Abstract: Systems, methods and apparatus for launching and recovering a vehicle via a side manway of a tank are provided. A launcher is coupled to a vertical side of the tank via a manway adapter. The launcher includes a first side having a lid and a second side configured to couple with the manway adapter. The lid is removed from the first side of the launcher. An autonomous vehicle is loaded into the launcher via the first side of the launcher. The lid is placed on the first side of the launcher. The launcher can be pressurized with the autonomous vehicle inside the launcher. The manway adapter is opened at the second side of the launcher to release the flammable fluid into the launcher. Subsequent to opening the manway adapter, the autonomous vehicle launches from the launcher into the tank to perform a tank inspection process.

Abstract: Methods and systems are described herein for a layered fail-safe redundancy system and architecture for privileged operation execution. The system may receive vehicle maneuvering commands from a controller over a first channel. When a user input is received to initiate a privileged mode for executing privileged commands, the system may receive a privileged command over a second channel. The system may identify, based on the privileged mode of operation and the privileged command, a privileged operation to be performed by a vehicle. The system may then transmit a request to the vehicle to perform the privileged operation.

Abstract: Systems and methods of manipulating/controlling robots. In many scenarios, data collected by a sensor (connected to a robot) may not have very high precision (e.g., a regular commercial/inexpensive sensor) or may be subjected to dynamic environmental changes. Thus, the data collected by the sensor may not indicate the parameter captured by the sensor with high accuracy. The present robotic control system is directed at such scenarios. In some embodiments, the disclosed embodiments can be used for computing a sliding velocity limit boundary for a spatial controller. In some embodiments, the disclosed embodiments can be used for teleoperation of a vehicle located in the field of view of a camera.

Abstract: Embodiments herein include an autonomy system of an automated vehicle performing operations for detecting and avoiding lazy or unpredictable vehicles on a roadway. The autonomy system generates bounding boxes for tracking traffic vehicles recognized in sensor data. For close vehicles, the autonomy system generates expanded bounding boxes. The autonomy system determines the close vehicle is an encroaching vehicle by detecting that the expanded bounding box of the close vehicle overlapped the middle lane line or enters into the automated vehicle's current lane of travel. The autonomy system may simulate the behavior of the close vehicle by forward-propagating predicted positions of the close vehicle and expanded bounding box over some future time. In response to detecting the encroaching vehicle, the autonomy system may determine whether to perform an avoidance action or select a particular avoidance action, such as biasing away from the encroaching vehicle.

Abstract: An autonomous vehicle can include a tractor; a trailer coupled with the tractor; a thermal sensor coupled with the trailer, the thermal sensor configured to measure a temperature of the trailer; and one or more processors. The processors can be configured to receive a plurality of temperature measurements of the trailer from the thermal sensor while the autonomous vehicle is driving; compare the plurality of temperature measurements to a condition; and automatically execute a trailer anomaly response protocol responsive to determining at least one of the plurality of temperature measurements satisfies the condition.

Abstract: Systems, apparatus, and methods are described for robotic learning and execution of skills. A robotic apparatus can include a memory, a processor, sensors, and one or more movable components (e.g., a manipulating element and/or a transport element). The processor can be operatively coupled to the memory, the movable elements, and the sensors, and configured to obtain information of an environment, including one or more objects located within the environment. In some embodiments, the processor can be configured to learn skills through demonstration, exploration, user inputs, etc. In some embodiments, the processor can be configured to execute skills and/or arbitrate between different behaviors and/or actions. In some embodiments, the processor can be configured to execute skills and/or behaviors using cached trajectories or plans. In some embodiments, the processor can be configured to execute skills requiring navigation and manipulation behaviors.

Abstract: Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

Abstract: A method for controlling a robot is provided. The method includes the steps of: determining a target robot to travel to a first loading station among a plurality of robots, on the basis of information on a location of the first loading station and a task situation of each of the plurality of robots, when a first transport target object is placed at the first loading station; and determining a travel route of the target robot with reference to information on the location of the first loading station and a location of a first unloading station associated with the first transport target object.

Abstract: An actuator system includes an actuator with a deformable shell defining a pouch, a fluid dielectric contained within the pouch, and first and second electrodes disposed over opposing sides of the pouch, each electrode having two long edges and two short edges. The system also includes a power source for providing a voltage between the electrodes. The electrodes cover 50 to 90% of the first and second sides, respectively, of the pouch, and a gap is defined between long edges of the pouch and the electrodes such that, upon application of the voltage at one of the short edges of the electrodes, respectively, the electrodes selectively zip together from the one of the short edges toward an opposing one of the short edges. The system may also include a support structure for enabling the actuator to maintain its shape regardless of the voltage provided by the power source.

Abstract: Systems and methods for safety-enabled control. Input values provided to a control system can be validated. Command gating can be performed for control values provided by the control system. Validation of input values and command gating for control values can be performed in accordance with respective validation windows. Validation windows can be dynamically adjusted based on data received via a sensor or interface.

Abstract: A method comprises monitoring, by a processor, using a sensor of a first vehicle, data associated with a retroreflective feature near a road being driven by the first vehicle; vectorizing, by the processor, the data associated with the retroreflective feature; generating, by the processor, a digital map including vectorized data associated with the retroreflective feature and a location associated with the retroreflective feature; receiving, by the processor, data associated with the retroreflective feature from a second vehicle; and executing, by the processor, a localization protocol to identify a location of the second vehicle using the digital map.

Abstract: Systems and methods for generating safe driving paths in autonomous driving adversity conditions can include receiving, by a computer system including one or more processors, sensor data of a vehicle that is traveling. The sensor data can be indicative of one or more autonomous driving adversity conditions, and can include image data depicting surroundings of the vehicle. The method can include executing, by the computer system, a trained machine learning model to predict, using the sensor data, a trajectory to be followed by the vehicle through the one or more autonomous driving adversity conditions, and providing, by the computer system, an indication of the predicted trajectory to an autonomous driving system of the vehicle.

Abstract: Systems and techniques performing autonomous navigation are illustrated. One embodiment includes a method for navigation. The method inputs a set of sensor data obtained from a plurality of sensors into at least one convolutional neural network (CNN). The at least one CNN generates a plurality of key-value pairs where each the key-value pair corresponds to an individual sensor from the plurality of sensors; and a value included in the key-value pair is determined based upon a subset of sensor data obtained from the individual sensor. The method inputs at least one navigation query and the plurality of key-value pairs into a Cross-Attention Transformer (CAT). The method obtains, from the CAT, a set of weighted sums, wherein each weighted sum corresponds to: a certain key-value pair; and a certain sensor from the plurality of sensors. The method updates a model depicting a 3D environment based on the set of weighted sums.

Abstract: An autonomous vehicle can include a perception sensor and one or more processors. The processors can be configured to automatically control the autonomous vehicle to an energy supply station to resupply the autonomous vehicle; detect an arrival of the autonomous vehicle at the energy supply station; detect a calibration target; collect data from the perception sensor corresponding with the calibration target; and calibrate the perception sensor based on the collected data.

Abstract: Disclosed herein include systems and methods for calibrating perception sensors of an automated vehicle using stationary calibration targets and preconfigured geographic information for the calibration targets. A controller of the automated vehicle references map data indicating locations of each calibration target, which is then recorded by the automated vehicle's perception sensors. The automated vehicle is configured to use a corrected geographical position system (GPS) process (e.g., RTK, PPK) to determine positions and orientations of each sensor and/or the automated vehicle. The controller uses these values to generate accurate calibrations of the automated vehicle and the sensors.

Abstract: An autonomous vehicle can include a horn; a sensor(s) configured to capture images; and one or more processors. The one or more processors can be configured to receive a sequence of images from the sensor(s), the sequence of images captured by the sensor(s) as the autonomous vehicle was moving; execute a machine learning model using the sequence of images as input to detect a human inside a second vehicle or in the surrounding environment of the autonomous vehicle depicted within the sequence of images; determine, based on the detection of the human inside the second vehicle or in the surrounding environment of the autonomous vehicle within the sequence of images, the human is depicted performing a defined arm gesture within the sequence of images; and activate the horn responsive to the determination that the human is depicted performing the defined arm gesture within the sequence of images.

Abstract: An autonomous vehicle comprises one or more processors. The processors can be configured to receive, from a sensor of the autonomous vehicle, an image of an environment outside of the autonomous vehicle. The processors can detect potential unknown objects based on the image. The processors can compare the detection based on the image to a set of data points of a LiDAR scan to determine if there are unknown objects on a roadway.

Abstract: Systems and methos for learning safe driving paths in autonomous driving adversity conditions can include acquiring, by a computer system, vehicle sensor data for a plurality of driving events associated with one or more autonomous driving adversity conditions. The vehicle sensor data can include, for each driving event, corresponding image data depicting surroundings of the vehicle and corresponding inertial measurement data. The computer system can acquire, for each driving event of the plurality of driving events, corresponding vehicle positioning data indicative of a corresponding trajectory followed by the vehicle, and train, using the vehicle sensor data and the vehicle positioning data, a machine learning model to predict navigation trajectories during the autonomous driving adversity conditions.

Abstract: Systems and methods of predicting a grade of a road upon which a vehicle is traveling are disclosed. An autonomous vehicle system can receive sensor data from a sensor measuring a response from at least one mechanical component of the autonomous vehicle as the autonomous vehicle navigates a road; detect a speed of the autonomous vehicle; determine a predicted grade of the road based on the sensor data and the speed; and navigate the autonomous vehicle based on the predicted grade of the road.

Abstract: Aspects of this technical solution can include determining, by one or more processors of a liquid collection system and based on respective amounts of liquid detected by a plurality of sensors and a velocity of a vehicle, a direction of movement of the liquid. The plurality of sensors can each be associated with respective ones of a plurality of receptacles. The plurality of sensors can be configured to detect respective amounts of liquid collected by respective openings of each of the plurality of receptacles associated with corresponding directions of movement of a liquid. The plurality of receptacles can be configured to collect at least a portion of the liquid via the respective openings.