Present implementations can display 3D illuminations using Flying Light Specks (FLS). Each FLS can include a miniature (hundreds of micrometers) sized drone with one or more light sources to generate colors and textures with adjustable brightness. The FLS can be network enabled with a processor and local storage. Synchronized swarms of cooperating FLSs can render static and motion illumination of virtual objects in a pre-specified 3D volume, an FLS display. Present implementations can consider the limited flight time of an FLS on a fully charged battery and the duration of time to charge the FLS battery. Present implementations can accommodate failure of FLS as a norm of operation, rather than an exception. A hardware and software architectures for an FLS-display can compute flight paths of FLSs for illumination. With motion illuminations, one technique can minimize overall distance traveled by the FLSs significantly.

Systems and methods for public transport infrastructure facilitated drone delivery are provided. In example embodiments, a request to deliver a package to a drop-off destination using a drone is received. Public infrastructure information is accessed. A public infrastructure terminal from which the drone delivers the package is identified based on the public infrastructure information. An instruction is communicated to transport the package to the identified public. A drone delivery route from the identified public infrastructure terminal to the drop-off destination is determined based on the public infrastructure information. An instruction to deliver the package using the drone delivery route is communicated to the drone.

A method of training a machine learning, ML, algorithm to control a watercraft is described. The watercraft is a submarine or a submersible submerged in water. The method is implemented, at least in part, by a computer, comprising a processor and a memory, aboard the watercraft. The method comprises: obtaining training data including respective sets of environmental parameters and corresponding actions of a set of communicatively isolated watercraft, including a first watercraft; and training the ML algorithm comprising determining relationships between the respective sets of environmental parameters and the corresponding actions of the watercraft of the set thereof. A method of controlling a watercraft by a trained ML algorithm is also described.

The present application discloses a sweeping method of a swimming pool cleaning robot and a cleaning robot, the method including: acquiring map information about an area to be cleaned; planning a first sweeping path based on the map information, the first sweeping path meeting pre-set cleaning parameter requirements; controlling the cleaning robot to travel and perform a cleaning operation based on the first sweeping path; determining whether the cleaning operation is ended, and if so, controlling the cleaning robot to travel to a missed area so as to perform supplementary sweeping. This application can improve sweeping coverage rate and sweeping efficiency.

An optimized coverage-path planning method for a single unmanned surface mapping vessel (USMV) is implemented with a system including a computer processor executing a computer program loaded in a storage device and implanting the method. The method includes rasterizing and initializing an environmental map, and an unmanned vessel outputting position data and obstacle data according to the environmental map so that path planning is started to provide a target point to the unmanned vessel. In case of tripping in a local optimum at a current-level map for the target point, the map level is updated in an ascending order until the highest level, in order to identify a map level in which the target point is found.

Aspects of the subject disclosure may include, for example, receiving a user selection relating to a course, transmitting a request to a controller in a vehicle for information regarding capabilities of available devices onboard the vehicle, wherein the available devices include uncrewed aerial vehicles (UAVs), based on the transmitting, obtaining, from the controller, the information regarding the capabilities, responsive to the obtaining, sending a command to the controller to facilitate deployment of one or more of the UAVs to collect data for the course, and after the sending, receiving the data from the controller and incorporating the data into the course for delivery to one or more users onboard the vehicle. Other embodiments are disclosed.

A cleaning robot for a swimming pool is provided. A steering determination module is arranged inside the cleaning robot. The steering determination module includes a gyroscope. During a driving process of the cleaning robot: if the gyroscope monitors an increase in instantaneous acceleration, it is determined that the cleaning robot hits a wall, and the cleaning robot steers after a preset period of steering time; if the gyroscope monitors a change in a climbing angle, and the climbing angle exceeds a preset angle, it is determined that the cleaning robot climbs, and the cleaning robot steers after the preset period of steering time; and if the gyroscope monitors that continuous vibration occurs and the continuous vibration lasts over a preset period of vibration time, it is determined that the cleaning robot encounters an obstacle, and the cleaning robot steers after the preset period of steering time.

A system for operating a plurality of autonomous vessels (101). Communication interfaces (108) enable communication between a remote-control center (110) and the respective autonomous vessels (101). A fleet coordination layer (201) implemented in the remote-control center (110) aggregates information relating to operation of the autonomous vessels (101), performs risk assessment, and issues operational mode transition instructions to the autonomous vessels (101) when appropriate. Aa vessel coordination layer (202) is implemented on the autonomous vessels (101) and controls transitions between operational modes of the autonomous vessels (101). A vessel execution layer (203) implemented on the autonomous vessels (101) includes sensors (301), a perception, planning, and execution module (302) and actuators (303), uses the actuators (303) to control the motion of the autonomous vessel (101) in accordance with a mission description, received sensor data, and operational mode instructions from the operational mode management module (402). A corresponding method is also disclosed.

A scene identification method, a control device, a movable platform and a computer-readable storage medium are provided, the method includes: controlling a probing device to emit probing light to detect a current scene; the probing device including an area-array photoelectric sensor; when the area-array photoelectric sensor receives a reflected echo of the probing light, obtaining multiple signals output by multiple photoelectric units of the area-array photoelectric sensor; identifying whether the current scene is a target scene based on signal parameters of the multiple signals, where the target scene at least includes a water surface scene. The scene identification steps are simple, facilitating rapid acquisition of scene identification results.

A cleaning robot (1000), comprising a water-surface body (100) and an underwater body (200) detachably connected to the water-surface body (100). The water-surface body (100) is configured for floating on the surface of a working water area. The underwater body (200) is configured for cleaning at least one of the working water area and the wall surface of a pool. The water-surface body (100) comprises a functional module (2), which comprises at least one of a power supply module (21), a collection module (22), a driving module (23), a communication module (24), a sensor module (25), an alert module (26), a water-quality detection module (27) or a water-quality improvement module (280).