Rios Intelligent Machines, Inc.

Rioa Intelligent Machines, Inc.

Christopher Lalau Keraly

Christopher A Paulson

Clinton J Smith

Matthew E Shaffer

Bernard D Casse

Nicholas L Choi

Leo Keselman

Michael Alexander Benedict

Laura L Sullivan

Kent A Evans

Laura Stelzner

Anthony Canalungo

Christipher A Paulson

Scott M Dellenbaugh

A robotic work cell uses an object separating mechanism to disperse bulk objects into a 2D arrangement on a horizontal surface and uses a vision system to generate pick-up (positional) data and rotational orientation data for each sequentially selected target object of the 2D arrangement. A pick-and-place robot mechanism uses the positional data to pick-up each target object and uses the rotational orientation data to reorientate the target object during transfer to a designated hand-off location. A carousel-type robotic end-tool disposed on a 4-axis object-processing robot mechanism rotates a gripper mechanism around a vertical axis to move the target object from the hand-off location to a designated processing location, where an associated processing device performs a desired process (e.g., label application) on the target object. In one embodiment the gripper mechanism is selectively rotatable around a horizontal axis to facilitate processing on opposing surfaces of the target object.

Automated production work cell

A work cell and method for automatically separating objects disposed in 3D clusters includes dispensing the objects onto a horizontal conveying surface to form a 2D array, reforming the 2D array into a 1D stream in which the objects move in single-file in a predefined moving direction, utilizing a vision-based or other stationary sensing system to identify a selected target object in the 1D stream as the target object passes through an image capture (sensing) region, calculating trajectory data defining the target object's time-based position in the 1D stream, and then utilizing the trajectory data to control a robot arm or other object removal mechanism such that only the selected object is forcibly removed (e.g., swiped or picked-up) from the horizontal conveying surface. A continuous-loop-type conveying mechanism includes two parallel conveyor-belt-type conveying structures and associated belt-switching structures. An AI-powered vision system identifies new object shapes during preliminary learning phases.

Automatically individually separating bulk objects

A human-like tactile perception apparatus for providing enhanced tactile information (feedback data) from an end-effector/gripper to the control circuit of an arm-type robotic system. The apparatus's base structure is attached to the gripper's finger and includes a flat/planar support plate that presses a pressure sensor array against a target object during operable interactions. The pressure sensor array generates pressure sensor data that indicates portions of the array contacted by surface features of the target object. A sensor data processing circuit generates tactile information in response to the pressure sensor data, and then transmits the tactile information to the robotic system's control circuit. An optional mezzanine connector extends through an opening in the support plate to pass pressure sensor data to the processing circuit. An encapsulating layer covers the pressure sensor array and transmits pressure waves generated by slipping objects to enhance the tactile information.

Tactile perception apparatus for robotic systems

A robotic gripper (end effector) for an arm-type robotic system includes a hierarchical sensor architecture that utilizes a central data processing circuit to generate rich sensory tactile data in response to pressure, temperature, vibration and/or proximity sensor data generated by finger-mounted sensor groups in response to interactions between the robotic gripper and a target object during robotic system operations. The rich sensory tactile data is used to generate feedback signals that directly control finger actuators and/or tactile information that is supplied to the robotic system's control circuit. Sensor data processing circuits are configured to receive single-sensor data signals in parallel from the sensor groups, and to transmit corresponding finger-level sensor data signal on a serial bus/signal line to the central data processing circuit. Each sensor group and an associated sensor data processing circuit are disposed on a PCB structure and mounted on a contact portion of an associated gripper finger.

Robotic gripper with integrated tactile sensor arrays

A flex-rigid sensor apparatus for providing sensor data from sensors disposed on an end-effector/gripper to the control circuit of an arm-type robotic system. The apparatus includes piezo-type pressure sensors sandwiched between lower and upper PCB stack-up structures respectively fabricated using rigid PCB (e.g., FR-4) and flexible PCB (e.g., polyimide) manufacturing processes. Additional (e.g., temperature and proximity) sensors are mounted on the upper/flexible stack-up structure. A spacer structure is disposed between the two stack-up structures and includes an insulating material layer defining openings that accommodate the pressure sensors. Copper film layers are configured to provide Faraday cages around each pressure sensor. The pressure sensors, additional sensors and Faraday cages are connected to sensor data processing and control circuitry (e.g., analog-to-digital converter circuits) by way of signal traces formed in the lower and upper stack-up structures and in the spacer structure. An encapsulation layer is formed on the upper PCB stack-up structure.

Flex-rigid sensor array structure for robotic systems

A robotic network includes multiple work cells that communicate with a cloud server using a network bus (e.g., the Internet). Each work cell includes an interface computer and a robotic system including a robot mechanism and a control circuit. Each robot mechanism includes an end effector/gripper having integral multimodal sensor arrays that measure physical parameter values (sensor data) during interactions between the end effector/gripper and target objects. The cloud server collects and correlates sensor data from all of the work cells to facilitate efficient diagnosis of problematic robotic operations (e.g., accidents/failures), and then automatically updates each work cell with improved operating system versions or AI models (e.g., including indicator parameter value sets and associated secondary robot control signals that may be used by each robot system to detect potential imminent robot accidents/failures during subsequent robot operations.

Robotic work cell and network

A multimodal sensing architecture utilizes an array of single sensor or multi-sensor groups (superpixels) to facilitate advanced object-manipulation and recognition tasks performed by mechanical end effectors in robotic systems. The single-sensors/superpixels are spatially arrayed over contact surfaces of the end effector fingers and include, e.g., pressure sensors and vibration sensors that facilitate the simultaneous detection of both static and dynamic events occurring on the end effector, and optionally include proximity sensors and/or temperature sensors. A readout circuit receives the sensor data from the superpixels and transmits the sensor data onto a shared sensor data bus. An optional multimodal control generator receives and processes the sensor data and generates multimodal control signals that cause the robot system's control circuit to adjust control operations performed by the end effector or other portions of the robot mechanism and when the sensor data indicates non-standard operating conditions.

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San Francisco, CA, United States of America

Christopher Lalau Keraly