Systems and techniques are provided for management of autonomous cargo by autonomous vehicles (AVs). An example method can include determining, based on data from one or more sensors, a location for deploying a ramp that enables robots to enter the AV, the location comprising an area free of obstacles having one or more dimensions above a threshold; generating an instruction configured to trigger the AV to stop at the location; based on a determination that the AV is at the stopping position, deploying the ramp; sending, to the robots, a message instructing the robots to enter a cabin of the AV via the ramp and guiding each robot to a respective location within the cabin; and based on a determination that the AV has reached a destination of one or more robots, deploying the ramp and guiding the one or more robots to exit the cabin via the ramp.

The provided is a data-driven bipartite consensus control method for multi-robot collaborative rotation of a large workpiece. The method includes: setting a multi-robot system; constructing and discretizing a dynamics model of a follower robot, and constructing a lateral error based on position information between the follower and leader robots; constructing an unknown nonlinear function with the lateral error and a control input as variables, and constructing a lateral error data model of the follower robot through a dynamic linearization technique; designing, based on a topological relationship of the multi-robot system and the lateral error, a bipartite consensus error; substituting the data model into a designed objective function to solve a data-driven bipartite consensus controller; designing a parameter estimation algorithm to estimate an unknown parameter in the controller; allowing an estimated value to participate in a controller update; and calculating a front wheel steering angle control signal.

Disclosed herein are systems and methods for path planning for UAVs. A set of UAVs is logically arranged into a plurality of swarms, each having a swarm-leader UAV. A first path planning algorithm is applied to determine navigation instructions to steer the swarm-leader UAVs towards respective destinations for the swarms and to deconflict the swarm-leader UAVs from one another. A set of second path planning algorithms is applied to determine navigation instructions to steer non-swarm-leader UAVs in each swarm toward their respective swarm leaders and to deconflict the UAVs from other UAVs in the swarm. Separate QUBO path planning algorithms may be used for the first path planning algorithm and the set of second path planning algorithms. If merging criteria for combining two swarms are met, a single QUBO may be used to control all non-swarm-leader UAVs in merged swarms.

An unmanned ground-based transport vehicle, UGV, includes a housing, having a base plate and at least one housing side wall substantially perpendicular to the base plate. Arranged in the housing is at least one wheel drive, which is coupled to at least one wheel. The wheel is arranged in a recess in the base plate. The UGV further includes sensors for sensing the environment of the UGV, and a controller for autonomous location and navigation of the UGV on the basis of sensing parameters of the sensors. The UGV includes at least one load-receiving element coupled to the housing side wall and extending outwards from the housing side wall, wherein the load-receiving element includes a load support surface for supporting an item with respect to a vertical direction which extends transverse to the base plate.

A platooning control apparatus may include: a navigation unit configured to guide an ego vehicle to a destination set by a driver; a driving module configured to drive the ego vehicle; and a control unit configured to primarily select platooning groups based on the destination set in the navigation unit, analyze platooning information of the primarily selected platooning groups, finally decide any one of the primarily selected platooning groups, and then control the driving module to join the finally decided platooning group.

Embodiments provide systems, method, and computer-readable storage media for performing robotic tasks in a distributed and coordinated manner. A fleet of robots may use a sequence of messages to appoint supervisors for a set of tasks, where the supervisor robots are responsible for ensuring that their supervised tasks are completed by other robots of the fleet. The supervisors may solicit requests from other robots to perform available tasks and select a robot to perform an available task. Once a robot is appointed, the supervisor and the worker may use messaging sequences to monitor the status of the task and participating robots (e.g., the supervisor and the worker). The monitoring may enable the supervisor to detect a failed worker and enable other robots to detect a failed supervisor. When failed robots are detected, the robot(s) detecting the failure may initiate operations to take over the role(s) of the failed robots to ensure operation of the fleet of robots continues in a smooth manner and the tasks are completed efficiently.

The provided is a data-driven bipartite consensus control method for multi-robot collaborative rotation of a large workpiece. The method includes: setting a multi-robot system; constructing and discretizing a dynamics model of a follower robot, and constructing a lateral error based on position information between the follower and leader robots; constructing an unknown nonlinear function with the lateral error and a control input as variables, and constructing a lateral error data model of the follower robot through a dynamic linearization technique; designing, based on a topological relationship of the multi-robot system and the lateral error, a bipartite consensus error; substituting the data model into a designed objective function to solve a data-driven bipartite consensus controller; designing a parameter estimation algorithm to estimate an unknown parameter in the controller; allowing an estimated value to participate in a controller update; and calculating a front wheel steering angle control signal.

A vehicle control system that makes it possible to ensure the safety and enhance the productivity at the same time is provided. An unmanned dump 10 receives positional information about a manned vehicle 20 by using infrastructure-to-vehicle communication 520 and infrastructure-to-infrastructure communication 510. In a case where an inter-vehicle distance X between the unmanned dump 10 and the manned vehicle 20 is equal to or shorter than a reference distance Y, the unmanned dump 10 decides whether or not vehicle-to-vehicle communication 550 is established between the unmanned dump 10 and the manned vehicle 20. In a case where it is decided that the vehicle-to-vehicle communication 550 is established, the upper limit of the travel speed of the unmanned dump 10 is set to a first speed V1, and in a case where it is decided that the vehicle-to-vehicle communication 550 is not established, the upper limit of the travel speed of the unmanned dump 10 is set to a second speed V2.

A method of operating a vehicle convoy that includes a leader vehicle generating and transmitting a control command for and to at least one follower vehicle that includes a current timestamp, a drive action to be performed by the at least one follower vehicle and a spatial and/or temporal condition to be fulfilled prior to carrying out the drive action, the drive action including at least one of steering the at least one follower vehicle, changing the speed of the at least one follower vehicle and setting the speed of the at least one follower vehicle, the at least one follower vehicle receiving the control command from the leader vehicle and carrying out the drive action included in the received control command when the spatial and/or temporal condition included in the received control command is fulfilled.

A remote control system for operating vehicles includes a first vehicle, a second vehicle including one or more controllable elements, and one or more processing circuits. The one or more processing circuits are configured to acquire, from the first vehicle, input data corresponding to the second vehicle, generate, based on the input data, control signals for the one or more controllable elements of the second vehicle, and provide the control signals to the one or more controllable elements of the second vehicle to operate the one or more controllable elements according to the input data.