The invention discloses a cleaning partition planning method for robot walking along the boundary, a chip and a robot. According to the cleaning partition planning method, a complete global map does not needed to be prestored in advance, but an initial room cleaning partition of the robot is divided in real time in a predefined cleaning area according to map image pixel information obtained by laser scanning in the process of walking along the boundary, meanwhile, the initial room cleaning partition of the robot is expanded by repeated iterative processing of the wall boundary of an uncleaned area in the same predefined cleaning area.

The invention discloses a cleaning partition planning method for robot walking along the boundary, a chip and a robot. According to the cleaning partition planning method, a complete global map does not needed to be prestored in advance, but an initial room cleaning partition of the robot is divided in real time in a predefined cleaning area according to map image pixel information obtained by laser scanning in the process of walking along the boundary, meanwhile, the initial room cleaning partition of the robot is expanded by repeated iterative processing of the wall boundary of an uncleaned area in the same predefined cleaning area.

A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

A computing system that analyzes the network effects of avoidance areas on autonomous vehicle routing is described herein. The computing system includes a data store that comprises a set of avoidance areas through which the autonomous vehicle is prohibited from being routed. A grouping system identifies groups of avoidance areas. A graph construction system constructs a graph representation of the avoidance area groups. A ranking algorithm is evaluated over the graph representation to generate a ranking of the avoidance area groups by relative impact on routing metrics for routes through an operational area of the autonomous vehicle. A mapping vehicle can be dispatched to resolve avoidance areas in avoidance area groups indicating in the ranking as having a greater impact on routing metrics than other avoidance area groups.

Methods, apparatus, systems and articles of manufacture are disclosed to generate a path plan. An example method includes generating guidance lines for a next path of a vehicle based on an edge of a current path, generating a first path plan of a field within a field boundary, the first path plan generated using a first degree heading, generating a second path plan of the field within the field boundary, the second path plan generated using a second degree heading, and when the first path plan includes a first cost lower than a second cost of the second path plan, transmitting the first path plan to a vehicle to control the vehicle.

Methods, apparatus, systems and articles of manufacture are disclosed to generate a path plan. An example method includes generating guidance lines for a next path of a vehicle based on an edge of a current path, generating a first path plan of a field within a field boundary, the first path plan generated using a first degree heading, generating a second path plan of the field within the field boundary, the second path plan generated using a second degree heading, and when the first path plan includes a first cost lower than a second cost of the second path plan, transmitting the first path plan to a vehicle to control the vehicle.

Spin controller that can enable an autonomous vehicle (AV) to spin in place. A model predictive controller (MPC) can trigger the spin controller any time the MPC determines that a spin in place is required. In some configurations, the spin controller can move the AV to within 5 of the destination point. Spin controller can determine spin method based on the configuration of the AV, calculate an optimum turning path based at least on device mode and obstacles, and can enable the AV to spin in place.

Spin controller that can enable an autonomous vehicle (AV) to spin in place. A model predictive controller (MPC) can trigger the spin controller any time the MPC determines that a spin in place is required. In some configurations, the spin controller can move the AV to within 5 of the destination point. Spin controller can determine spin method based on the configuration of the AV, calculate an optimum turning path based at least on device mode and obstacles, and can enable the AV to spin in place.

The present disclosure is directed to performing one or more validity checks on potential trajectories for a device, such as an autonomous vehicle, to navigate. In some examples, a potential trajectory may be validated based on whether it is consistent with a current trajectory the vehicle is navigating such that the potential and current trajectories are not too different, whether the vehicle can feasibly or kinematically navigate to the potential trajectory from a current state, whether the potential trajectory was punctual or received within a time period of a prior trajectory, and/or whether the potential trajectory passes a staleness check, such that it was created within a certain time period. In some examples, determining whether a potential trajectory is feasibly may include updating a set of feasibility limits based on one or more operational characteristics of statuses of subsystems of the vehicle.

Robot-assisted package delivery with integrated curbside parking monitoring and curb operations optimization is disclosed herein. An example method includes dispatching a delivery vehicle and delivery robot to a delivery location, the delivery location including a parking location for the delivery vehicle that allows for deployment of the delivery robot on a delivery mission, determining occupancy of the parking location, the delivery vehicle parking at the parking location when the parking location is unoccupied, the delivery robot being deployed upon parking of the delivery vehicle, and instructing the delivery vehicle to remain parked during the delivery mission or to leave the parking location and return at later point in time based on an estimated time of arrival of the delivery robot after the delivery mission.