The present application relates to a multi-sensor-fusion-based autonomous mobile robot indoor and outdoor positioning method and a robot. The method includes: acquiring GPS information and three-dimensional point cloud data of a robot at a current position; acquiring, a two-dimensional map corresponding to the GPS information of the robot at the current position; projecting the three-dimensional point cloud data of the robot at the current position onto a road surface where the robot is currently moving, to obtain two-dimensional point cloud data of the robot at the current position; and matching the two-dimensional point cloud data of the robot at the current position with the two-dimensional map corresponding to the GPS information of the robot at the current position, to determine the current position of the robot.

An infrastructure is provided for improving the safety of autonomous systems. An autonomous vehicle management system (AVMS) controls one or more autonomous functions or operations performed by a vehicle or machine such that the autonomous operations are performed in a safe manner. The AVMS uses various artificial intelligence (AI) based techniques (e.g., neural networks, reinforcement learning (RL) techniques, etc.) and models as part of its processing. For an inferring data point, for which a prediction is made by AVMS using an AI model, the AVMS checks how statistically similar (or dissimilar) the inferring data point is to the distribution of the training dataset. A score (confidence score) is generated indicative of how similar or dissimilar the inferring data point is to the training dataset. The AVMS uses this confidence score to decide how the prediction made by the AI model is to be used.

A vehicle computing system may identify a scenario in an environment that violates an operating constraint. The vehicle computing system may request remote guidance from a guidance system of a service computing device. The vehicle computing system may receive input from the guidance system including one or more waypoints and/or associated orientations for the vehicle to navigate through the scenario. The vehicle computing system may be configured to validate the input. A validation may include processing the input to determine whether the waypoint(s) and/or orientation(s) associated therewith may cause the vehicle to violate a safety protocol. Based on a determination that the input will not cause the vehicle to violate the safety protocol, the vehicle computing system may control the vehicle according to the input, such as by causing a drive system to operate the vehicle to each waypoint at the associated orientation.

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.

Provided is a conveying vehicle that ensures efficiently travelling while suppressing vehicle slip. A dump truck 100 includes a vehicle body 101 provided with wheels 103 and a vehicle control device 300 and travels on a travel route. The vehicle control device 300 calculates and stores slip limit values at a plurality of positions on the travel route, reads out the slip limit values to calculate at least one of a maximum acceleration and a maximum deceleration of the dump truck 100 at which the wheels 103 is capable of maintaining a grip state against a road surface, and sets a target travel speed at a travel position between the dump truck 100 and a target position according to a target speed at the target position and at least one of the maximum acceleration and the maximum deceleration.

Embodiments of the present disclosure provide a control method of an unmanned vehicle and an unmanned vehicle, which have excellent safety. The control method of the unmanned vehicle includes: detecting vibration information and running attitude information of the unmanned vehicle; according to the vibration information, the running attitude information and a running status of the unmanned vehicle, determining a condition of the unmanned vehicle, wherein the running status of the unmanned vehicle includes a stop status and a driving status; and when the condition of the unmanned vehicle is abnormal, controlling the unmanned vehicle according to an abnormal condition coping strategy.

A robot climbing control method is disclosed. A gravity direction vector in a gravity direction in a camera coordinate system of a robot is obtained. A stair edge of stairs in a scene image is obtained and an edge direction vector of the stair edge in the camera coordinate system is determined. A position parameter of the robot relative to the stairs is determined according to the gravity direction vector and the edge direction vector. Poses of the robot are adjusted according to the position parameter to control the robot to climb the stairs.

A method for specifying a cleaning area to a cleaning robot without an in-built map provides a hand-held mobile device capturing a two-dimensional code label arranged on a top of a cleaning robot parked on a charging base, and obtaining a positional relationship between the mobile device and the cleaning robot through the captured image. The cleaning robot is controlled to enter a cleaning mode under the guidance of the mobile device. With captured images, a user can specify an area within the environment for cleaning, and through a touch display screen can control the cleaning robot to go to the specified cleaning area for cleaning. The mobile device and the cleaning robot employing the method are also disclosed.

The invention simplifies the process of utilizing mmmg or construction trucks to automatically carry ore, dirt, or other matter from one location to another. Transportation of the dirt, ore, or matter is usually performed using trucks with loaders or excavators. The trucks then take the loads and deposit them in piles, which are then used for the next step of the mining or construction process. The invention uses a teach-and-follow process to establish the trajectories that these paths must follow. The present invention describes a system to record and execute trajectories for autonomous mining and construction trucks. This system comprises one or more sensors that can detect road features, a drive-by-wire kit installed onto the truck(s), a user interface that allows the operator to learn trajectories and “replay trajectories”, and a planning algorithm that creates trajectories which take the vehicle from a starting location to an ending location (final destination), while maintaining the vehicle inside of the allowed driving envelope. The invention allows the user to drive the truck along the desired route and have the truck automatically learn the route using features in the environment to localize. In future runs, the truck is able to automatically follow the learned route.

A robot cleaner is provided. The robot cleaner includes a three-dimensional image sensor, an optical sensor, a gyro sensor, and at least one processor configured to control a driving state of the robot cleaner based on image data acquired by the three-dimensional image sensor, optical data acquired by the optical sensor, and angular velocity data acquired by the gyro sensor, wherein the three-dimensional image sensor and the optical sensor are respectively arranged to be tilted by a predetermined tilting angle, and a tilting angle of the three-dimensional image sensor is smaller than a tilting angle of the optical sensor.