A method for determining a geographical location of a vehicle (10) includes using a camera/sensor device (20) of the vehicle for recording (S10) first image and sensor data (30) from surroundings of the vehicle (10) while the vehicle (10) is traveling a route. The first image and sensor data (30) are assigned geographical coordinates and are sent to a data evaluation unit (50) for creating a digital map. The method continues by using a second camera and sensor device (20) for recording (S40) second image and sensor data (30) from surroundings while the vehicle (10) is traveling the same route and sending (S50) the recorded second image and sensor data (30) to the data evaluation unit (50). The data evaluation unit (50) compares (S60) the recorded second image and sensor data (30) with the digital map of the surroundings (70) and determines (S70) a position of the vehicle (10).

The invention discloses a fully automatic intelligent rubber tapping robot, which comprises a moving platform and a rubber tapping robot arm. The rubber cutting mechanical arm is installed on the moving platform. tapping robot arm is installed on the moving platform. The tapping robot arm is specially designed for rubber cutting operation, the end of the tapping robot arm is equipped with an end actuator, which is composed of a tree-hugging fixed device and a sliding rubber tapping device. The invention can carry out the rubber cutting operation independently without manual intervention, which greatly reducing the manual input, and obviously improving the rubber cutting efficiency and time economy conversion efficiency. The movable system can work alone in a whole rubber forest with a large working area and reduces the average input cost per tree. The technical indexes of the rubber tree, such as cutting depth, cutting skin consumption and cutting smoothness, all meet the requirements of traditional rubber cutting technology and have good popularization and application value.

An autonomous driving control device is capable of starting an autonomous driving control without an operation of a driver and reducing a possibility that the driver can not start manual driving. An autonomous driving control is switched to manual driving when a determination section determines that the amount of operation by the driver is equal to or greater than a first threshold, before a predetermined time elapses since the autonomous driving control is automatically started. An autonomous driving control is switched to a manual driving when the determination section determines that the amount of operation by the driver is equal to or greater than a second threshold that is greater than the first threshold, after the predetermined time elapses.

A prediction system for simulating effects of a real-world event can be used for autonomous driving. In operation, the system receives input data regarding a complex system (e.g., roadways) and various real-world events. A full-scale network is constructed of the complex system, such that nodes represent road intersections and edges between nodes represent road segments linking the road intersections. The network is reduced is scaled down to generate a multi-layer model of the complex system. Each layer in the model is simulated to identify equilibrium flows, with the model thereafter destabilized by applying stimuli to reflect the real-world event. An autonomous vehicle can then be caused to chart and traverse a road path based on road segments and intersections that are least affected by the real-world event.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

An automatic storage and retrieval system includes: a delivery vehicle; a storage container carried by the delivery vehicle; and an unloading station for unloading an item from the storage container while it is being carried by the delivery vehicle. The unloading station includes: an unloading device; and a destination conveyor configured to convey the item to a target destination, wherein the unloading device is configured to move the item through a side opening of the storage container to the destination conveyor.

Automated systems and methods for deployment of mounting tubs that support photovoltaic modules are provided in which a feeder assembly includes a screw thread assembly and a pivot arm. The screw thread assembly has at least one rotatable threaded component, and two such components in exemplary embodiments, positioned within the feeder assembly. The rotatable threaded component supports the stack of mounting tubs and rotates to separate the individual mounting tub from the stack of mounting tubs and lower the individual mounting tub onto the pivot arm. The pivot arm is configured to interact with an individual mounting tub and pivots to dispense the individual mounting tub onto a mounting surface. A sensor may be provided to detect the positions of the individual mounting tubs as they are moved, and a control system communicates with the sensor and the feeder assembly. The feeder assembly and a hopper holding the stack of mounting tubs may be mounted on an autonomous cart.

A method of controlling a primary vehicle (18) comprising an automated driving system (20) for driving the primary vehicle autonomously when the primary vehicle is in an autonomous mode, the primary vehicle also being operable manually by a driver when in a manual mode, the method comprising: determining failure of the driver to accept a request to switch the primary vehicle to the manual mode when the vehicle is in the autonomous mode; determining a primary vehicle driving state; acquiring vehicle data for one or more surrounding secondary vehicles (22); determining a contingency action to take with the primary vehicle based on the primary vehicle driving state and the vehicle data for the or each secondary vehicle; and outputting the contingency action to at least one system of the primary vehicle to drive the primary vehicle autonomously in accordance with the determined contingency action.

A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.

Provided are autonomous vehicles and methods of controlling autonomous vehicles through topological planning with bounds, including receiving map data and sensor data, expanding a topological tree by adding a plurality of nodes to represent a plurality of actions associated with the plurality of constraints, generating a bound based on a constraint in the geographic area, the bound associated with an action for navigating the autonomous vehicle relative to the at least one constraint, storing the bound in a central bound storage, linking a set of bounds of a tree node to the bound via a bound identifier, wherein the first bound is initially linked as an active bound, or alternatively, as an inactive bound after determining it is not the most restrictive bound at any sample index, and control the autonomous vehicle based on the topological tree, to navigate the plurality of constraints.