, and a goal, and causing the robot to perform at least part of the robot plan.

The invention belongs to the field of robot planning control, and particularly relates to a method for a cleaning robot to memorize obstacles under the condition of sensing limitation, which comprises the following steps of: acquiring multi-source point cloud data of a laser radar and a depth camera, and uniformly converting the multi-source point cloud data into a robot main body coordinate system through external parameter calibration; effective point clouds are screened based on a sensor visual field model, and sensor weights and distance weights are fused and then mapped to a local grid map; updating the grid occupancy probability by adopting a Bayesian updating rule, and executing time-based linear attenuation on an area which is not continuously observed; and finally, generating a global cost map according to the updated local cost map for path planning and obstacle avoidance control of the robot. According to the invention, the problem of unstable obstacle memory under sensing limited conditions such as a sensor blind area, shielding and illumination change is effectively solved, and the navigation safety and planning efficiency of the cleaning robot in a dynamic environment are improved.

The invention relates to an information processing method for robot planning and an intelligent mowing robot. The information processing method for robot planning is used for an execution unit of the mowing robot. The information processing method for robot planning comprises the steps that S1, an execution unit obtains a grassland modeling graph; s2, obtaining grassland standardization information from the grassland modeling graph; and S3, performing path planning based on the grassland standardization information, and generating a mowing path scheme. In the prior art, under the condition that a mowing robot faces a large-range grassland, the workload for processing a to-be-mowed area is very large, time consumption is long, and the mowing robot is limited by the limited electric quantity of a battery, so that mowing cannot be completed at a time; on the basis of the scheme, the negative influence of the electric quantity on the mowing robot can be reduced under the condition of facing a large-range grassland, the time required for mowing is shortened, and then the mowing efficiency for processing the target large-range grassland is remarkably improved.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for robot planning. One of the methods includes maintaining a library of pre-generated candidate actions that represent respective robot capabilities; receiving an initial task plan for performing a particular task, wherein the initial task plan includes a sequence of two or more actions that comprises a first action followed by a second action; processing the initial task plan to determine that the first effect of the first action does not result in satisfaction of the second precondition of the second action; selecting one or more selected pre-generated candidate actions from the library of pre-generated candidate actions; and modifying the initial task plan to include the one or more selected pre-generated candidate actions as the intermediate actions to generate a final task plan to be executed by the one or more particular robots when performing the particular task.

The application discloses a kind of magnetic micro-robot parallel collaborative field navigation system, it is related to micro-nano operation technical field, and the micro resource conflict resolution scheduler MCDO in this system is based on the smooth single-machine path of dynamic evolution navigation planner DEN, the safety path constraint of conflict-aware evolutionary planner CAEP and the coil current with anti-interference control of double-field disturbance suppressor DFDS, to each magnetic micro-robot planning obstacle avoidance path and outputting multi-machine conflict-free instruction, to drive coil array to generate distributed gradient magnetic field, realize multi-robot independent drive;Micro-vision anchor MVAM is used to track the motion trajectory of magnetic micro-robot in real time, and provides robot pose data for MCDO and DFDS.The above-mentioned module realizes the whole process logic closed-loop management and control from path planning to physical drive, interference suppression to state monitoring, and is improved in path planning efficiency, anti-interference control accuracy, multi-machine cooperation capability, environment adaptation and the like.

This disclosure provides a motion optimization obstacle avoidance control method and system for vehicle-like robots, relating to the field of robot path planning and obstacle avoidance technology. The method includes: defining the state space of the vehicle-like robot; planning the global path of the vehicle-like robot; fitting the sparse state sequence using Hermite curves to obtain a time-space continuous trajectory; establishing a collision model between the vehicle-like robot and obstacles; transforming the geometric separation conditions between the robot and obstacles into a set of continuously differentiable nonlinear algebraic constraints based on duality theory; using the time-space continuous trajectory as a reference trajectory; and constructing a trajectory optimization objective function. This function can solve for the motion control quantities of the vehicle-like robot while satisfying its dynamic constraints, input constraints, and acceleration smoothing constraints, thus achieving obstacle avoidance planning and control of polygonal obstacles. This disclosure enables real-time, full-size robot planning and obstacle avoidance control.

Techniques for controlling a robot include receiving a task and motion planning (TAMP)-domain language input, a goal, and sensor data, generating a plan skeleton based on the TAMP-domain language input, the goal, and the sensor data, generating, based on the plan skeleton, one or more feasible particles, generating, based on the plan skeleton and at least one of the one or more feasible particles, a robot plan, and causing the robot to perform at least part of the robot plan.

A robot location planning method and apparatus based on reinforcement learning. The location planning method comprises: performing, by means of a preset positioning pen module, digital description on the location, shape and posture information of an object to be manipulated by means of a robot, so as to obtain digital object data; by using the digital object data as an input to a deep neural network of a simulation platform corresponding to the robot, and using, as an output, a manipulation location at which the robot controls the object, performing training to obtain an initial location planning model of the robot; on the basis of an application scenario of the object, selecting a corresponding observation space and task return logic to perform scenario optimization on the initial location planning model, so as to obtain a robot location planning model; and on the basis of the robot location planning model, predicting the digital object data of the object to be manipulated, so as to obtain a target location at which the robot manipulates the object to be manipulated. Therefore, on the basis of a trained robot location planning model, the robot planning accuracy meets the requirements of various industrial scenarios, and the workload involved in manual operations is greatly reduced.

The present application relates to the technical field of robot planning, in particular to an advertisement display robot cruise path intelligent planning method for commercial scenarios, comprising: collecting a commercial scenario static environment map, a dynamic people flow density heat map, real-time state information of an advertisement display robot and an advertisement delivery task list. Identifying the passable area of the scene, the people flow gathering hotspot area and the fixed obstacle area to be avoided, generating recommended stopping points according to the task priority, the target crowd portrait and the expected exposure time. Combined with multiple constraint conditions, the ant colony algorithm optimized by the joint evaluation model of delivery value and energy consumption is used to plan the globally optimal cruise path. Real-time monitoring of environmental changes and dynamic adjustment of the route during the journey, recording running and exposure data to update people flow information. The method continuously iterates the path planning logic, adapts to the commercial dynamic complex environment, and realizes the intelligent and reasonable layout of the advertisement display robot cruise path.

The application provides a robot planning method and system based on multimodal large model predictive control, which utilizes the powerful visual reasoning and visual positioning capabilities of the multimodal large model, samples candidate action sequences through target images or text instructions of robot movement and current observation images of the robot, and then predicts future state videos of the sampled candidate action sequences to guide the robot operation, complex path planning of rotation and interaction with scene objects, avoids the need for manual design of basic actions and skills, and breaks through the limitation that previous methods based on multimodal large models can only form rough trajectories without foresight, and realizes robot control planning based on future state prediction.

The planning method of reinforcement binding mechanical arm based on visual semantic constraint, the algorithm first constructs the visual perception system composed of high-precision six-degree-of-freedom mechanical arm, end binding executor, visual sensor and industrial camera. Then, the steel semantic understanding and key point detection module is developed, the multi-modal deep vision network is fused with the RGB image and the depth map information, the high-precision semantic segmentation of the reinforcement grid and the binding node key point detection are realized through data preprocessing, feature fusion and other steps, and the binding point coordinate in the world coordinate system is output. Then, the dynamic task decomposition and reinforcement learning decision mechanism is designed, the global binding task is converted into local action sequence, and the path planning and binding sequence are optimized through reinforcement learning. Finally, according to the optimal action sequence output by the reinforcement learning, the adaptive path planning is generated and the precise binding operation is executed. The application effectively improves the automation level of reinforcement binding, and solves the deficiencies of the prior art in visual perception, task planning and path control.

The application specifically relates to a polishing production line for power transformer coils. The production line comprises a transverse polishing conveying line, a feeding mechanism and a discharging mechanism arranged at the transverse two ends of the polishing conveying line respectively, a tool clamp material disc arranged on the polishing conveying line, the tool clamp material disc being used for clamping and fixing materials to convey the materials along the polishing conveying line, a 3D scanning device and a polishing robot arranged in sequence in the conveying direction of the polishing conveying line, the 3D scanning device being used for acquiring a three-dimensional model of the materials and transmitting scanning information to the polishing robot, the polishing robot planning a polishing path through model parameters provided by the 3D scanning device and polishing the materials, and a dust removal system, the dust removal system being used for removing particle dust in polishing. The polishing robot is used to replace manual polishing, circulation and cleaning, is safe and reliable, can be operated cyclically, realizes cooperation of feeding, scanning, polishing and discharging of various stations and realizes full-process automation.

Disclosed herein are systems, methods, and devices for providing mobility management and path planning with an interface between the mobility management functions and path planning functions. A mobility controller may receive an expected location into which a robot plans to move at a predefined time and determine, based on the expected location and a current location of the robot, an infrastructure configuration for a wireless infrastructure device that provide wireless connectivity at the expected location. The mobility controller may also generate a message to configure the wireless infrastructure device at the predefined time according to the infrastructure configuration.

The invention discloses an inspection robot path planning and dynamic monitoring system, which comprises a communication module, a dynamic monitoring module, a central scheduling module and a multi-machine cooperative path planning module, and is characterized in that the system is connected with a fixed camera and an inspection robot through the communication module, and the dynamic monitoring module analyzes a video stream in real time, identifies an abnormal event and generates an alarm. The central scheduling module distributes reconnaissance tasks according to the states of the robots and event levels, the path planning module plans moving paths and hidden observation positions in a target object view blind area in the whole process for the robots based on an examination room map, and dynamic complementary coverage of the monitoring blind area and path space avoidance between the robots are achieved through cooperative calculation. According to the method, deep collaboration of fixed and mobile monitoring resources is realized, the capabilities of task preemption, adaptive weight patrol, random path generation, blind area self-discovery and compensation and cheating evidence automatic fixing and reporting are realized, and the accuracy, the concealment and the automation level of examination room monitoring are remarkably improved.