An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

Provided is a data processing unit that analyzes detection information of a visual sensor and determines a movement route of the moving apparatus, and the data processing unit generates traveling surface shape data such as three-dimensional point cloud data that enables analysis of a shape of a traveling surface of the moving apparatus. The data processing unit selects a generation target region of the traveling surface shape data on the basis of the target movement route information of the moving apparatus and a predetermined search range region, generates the traveling surface shape data in the selection region selected, and determines a movement route such as a foot placement position of the moving apparatus with reference to the generated traveling surface shape data.

Approaches for environment reconstruction and path planning for autonomous machine systems and applications are described. An iterative volumetric mapping function for an ego-machine may compute a distance field, and from the distance field derive a cost map representing a volumetric reconstruction of the physical environment around the ego-machine. The cost map may be used for collision avoidance and path planning. The iterative volumetric mapping function may also optionally compute a color integration map and visualization mesh from the distance field that can be used for visualization of the physical environment around the ego-machine. The cost map may be computed as a Euclidean Signed Distance Field (ESDF) and the distance field from which the cost map is computed may include a Truncated Signed Distance Field (TSDF). The distance field, cost map, color integration map and visualization mesh may each be stored in memory as maps of a plurality of map layers.

A system for the securing of a load handling environment of load handling kinematics (30) in a changing working environment includes an environment sensing unit, which is designed to acquire data of the load handling environment and an environment monitoring unit that is in an operational connection with the environment sensing unit. The environment monitoring unit is designed to analyze the data so that an open space (7) surrounding a load to be handled, a work space (12, 13, 14, 60) defined by a movement space of the load handling kinematics (30) and a process space (40, 50) is determined by addition of the work space (12, 13, 14, 60) and a distance space. The environment monitoring unit is configured to at least partly monitor the distance space and/or the process space (40, 50).

Systems and methods described herein relate to detecting and tracking objects. In one embodiment, a system extracts first features from time-sequential perceptual sensor data to generate a first set of bird's-eye-view (BEV) feature images. The system also extracts second features from the first set of BEV feature images using a three-dimensional (3D) detection backbone to generate a second set of BEV feature images. The system also consumes the second set of BEV feature images using a neural-network 3D detection head that is trained with a similarity objective to support an object tracker for use in one of (1) controlling an autonomous robot and (2) generating automatically labeled perception data to train one or more of an online perception model, an online prediction model, and an online planning model used to control an autonomous robot.

A system for housing a drone for locating a source in an electrical structure includes a plurality of drones capable of hovering in positions to form a virtual enclosure around an electrical structure and a server communicably coupled to the drones. The virtual enclosure is divided into a plurality of cells. The drone is configured to measure a plurality of time difference of arrival (TDOA) values from signals originating from the source; calculate a plurality of propagation times comprising a propagation time for a calibration signal that travels from a drone to each of the plurality of cells; and send the TDOA values and the propagation times to a server. The server is configured to receive the TDOA values and the propagation times from the plurality of drones; and determine a location of the source based on the plurality of TDOA values and the plurality of propagation times.

The disclosure relates generally to methods and systems for optimized assignment of traversal tasks under imperfect sensing of autonomous vehicles. Most of the techniques assumes perfect equipment conditions. With the imperfect sensing, most of the optimized assignment and scheduling algorithm may not be effective during actual execution of the tasks. The present disclosure solves the technical problems in the art by providing an analytical model which estimates the basic performance metrics such as an expected travel duration and safety estimation such as collision probability on its path, under imperfect sensing, for optimal assignment of the tasks. An analytical model is integrated with a performance estimator as implemented by the systems of the present disclosure, which tracks, predicts, and alerts on any major deviations from its intended performance of safety parameters.

The apparatus for detecting and removing a dynamic obstacle of a robot and the operating method thereof according to a predetermined exemplary embodiment detect and remove the dynamic obstacle while simultaneously performing the mapping and the localizing using the simultaneous localization and mapping (SLAM) technique to efficiently detect and remove a dynamic obstacle even in a situation in which a dynamic change of surrounding environment is severe and an environment to be localized is large.

The present disclosure relates to a system that comprises a lab space housing multiple workstations comprising at least two workstations each performing a different type of bio lab task from another. The lab space can have a lab floor space comprising an occupied lab floor space on which the multiple workstations are occupied, and an unoccupied lab floor space on which a stand-alone robotic arm moves through.

A system and/or a method for controlling the movement of a vehicle in a constrained environment subject to a disturbed vehicle model including uncertainty on the dynamics governing the movement of the vehicle, collects a feedback signal indicative of a state of the vehicle and a setpoint for controlling the vehicle according to a task and determine a robust invariant set centered on the setpoint for the operation of the vehicle in an unconstrained environment using the disturbed vehicle model. The robust invariant set is inflated equally in all directions until a termination condition defined by the constraint environment is met to produce a safe invariant set enabling control of the operation of the vehicle according to the task while maintaining the state of the vehicle within the safe invariant set.