Methods, systems, and apparatus, including computer-readable storage devices, for robot navigation using 2D and 3D path planning. In the disclosed method, a robot accesses map data indicating two-dimensional layout of objects in a space and evaluates candidate paths for the robot to traverse. In response to determining that the candidate paths do not include a collision-free path across the space for a two-dimensional profile of the robot, the robot evaluates a three-dimensional shape of the robot with respect to a three-dimensional shape of an object in the space. Based on the evaluation of the three-dimensional shapes, the robot determines a collision-free path to traverse through the space.

A control system, method and computer program product cooperate to assist control for an autonomous robot. The system includes a communications interface that exchanges information with the autonomous robot. A user interface displays a scene of a location in which the autonomous robot is positioned, and also receives an indication of a user selection of a user selected area within the scene. The communications interface transmits an indication of said user selected area to the autonomous robot for further processing of the area by said autonomous

A robot step control method, a robot control apparatus, and a storage medium are provided. The method includes: determining an expected support force of two legs of a biped robot according to zero-moment point planning data and actual position data of the two legs at a current moment, and determining a current desired joint posture angle of ankle joints of the two legs and a desired joint position matching an actual leg support state using a compliance control algorithm based on an expected support force of the two legs, and centroid movement planning data, centroid actual movement data, step planning data and actual force data of the two legs at the current moment. In such manner, all-direction compliant controls can be performed on a desired leg pose condition according to the actual motion status of the biped robot, thereby improving the walking stability and terrain adaptability of the biped robot.

The present disclosure provides a stair climbing gait planning method and an apparatus and a robot using the same. The method includes: obtaining first visual measurement data through a visual sensor of the robot; converting the first visual measurement data to second visual measurement data; and performing a staged gait planning on a process of the robot to climb the staircase based on the second visual measurement data. Through the method, the visual measurement data is used as a reference to perform the staged gait planning on the process of the robot to climb the staircase, which greatly improves the adaptability of the robot in the complex scene of stair climbing.

A foothold position control system and method for a biped robot are provided. 1) A feasible collision-free path is planned by using a path planning algorithm; 2) an available foothold area of a swing foot is determined according to step-length constraints, movement capabilities, foot sizes, and center offsets of a biped robot; and 3) fuzzy processing is performed to determine a specific foothold position of the biped robot. Selection of suitable foothold positions on both sides of a path when a biped robot executes specific walking actions after finishing path planning is realized. The foothold position control system and method has the advantages of being simple and easy to implement, having low computational load and high speed, being capable of exerting extreme movement capabilities of different biped robots, enabling more flexible movement of the biped robots, and so on.

A method for negotiating stairs includes receiving image data about a robot maneuvering in an environment with stairs. Here, the robot includes two or more legs. Prior to the robot traversing the stairs, for each stair, the method further includes determining a corresponding step region based on the received image data. The step region identifies a safe placement area on a corresponding stair for a distal end of a corresponding swing leg of the robot. Also prior to the robot traversing the stairs, the method includes shifting a weight distribution of the robot towards a front portion of the robot. When the robot traverses the stairs, the method further includes, for each stair, moving the distal end of the corresponding swing leg of the robot to a target step location where the target step location is within the corresponding step region of the stair.

Systems and methods for determining movement of a robot about an environment are provided. A computing system of the robot (i) receives information including a navigation target for the robot and a kinematic state of the robot; (ii) determines, based on the information and a trajectory target for the robot, a retargeted trajectory for the robot; (iii) determines, based on the retargeted trajectory, a centroidal trajectory for the robot and a kinematic trajectory for the robot consistent with the centroidal trajectory; and (iv) determines, based on the centroidal trajectory and the kinematic trajectory, a set of vectors having a vector for each of one or more joints of the robot.

The embodiment of the present disclosure provides a robot control method, a robot and a storage medium. In the embodiment of the present disclosure, the robot determines a position when the robot is released from being hijacked based on relocalization operation; determines a task execution area according to environmental information around the position when the robot is released from being hijacked; and afterwards executes a task within the task execution area. Thus, the robot may flexibly determine the task execution area according to the environment in which the robot is released from being hijacked, without returning to the position when the robot is hijacked, to continue to execute the task, then acting according to local conditions is realized and the user requirements may be met as much as possible.

Systems, computer program products, and methods for constructing models and simulations of real-world environments are described. A robot employs various sensors to collect data from its environment and provides this data to a tele-operation system. Any number of tele-artists may access the tele-operation system and use the robot sensor data to collaboratively construct a simulated scene representative of the robot's environment. The tele-artists may continue to update the simulation in real-time as the robot explores its environment and provides more sensor data. The robot may use the simulation in support of fundamental operations through its cognitive architecture, such as action planning and hypothesis generation.

An artificial intelligence controller of the robot may monitor the adaptations made to the simulation by the tele-artists in response to the sensor data in order to learn (e.g., via reinforcement learning) how to autonomously generate and update its own simulation based on its own sensor data.

The disclosure relates to a method for predicting and controlling robot walking. The method includes the following steps: constructing a grid map based on grid units marked with a status; establishing a dynamic detection model with a current location of a robot as a reference point based on the grid map; predicting a forward path condition of the robot based on the dynamic detection model; and controlling a walking mode of the robot based on the prediction result.