A mobile object control device includes a first controller that recognizes a surrounding situation of a mobile object based on an output of a detection device having a space around the mobile object as a detection range and generates a first movement plan for the mobile object in a first period based on the recognized surrounding situation of the mobile object, and a second controller that generates a second movement plan for the mobile object in a second period shorter than the first period, and when the second controller generates label data in which label information indicating different values depending on at least the presence or absence of a moving object is imparted to each of division elements obtained by dividing the space around the mobile object into a finite number, and generates the second movement plan based on the label data.

A mobile object control device includes a first controller that recognizes a surrounding situation of a mobile object based on an output of a detection device having a space around the mobile object as a detection range and generates a first movement plan for the mobile object in a first period based on the recognized surrounding situation of the mobile object, and a second controller that generates a second movement plan for the mobile object in a second period shorter than the first period, and when the second controller generates label data in which label information indicating different values depending on at least the presence or absence of a moving object is imparted to each of division elements obtained by dividing the space around the mobile object into a finite number, and generates the second movement plan based on the label data.

Systems, devices, and methods for scheduling and controlling a mobile cleaning robot based on a seasonal or environmental debris accumulation event are discussed. A mobile cleaning robot receives a seasonal cleaning schedule corresponding to a seasonal or environmental debris accumulation event. The seasonal cleaning schedule includes instructions to clean a portion of the mobile robot's environment having a debris state varied seasonally. The mobile cleaning robot executes a cleaning mission in the environment in accordance with the seasonal cleaning schedule.

Consciousness is widely considered to be a mysterious and uniquely human trait, which cannot be achieved artificially. On the contrary, a system and method are disclosed for a computational machine that can recognize itself and other agents in a dynamic environment, in a way that seems quite similar to biological consciousness in humans and animals. The machine comprises an artificial neural network configured to identify correlated temporal patterns and attribute causality and agency. The machine is further configured to construct a virtual reality environment of agents and objects based on sensor inputs, to create a coherent narrative, and to select future actions to pursue goals. Such a machine may have application to enhanced decision-making in autonomous vehicles, robotic agents, and intelligent digital assistants.

Techniques are provided which may be implemented using various methods and/or apparatuses in a vehicle to utilize vehicle external sensor data, vehicle internal sensor data, vehicle capabilities and external V2X input to determine, send, receive and utilize V2X information and control data, sent between the vehicle and a road side unit (RSU) to determine intersection access and vehicle behavior when approaching the intersection.

Spatiotemporal robotic navigation may include providing a set of robots non-conflicting access to the same shared resources at different times so that the robots may operate without continually accounting for the locations of the other robots and workers operating in the particular site, without continually planning or updating paths after determining an initial path, and without continuously adjusting movements as the robots near one another. The spatiotemporal robotic navigation involves generating spatiotemporal plans. Each plan has a set of objectives that a robot is to execute by different time intervals. Each plan is generated so as to not conflict with the resources being accessed by other robots at time intervals set in the plans of other robots.

The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting locations in an environment of a vehicle where objects are likely centered and determining properties of those objects. One of the methods includes receiving an input characterizing an environment external to a vehicle. For each of a plurality of locations in the environment, a respective first object score that represents a likelihood that a center of an object is located at the location is determined. Based on the first object scores, one or more locations from the plurality of locations are selected as locations in the environment at which respective objects are likely centered. Object properties of the objects that are likely centered at the selected locations are also determined.

A pool cleaning system for cleaning debris from a submerged surface of a swimming pool includes a self-propelled pool cleaner having rotatably-mounted supports for supporting and guiding the cleaner on the pool surface; an electric motor for enabling the rotation of the rotatably-mounted supports on the pool surface; at least one camera to capture imagery of the pool surface; a controller, in electronic communication with the at least one camera, to determine a cleanliness characteristic of the pool surface on which the cleaner has passed based on the camera imagery and generate a control signal to direct movement of the cleaner based on the cleanliness characteristic of the pool surface, and a portable electronic device configured to present a graphic on a display, the graphic depicting the submerged surface of the pool and those portions of the surface that remain uncleaned as the cleaner traverses the pool surface.

Consciousness is widely considered to be a mysterious and uniquely human trait, which cannot be achieved artificially. On the contrary, a system and method are disclosed for a computational machine that can recognize itself and other agents in a dynamic environment, in a way that seems quite similar to biological consciousness in humans and animals. The machine comprises an artificial neural network configured to identify correlated temporal patterns and attribute causality and agency. The machine is further configured to construct a virtual reality environment of agents and objects based on sensor inputs, to create a coherent narrative, and to select future actions to pursue goals. Such a machine may have application to enhanced decision-making in autonomous vehicles, robotic agents, and intelligent digital assistants.