A system for intralogistics comprising a self-propelled load bearing cart (200, 200, 200, 200, 200B) and a remote controlled or autonomous self-propelled guide unit (100, 100). The self-propelled load bearing cart (200, 200, 200, 200, 200B) comprises a drive unit (223) comprising at least one drive wheel (222) for propelling the self-propelled load bearing cart (200, 200, 200, 200, 200B), a mechanical connection (172), and a computing unit (253) connected to the drive unit (223). The computing unit (253) comprises a transceiving unit (253) for communicating with the remote controlled or autonomous self-propelled guide unit (100, 100), and the remote controlled or autonomous self-propelled guide unit (100, 100) comprises a mechanical connection (171) configured to connect to the mechanical connection (172) of the self-propelled load bearing cart (200, 200, 200, 200, 200B), such that a mechanical interconnection can be created between the remote controlled or autonomous self-propelled guide unit (100, 100) and the self-propelled bearing cart (200, 200, 200, 200, 200B).

A remote control device includes, at least one processer configured to perform processes including, acquiring three-dimensional point cloud data measured using a distance measuring device, estimating at least one of a position and an orientation of a moving object in the three-dimensional point cloud data by matching a template point cloud indicating the moving object with the three-dimensional point cloud data, determining a start position to start matching of the template point cloud with the three-dimensional point cloud data; and generating a control command for remotely controlling the moving object using at least one of the estimated position and the estimated orientation of the moving object and transmit the control command to the moving object.

Embodiments of the present disclosure may include a method for optimizing flight of an unmanned aerial vehicle (UAV) including a payload, the method including receiving one or more human-initiated flight instructions. Embodiments may also include determining a UAV context based at least in part on Inertial Measurement Unit (IMU) data from the UAV. Embodiments may also include receiving payload identification data. Embodiments may also include accessing a laden flight profile based at least in part on the payload identification data. Embodiments may also include determining one or more laden flight parameters. In some embodiments, the one or more laden flight parameters may be based at least in part on the one or more human-initiated flight instructions, the UAV context, and the laden flight profile.

A processor included in an information processing apparatus is configured to generate a follow-up command to perform follow-up travel to follow an autonomous vehicle with an autonomous driving function or an unmanned aircraft. The processor is configured to send the follow-up command to a work vehicle with a follow-up travel function.

The method of managing unmanned aerial vehicle (UAV) patrols is a method for organizing and managing a fleet of UAVs for patrolling a geographic region for the detection of threats. During patrols, the UAVs visit two types of waypoints: critical waypoints and strategic waypoints. To establish the strategic waypoints, a set of seed points are generated using a beta probability distribution. Voronoi tessellation is applied to produce the set of strategic waypoints within the region of interest by using the set of seed points as an input. Each of the strategic waypoints has a set of coordinates on the three-dimensional occupancy map associated therewith. The set of coordinates associated with each of the strategic waypoints is a centroid of a corresponding Voronoi cell produced by the Voronoi tessellation. A priority score is assigned to each of the critical waypoints and each of the strategic waypoints.

An embodiment controller for use in a communication system for platooning vehicles that performs Vehicle-to-Vehicle (V2V) communication through controllers mounted in the platooning vehicles includes a communication unit configured to transmit and receive a message including control and sensor information of the platooning vehicles, a scheduler configured to store message transmission times of the platooning vehicles, and a determiner configured to determine whether a message received through the communication unit corresponds to schedule information stored in the scheduler, wherein the controller is configured to transmit a message in accordance with an order determined based on the schedule information and find out a message transmission order of the platooning vehicles by sharing the schedule information.

A method for autonomously operating a following vehicle in a vehicle train together with a vehicle driving ahead of the following vehicle with respect to a direction of travel includes acquiring first route information with a front-mounted sensor. The method also includes operating the following vehicle on the basis of first route information and acquiring second route information with a rear-mounted sensor. The method further includes transmitting the second route information to the following vehicle and, when a substitute criterion is present, operating the following vehicle on the basis of the second route information.

A system for intralogistics comprising a self-propelled load bearing cart and a remote controlled or autonomous self-propelled guide unit. The self-propelled load bearing cart comprises a drive unit comprising at least one drive wheel for propelling the self-propelled load bearing cart, a mechanical connection, and a computing unit connected to the drive unit. The computing unit comprises a transceiving unit for communicating with the remote controlled or autonomous self-propelled guide unit, and the remote controlled or autonomous self-propelled guide unit comprises a mechanical connection configured to connect to the mechanical connection of the self-propelled load bearing cart, such that a mechanical interconnection can be created between the remote controlled or autonomous self-propelled guide unit and the self-propelled load bearing cart.

Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a first subset of the plurality of agents having an anomaly sensor subsystem; determining a set of relative pose vectors based at least in part on the relative distance data; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents; obtaining pre-surveyed map data; determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data; and assigning a task to at least one of the plurality of agents based at least in part on a specialized operational capability of the at least one of the plurality of agents.

A platooning control device includes: a learning device configured to perform reinforcement learning on the basis of image information and a feedback signal and to control a pertinent vehicle so as to follow a traveling trajectory of a front vehicle according to a result of the reinforcement learning; and a compensation determination unit configured to receive a coordinate of a control point regarding the traveling trajectory of the front vehicle from the front vehicle and to compare a coordinate of the pertinent vehicle with the coordinate of the control point, thereby generating the feedback signal.