According to one embodiment, a cargo handling apparatus includes a hand, a robot arm, a transfer device, a measurement device, and a control device. The hand holds an article. The robot arm moves the hand. The transfer device is arranged with the robot arm, and transfers the article. The measurement device measures a position and a size of the article. The control device performs a first operation of transferring the article to the transfer device by using the hand and the robot arm, and a second operation of transferring the transferred article by using the transfer device. The control device determines whether or not the robot arm will interfere with the transfer device or a second article on the transfer device when performing the first operation for a first article. The control device controls a start timing of the first operation according to a determination result of the interference.

The present disclosure relates to the field of modular robot control, and more particularly to a modular robot and a method for calculating a position of a module unit thereof. The modular robot and the position calculation method for module units of the modular robot are characterized in that position information of the module units is determined by recognizing interface identification information of docking parts of adjacent module units, and have the advantage of precise and quick recognition, thereby ensuring the accuracy of a position calculation result of each module unit, and facilitating the widespread application of the modular robot.

The present disclosure relates to the field of modular robot control, and more particularly to a modular robot and a method for calculating a position of a module unit thereof. The modular robot and the position calculation method for module units of the modular robot are characterized in that position information of the module units is determined by recognizing interface identification information of docking parts of adjacent module units, and have the advantage of precise and quick recognition, thereby ensuring the accuracy of a position calculation result of each module unit, and facilitating the widespread application of the modular robot.

In described embodiments of method for executing autonomous bin picking, a physical environment comprising a bin containing a plurality of objects is perceived by one or more sensors. Multiple artificial intelligence (AI) modules feed from the sensors to compute grasping alternatives, and in some embodiments, detected objects of interest. Grasping alternatives and their attributes are computed based on the outputs of the AI modules in a high-level sensor fusion (HLSF) module. A multi-criteria decision making (MCDM) module is used to rank the grasping alternatives and select the one that maximizes the application utility while satisfying specified constraints.