Abstract: Solutions for multi-robot configurations are co-optimized, to at least some degree, across a set of non-homogenous parameters based on a given set of tasks to be performed by robots in a multi-robot operational environment. Non-homogenous parameters may include two or more of: the respective base position and orientation of the robots, an allocation of tasks to respective robots, respective target sequences and/or trajectories for the robots. Such may be executed pre-runtime. Output may include for each robot: workcell layout, an ordered list or vector of targets, optionally dwell time durations at respective targets, and paths or trajectories between each pair of consecutive targets. Output may provide a complete, executable, solution to the problem, which in the absence of variability in timing, can be used to control the robots without any modification. A genetic algorithm, e.g., Differential Evolution, may optionally be used in generating a population of candidate solutions.

Abstract: A system for teaching a robot includes a wearable device having a plurality of sensors for sensing signals representing at least one movement, orientation, position, force, and torque of any part of a user's body applied on a target. The system further includes a processing device configured to receive the sensed signals, the processing device further configured to store the sensed signals defining as data of teaching commands, a controller for controlling a robot receives the data of teaching commands and operates the robot according to the received data of teaching commands, an object sensor to detect position and orientation of an object as a workpiece of the robot, and a visual input device communicatively coupled to at least one of the processing device and the controller.

Abstract: Solutions for multi-robot configurations are co-optimized, to at least some degree, across a set of non-homogenous parameters based on a given set of tasks to be performed by robots in a multi-robot operational environment. Non-homogenous parameters may include two or more of: the respective base position and orientation of the robots, an allocation of tasks to respective robots, respective target sequences and/or trajectories for the robots. Such may be executed pre-runtime. Output may include for each robot: workcell layout, an ordered list or vector of targets, optionally dwell time durations at respective targets, and paths or trajectories between each pair of consecutive targets. Output may provide a complete, executable, solution to the problem, which in the absence of variability in timing, can be used to control the robots without any modification. A genetic algorithm, e.g., Differential Evolution, may optionally be used in generating a population of candidate solutions.

Abstract: A system for teaching a robot includes a wearable device having a plurality of sensors for sensing signals representing at least one movement, orientation, position, force, and torque of any part of a user's body applied on a target. The system further includes a processing device configured to receive the sensed signals, the processing device further configured to store the sensed signals defining as data of teaching commands, a controller for controlling a robot receives the data of teaching commands and operates the robot according to the received data of teaching commands, an object sensor to detect position and orientation of an object as a workpiece of the robot, and a visual input device communicatively coupled to at least one of the processing device and the controller.