In accordance with an embodiment, a method of operating a vehicle within a vehicle swarm includes: receiving a sortie specification, the sortie specification specifying a desired behavior the vehicle swarm is to perform; obtaining a position identification within the vehicle swarm; and calculating a set of waypoints based on the received sortie specification and the position identification.

First and second movers can make communication with each other by means of a communication system in which a signal-receiving unit of the first mover receives from a satellite a signal indicative of a position coordinate of itself, a control unit of the first mover makes a pattern indicative of a position coordinate of the first mover, a display unit of the first mover displays the pattern, an image pickup unit of the second mover photographs the pattern, and a control unit of the second mover deciphers the thus photographed pattern to thereby control a position of the second mover in accordance with the thus deciphered pattern.

A method of generating an environmental model of an environment (24) in an industrial plant or logistics plant is provided, wherein a plurality of sensors (18) distributed over the environment (24) detect a respective local partial zone of the environment (24) and the environmental model is assembled therefrom, In this respect, a plurality of autonomous mobile reconnaissance units (12), in particular autonomous mobile robots; move in the environment (24) and at least some of the sensors (18) are part of a mobile reconnaissance unit (12) and thus the environment (24) at changing locations.

Systems and methods for determining the order of autonomous vehicles proceeding through the intersection locally by the autonomous vehicles are disclosed. The method may include determining the presence of overlapping regions between paths of different autonomous vehicles. The method may also include assigning one of the autonomous vehicles as the authority to decide which autonomous vehicle should proceed through the intersection before all other autonomous vehicles.

The present invention provides a system for remote vehicle operation by human pilot and artificial intelligence systems. The system comprises: a vehicle capable of movement, a human operator and control station situated outside of the vehicle in a remote location, a bidirectional wireless communications channel, which transmits commands from the control station to the vehicle and which receives information related to the vehicle's state and its environment from the vehicle, and a human interface device conveying information to the human operator and receiving inputs.

A computerized system configured to execute a multi-agent machine learning model for controlling a plurality of vehicles in a multi-vehicle autonomous control session in a multi-vehicle environment is disclosed. Multi-modal neural network agents of the model each control a corresponding autonomous vehicle in the session. The agents receive image data and parameter data, input the image data to an image feature extractor to produce an image feature vector, input the parameter data to a parameter data feature extractor to produce a parameter data feature vector, produce a joint latent representation of the image data and parameter data, and input the joint latent representation to an actor model neural network, to generate a selected action for the autonomous vehicle. The multi-agent machine learning model is configured to control each autonomous vehicle in the session according to the corresponding selected action for each autonomous vehicle.

A hybrid, redundant, fail-safe architecture provides a unified fail-operational framework for autonomous agents operating across physical and virtual domains. The system employs a multi-modal data source suite, an adaptive hybrid data fusion module, and an intelligent decision-making module. A novel closed-loop interaction enables a health monitoring module that detects an incipient fault in a data source by monitoring ancillary performance metrics. Upon detection, the module generates a fault signature, including a quantitative prognostic estimate of a future failure time, and transmits it to an adaptive data fusion module. The fusion module proactively reconfigures its state estimation algorithm by decreasing reliance on the degrading data source in proportion to the prognostic estimate. This preemptive compensation ensures the system maintains a high-integrity environmental model and achieves true fail-operational continuity. The architecture is applicable to numerous embodiments providing a universal solution for proactive fault management and system resilience.

A control device includes a communication unit, a restriction condition calculation unit, and a control unit. The communication unit receives, from a second robot, a current position of the second robot, and restriction-related information used for controlling the second robot based on positions of a first robot and the second robot at past times. The restriction condition calculation unit calculates restriction condition candidates indicating conditions of a range in which movement is possible, based on the current position and the restriction-related information received from the second robot. The restriction condition calculation unit also identifies a restriction condition having the most recent time among the calculated restriction condition candidates. The control unit controls the position of the first robot so as to move in a manner satisfying the restriction condition identified by the restriction condition calculation unit.

A method may include a first UAV of a plurality of UAVs flying a first vector comprising a first heading and speed; a second UAV of the plurality of UAVs flying a second vector comprising a second heading and speed; at a first time, while the first UAV is flying the first vector and the second UAV is flying the second vector, determining a first distance between the first UAV and the second UAV; at a second time, the second time being after the first time, the second UAV transitioning to flying a third vector comprising a third heading and speed, the third vector being different from the second vector; after the second UAV has transitioned to flying the third vector, the first UAV observing the second UAV; and the first UAV providing a first observation of the second UAV flying the third vector to the second UAV.

An autonomous distributed coordination system for a broad-area search in an unknown environment without the need for a prior plan and map sharing. It includes: a sensor input processing section for acquiring (i) relative position information relative to another mobile body, (ii) path information in past and (iii) surrounding shape information; other mobile body avoidance module for generating, based on the relative position information, a first action candidate for avoiding the another mobile body; a past path avoidance module for generating, based on the path information, a second action candidate for avoiding the past path; an obstruct avoidance module for generating, based on the depth information, a third action candidate for avoiding a surrounding obstruct; and an integration module for determining a velocity or angular velocity of the mobile body based on the first action candidate, the second action candidate and the third action candidate.