A method for operating a robotic agent swarm system. The method includes: sending, via a communication network, a reconfiguration instruction from an orchestration controller to a number of robotic luminaire agents, each of the robotic luminaire agents of the swarm system being held at least periodically against an architectural surface comprising a holonomic operational area by a suspension and having a light source configured to illuminate a region in a proximity of the architectural surface. The method also includes changing one or more operating conditions of one or more of the robotic luminaire agents in response to the reconfiguration instruction, including holonomically moving at least one of the robotic luminaire agents from a first position on the holonomic operational area to a second position on the holonomic operational area.

An apparatus for remotely controlling field robots, includes: an interface unit; a work command generator generating a work command signal for operating field robots; an autonomous command generator which generates an autonomous operation command signal for controlling an operation of a second field robot when a user selects a following mode and the work command generator generates a work command signal for a first field robot to correspond to the following mode, or generates an autonomous operation command signal for controlling operations of the first field robot and the second field robot in order to operate an object of work when the user selects an object mode and the work command generator generates a work command signal for the object of work to correspond to the object mode; and a communication unit transmitting the generated autonomous operation command signal to the first field robot and the second field robots.

Robotic applications are important in both indoor and outdoor environments. Establishing reliable end-to-end communication among robots in such environments are inevitable. Many real-time challenges in robotic communications are mainly due to the dynamic movement of robots, battery constraints, absence of Global Position System (GPS), etc. Systems and methods of the present disclosure provide assisted link prediction (ALP) protocol for communication between robots that resolves real-time challenges link ambiguity, prediction accuracy, improving Packet Reception Ratio (PRR) and reducing energy consumption in-terms of lesser retransmissions by computing link matrix between robots and determining status of a Collaborative Robotic based Link Prediction (CRLP) link prediction based on a comparison of link matrix value with a predefined covariance link matrix threshold. Based on determined status, robots either transmit or receive packet, and the predefined covariance link matrix threshold is dynamically updated. If the link to be predicted is unavailable, the system resolves ambiguity thereby enabling communication between robots.

This disclosure relates generally to autonomous devices, and more particularly to method and system to optimally allocate warehouse procurement tasks to distributed autonomous devices. The method includes obtaining, at a coordinating agent, a global task associated with the warehouse and information associated with the robotic agents. The information includes a count and status of the robotic agents. The global task is profiled to obtain a set of sub-tasks and constraints associated with the set of sub-tasks are identified. The constraints include utilization constraint and/or pricing constraints. A distributed, decentralized optimal task allocation is performed amongst the robotic agents based on constraints to obtain optimal performance of robotic agents. The distributed optimal task allocation includes performing primal or dual decomposition of the set of sub-tasks by each robotic agent and updating corresponding primal/dual variables by the coordinating agent when the optimization is performed based on utilization constraint and pricing constraints, respectively.

A system and method for intermodal container transport that utilizes swarm intelligence and the autonomous locating, lifting, supporting and moving via robots working in conjunction. A port central command locates and releases robots to the container location and then transports the container to its destination.

A system for localizing a swarm of robotic platforms utilizing ranging sensors. The swarm is localized by purposely leaving some of the platforms of the swarm stationary, providing localization to the moving ones. The platforms in the swarm can alternate between a stationary and moving state.

A system for localizing a swarm of robotic platforms utilizing ranging sensors. The swarm is localized by purposely leaving some of the platforms of the swarm stationary, providing localization to the moving ones. The platforms in the swarm can alternate between a stationary and moving state.

A method performed by a network node for handling one or more operations in a communications network comprising a plurality of computing devices performing one or more tasks. The network node obtains initial parameters relating to the plurality of computing devices, environment and the communications network; and generates a plan by taking one or more operation goals involving the plurality of computing devices into account as well as the obtained initial parameters, wherein the generated plan relates to operation of the plurality of computing devices. The network node further computes a number of back-up plans, wherein the number of back-up plans are taking one or more events into account wherein the one or more events relate to operation of the plurality of computing devices; and executes one or more operations using the generated plan, and in case the one or more events occur, using a computed back-up plan.

A swarm autonomy system and swarm autonomy method for organizing multiple industrial robots to carry out a number of manufacturing tasks comprises a swarm core and at least one swarm fleet, wherein, the swarm core is configured to manage the swarm autonomy system and generate a swarm plan; and the swarm fleet is configured to execute a manufacturing execution according to the swarm plan.

Teams of robots can be organized to collectively complete complex real-world tasks, for example collective foraging in which robots search for, pick up, and drop off targets in a collection zone. A dynamic multiple-place foraging algorithm (MPFAdynamic) is a scalable, flexible, and efficient algorithm for robot swarms to collect objects in unmapped environments. It achieves scalability through a decentralized architecture in which robots search without central control, and then return to mobile depots which provide collection and communication points. Mobile depots move closer to clusters of targets as robots discover them, which reduces robot transport time as well as collisions among robots. Flexibility is achieved by incorporating individual robot behaviors in which robots move and communicate in ways that mimic the foraging behaviors of ants. The MPFAdynamic algorithm demonstrates that dispersed agents that dynamically adapt to local information in their environment provide more flexible and scalable swarms.