A robot control system according to the present embodiment is a robot control system that controls a plurality of mobile robots that can autonomously move in a facility. The robot control system acquires error information indicating that an error has occurred in a first transport robot, acquires transported object information related to a transported object of the first transport robot, determines a second transport robot able to transport the transported object of the first transport robot among the transport robots based on the transported object information and the error information, and moves the second transport robot to a transfer location of the transported object of the first transport robot.

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.

A system for configuring generic maintenance activity scheduling is provided. For example, user input may help define a maintenance process for a plurality of mobile drive units of the physical workspace. The maintenance process may be defined through a configuration of activity templates and input templates. Input may be received to configure maintenance activities and the system may automatically correlate failure rules with the activity template. The system may generate the configurable electronic instructions for the mobile drive units and transmit them with the failure rules, if generated, to one or more mobile drive units of the physical workspace. When the one or more mobile device units receive the configurable electronic instruction, the mobile drive units may operate in accordance with the configurable electronic instruction.

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.

Systems and methods for controlling a swarm of mobile robots are disclosed. In one aspect, the robots cover a domain of interest. Each robot receives a density function indicative of at least one area of importance in the domain of interest, and calculates a velocity vector based on the density function and a displace vector relative to an adjacent robot. Each robot moves to the area of importance according to its velocity vector. In some aspects, the robots together perform a sequence of formations. Each robot mimics a trajectory as part of its performance by switching among a plurality of motion modes. Each robot determines its next motion mode based on a displacement vector relative to an adjacent robot.

A robot control system according to the present embodiment is a robot control system that controls a plurality of mobile robots that can autonomously move in a facility. The robot control system acquires error information indicating that an error has occurred in a first transport robot, acquires transported object information related to a transported object of the first transport robot, determines a second transport robot able to transport the transported object of the first transport robot among the transport robots based on the transported object information and the error information, and moves the second transport robot to a transfer location of the transported object of the first transport robot.

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.

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.

Systems and methods for controlling a swarm of mobile robots are disclosed. In one aspect, the robots cover a domain main of interest. Each robot receives a density function indicative of at least one area of importance in the domain of interest, and calculates a velocity vector based on the density function and a displace vector relative to an adjacent robot. Each robot moves to the area of importance according to its velocity vector. In some aspects, the robots together perform a sequence of formations. Each robot mimics a trajectory as part of its performance by switching among a plurality of motion modes. Each robot determines its next motion mode based on a displacement vector relative to an adjacent robot.

One or more robots that make exploration and path planning decisions in a previously unknown or unmapped environment based on a map and localization data at least partially generated by human transported perception unit(s). One or more robots that make exploration and path planning decisions in a previously unknown or unmapped environment based on current and past position and velocity information of at least one human. Exploration and navigation recommendations presented to a human based on map and localization information at least partially generated by other humans or robots. A system of humans and robots that generates a map of an environment based data provided from at least one human transported sensor, and at least one robot transported sensor. One or more robots that make navigation, exploration and/or path planning decisions responsive to voice commands that are interpreted taking into consideration map and localization data at least partially generated by human transported perception unit A helmet (vest or other) mounted sensor used to generate map and localization data that is used by robots to make exploration and path planning decisions. A helmet (vest or other) mounted sensor used to generate map and localization data that is used by robots to map and localize themselves within the map.