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.