Disclosed are a method, an apparatus and a non-transitory storage medium. The method includes: dividing satellites of the mega-constellation into satellite topology groups, so as to construct corresponding space-time grids, obtaining a dynamic matching relationship in time domain between the space-time grids and the satellite topology groups, setting a path weight for each space-time grid, acquiring a static grid path for forwarding a mission, where the static grid path is determined by an order of the space-time grids that need to be passed sequentially to forward the mission, adjusting the static grid path according to the satellite node currently receiving the mission and the dynamic matching relationship, so as to acquire the next satellite node to which the mission is forwarded from the satellite node currently receiving the mission.

Disclosed are a method, an apparatus and a non-transitory storage medium. The method includes: dividing satellites of the mega-constellation into satellite topology groups, so as to construct corresponding space-time grids, obtaining a dynamic matching relationship in time domain between the space-time grids and the satellite topology groups, setting a path weight for each space-time grid, acquiring a static grid path for forwarding a mission, where the static grid path is determined by an order of the space-time grids that need to be passed sequentially to forward the mission, adjusting the static grid path according to the satellite node currently receiving the mission and the dynamic matching relationship, so as to acquire the next satellite node to which the mission is forwarded from the satellite node currently receiving the mission.

Examples described herein are directed to systems and methods for autonomous vehicle control system testing. A testing utility may receive log data describing a first location of a capturing vehicle at a first driven route time and a second location of the capturing vehicle at a second drive route time. The testing utility may, using at least the location data, determine a first routing graph modification to constrain a navigator component of an autonomous vehicle control system under test. The testing utility may provide the first routing graph modification to the navigator component to cause the navigator component to generate a testing route.

Disclosed are systems and methods for monitoring operations of autonomous vehicles within a warehouse. The autonomous vehicle's processing unit may detect when the autonomous vehicle is delayed based on one or more timers or time thresholds, such as an amount of time that the autonomous vehicle has been located at a particular location or the amount of time between when the autonomous vehicle received product picking instructions and a shipping deadline. The autonomous vehicle may also use various types of data to detect potential problems, such as determining an amount remaining battery life. Once the autonomous vehicle identified a particular triggering condition, then the autonomous vehicle may trigger an annunciator (e.g., flashing lights, sounding horn) or transmit a notification to other devices, such as client devices. The annunciators or notifications attempt to capture personnel attention to resolve the identifier problem.

The disclosed technology enables automated parking of an autonomous vehicle. An example method of performing automated parking for a vehicle comprises obtaining, from a plurality of global positioning system (GPS) devices located on or in an autonomous vehicle, a first set of location information that describes locations of multiple points on the autonomous vehicle, where the first set of location information are associated with a first position of the autonomous vehicle, determining, based on the first set of location information and a location of the parking area, a trajectory information that describes a trajectory for the autonomous vehicle to be driven from the first position of the autonomous vehicle to a parking area, and causing the autonomous vehicle to be driven along the trajectory to the parking area by causing operation of one or more devices located in the autonomous vehicle based on at least the trajectory information.

Disclosed in the embodiments of the present disclosure are a navigation method of a robot, a chip and a robot. In the navigation method, when the position of the robot is not communicated with a preset known grid area constructed before relocalization, and grids allowing the robot to pass are marked within a detectable distance of a sensor of the robot, so as to plan a navigation path for enabling the robot to actually walk into the preset known grid area.

Disclosed in the embodiments of the present disclosure are a navigation method of a robot, a chip and a robot. In the navigation method, when the position of the robot is not communicated with a preset known grid area constructed before relocalization, and grids allowing the robot to pass are marked within a detectable distance of a sensor of the robot, so as to plan a navigation path for enabling the robot to actually walk into the preset known grid area.

Broadly speaking, embodiments of the present techniques provide methods and systems for robot navigation in an unknown environment. In particular, the present techniques provide a navigation system comprising a navigating device and a sensor network comprising a plurality of static sensors. The sensor network is trained to predict a direction to a target object, and the navigating device is trained to reach the target object as efficiently as possible using information obtained from the sensor network.

Broadly speaking, embodiments of the present techniques provide methods and systems for robot navigation in an unknown environment. In particular, the present techniques provide a navigation system comprising a navigating device and a sensor network comprising a plurality of static sensors. The sensor network is trained to predict a direction to a target object, and the navigating device is trained to reach the target object as efficiently as possible using information obtained from the sensor network.

Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer implemented method includes obtaining, by one or more computing devices on-board an autonomous vehicle, data associated with one or more objects that are proximate to the autonomous vehicle. The data includes a predicted path of each respective object. The method includes identifying at least one object as an object of interest based at least in part on the data associated with the object of interest. The method includes generating cost data associated with the object of interest. The method includes determining a motion plan for the autonomous vehicle based at least in part on the cost data associated with the object of interest. The method includes providing data indicative of the motion plan to one or more vehicle control systems to implement the motion plan for the autonomous vehicle.