This description provides an autonomous or semi-autonomous excavation vehicle that is capable determining a route between a start point and an end point in a site and navigating over the route. The sensors collect any or more of spatial, imaging, measurement, and location data to detect an obstacle between two locations within the site. Based on the collected data and identified obstacles, the excavation vehicle generates unobstructed routes circumventing the obstacles, obstructed routes traveling through the obstacles, and instructions for removing certain modifiable obstacles. The excavation vehicle determines and selects the shortest route of the unobstructed and obstructed route and navigates over the selected path to move within the site.

A photoelectric conversion device includes a waveguide member disposed above a photoelectric conversion unit, and an insulating member disposed above a substrate, and surrounding at least part of the waveguide member. The waveguide member has a first side face, a second side face, and a third side face, arranged in that order from the substrate. An angle of inclination of the first side face is smaller than an angle of inclination of the second side face. An angle of inclination of the third side face is smaller than the angle of inclination of the second side face. The angle of inclination of the second side face is smaller than 90 degrees.

An object identification and collection method is disclosed. The method includes receiving a pick-up path that identifies a route in which to guide an object-collection system over a target geographical area to pick up objects, determining a current location of the object-collection system relative to the pick-up path, and guiding the object-collection system along the pick-up path over the target geographical area based on the current location. The method further includes capturing images in a direction of movement of the object-collection system along the pick-up path, identifying a target object in the images; tracking movement of the target object through the images, determining that the target object is within range of an object picker assembly on the object-collection system based on the tracked movement of the target object, and instructing the object picker assembly to pick up the target object.

A driving support Electronic Control Unit (ECU) initializes a target trajectory calculation parameter at a start of Lane Change Assist Control (LCA), calculates, based on the target trajectory calculation parameter, a target trajectory function representing a target lateral position in accordance with an elapsed time from the start of LCA; and calculates a target control amount according to the target trajectory function. When it is determined that the own vehicle has crossed a boundary white line, the driving support ECU again initializes the target trajectory calculation parameter, and calculate the target trajectory function based on the target trajectory calculation parameter.

Provided is an autonomous moving body that controls driving wheels by allowing a trailing caster to be located on a front side with respect to a traveling direction when it is recognized that there is no step in a traveling direction and controls the driving wheels to change the orientation of a base body in which the driving wheels and the trailing caster are arranged and approach the step so that at least one of two driving wheels contacts the step before the trailing caster contacts the step when it is recognized that there is a step in the traveling direction.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

The technology relates to autonomous vehicles having hitched or towed trailers for transporting cargo and other items between locations. Aspects of the technology provide a smart hitch connection between the fifth-wheel of a tractor unit and the kingpin of a trailer. This avoids requiring a person to make physical pneumatic and electrical connections between the fifth-wheel and kingpin using external hoses and cables. Instead, the necessary connections are made internally, autonomously. For instance, the fifth-wheel may provide air pressure via one or more slots arranged on a connection surface, and the trailer is configured to receive the air pressure through one or more openings on a contact surface of the kingpin. An electrical connection section of the fifth-wheel may also provide electrical signals and/or power to an electrical contact interface of the kingpin. Rotational information about relative alignment of the trailer to the tractor unit may also be provided.

Autonomous machine navigation involves determining a current pose of an autonomous machine based on non-vision-based pose data captured by one or more non-vision-based sensors of the autonomous machine. The pose represents one or both of a position and an orientation of the autonomous machine in a work region defined by one or more boundaries. Pose data is determined based on a return signal received in response to a wireless signal transmitted to a surface or subsurface object that passively provides the return signal. The return signal is identifiable with the object. The current pose is updated based on the pose data to correct or localize the current pose and to provide an updated pose of the autonomous machine in the work region.

An apparatus and the like for controlling a mobile body that are capable of adjusting a detection result by a radar device in accordance with a three-dimensional shape for each region of a three-dimensional map generated from an image captured by an image-capturing device are provided. A mobile body control unit 105 is an apparatus for controlling the vehicle (mobile body) including an image-capturing device 101 and a millimeter wave radar device 102 (radar device). A three-dimensional map generation unit 203 generates a three-dimensional map around the vehicle from an image captured by the image-capturing device 101. A radar weight map estimation unit 204 (weight estimation unit) estimates the weight of the detection result by the millimeter wave radar device 102 for each region of the three-dimensional map from the three-dimensional shape for each region of the three-dimensional map. A weight adjustment unit 205 (adjustment unit) adjusts a detection result by the millimeter wave radar device 102 on the basis of a weight.

This disclosure relates generally to systems and methods for object detection using a geometric semantic map based robot navigation using an architecture to empower a robot to navigate an indoor environment with logical decision making at each intermediate stage. The decision making is further enhanced by knowledge on actuation capability of the robots and that of scenes, objects and their relations maintained in an ontological form. The robot navigates based on a Geometric Semantic map which is a relational combination of geometric and semantic map. In comparison to traditional approaches, the robot's primary task here is not to map the environment, but to reach a target object. Thus, a goal given to the robot is to find an object in an unknown environment with no navigational map and only egocentric RGB camera perception.