An information processing apparatus includes a map generation unit and a route planning unit. The map generation unit calculates allowable complexity of a task as task complexity tolerance and generates a task complexity tolerance map indicating a distribution of the task complexity tolerance. The route planning unit performs route planning for a mobile body (MB) to satisfy constraints based on the task complexity tolerance map.

Embodiments of the present disclosure may include a system for lossy optimization of a return-to-home route, the system including a non-volatile memory. Embodiments may also include a wireless transceiver. Embodiments may also include a processor in communication with a non-volatile memory including a processor-readable media having thereon a set of executable instructions, configured, when executed, to cause the processor to receive via the wireless transceiver of coordinate samples (kn) over a time interval. In some embodiments, each coordinate sample may include at least two-dimensional pairs (x,y) and a vehicle yaw, the two-dimensional pairs (x,y) indicative of a pilot-assisted vehicle path over the time interval. Embodiments may also include identify a first coordinate pair of interest (x0,y0) and yaw0, a subsequent second coordinate pair (x1,y1), and a third subsequent coordinate pair (x2,y2) and yaw2.

A method and system for navigating a robot 100 in a designated environment are provided herein. In an embodiment, the method comprises: selectively identifying reference features from a scale plan of the designated environment; generating a topometric map 178 of the designated environment, the topometric map 178 being associated with the identified reference features; generating an initial navigation path 176 of a local region 174 from the topometric map 178; as the robot 100 navigates along the initial navigation path 176 of the local region 174, detecting onsite reference features of the local region 174 that correspond to the identified reference features of the scale plan; and updating the topometric map 178 based on the correspondence to generate a regional path plan 188 to guide the robot's direction in the local region 174.

A remote control apparatus includes a manipulator to manipulate a working vehicle remotely, a communication module configured or programmed to receive traveling information that indicates a speed or an acceleration of the working vehicle, a display, and a controller configured or programmed to cause the display to perform highlighted display that changes in accordance with the traveling information when the working vehicle is driven remotely by using the manipulator.

Systems and methods for controlling an aerial vehicle to avoid obstacles are disclosed. A system can detect, based on a world model generated from sensor data captured by one or more sensors positioned on the aerial vehicle during flight, an obstacle for the aerial vehicle, and trigger an augmented manual control mode responsive to a speed of the aerial vehicle being less than a predetermined threshold and detecting the obstacle. The system can set, responsive to triggering the augmented manual control mode, a speed constraint for the aerial vehicle in a direction of the obstacle based on a distance between the aerial vehicle and the obstacle. The system can receive an instruction to navigate the aerial vehicle in the direction at a second speed, and adjust the instruction to replace the second speed with the speed constraint, causing the aerial vehicle to navigate at the speed constraint.

Proposed is a mobile robot capable of learning risk-aware traversability via accumulated navigation experience. The mobile robot includes a point cloud sensor which acquires point cloud data, an elevation map generation part which generates a grid-based elevation map by using the point cloud data, an attribute data generation part which calculates a plurality of types of terrain attribute values for each of grid cells from the elevation map and generates attribute data sets having the plurality of types of terrain attribute values for each of the grid cells, a training data generation part which generates a plurality of traversability training data sets and a plurality of non-traversability training data sets by labeling whether each of the attribute data sets is in traversable condition, a risk calculation part which calculates a traversal risk, and a risk-aware self-training part which trains a traversability evaluation model for traversability evaluation through self-training.

An agricultural work assistance system includes a controller including a processor configured or programmed to, when agricultural work is to be performed or being performed by a working device coupled to an agricultural machine while the agricultural machine is traveling in an agricultural field, determine whether or not a manual operation of the working device is required, and an output interface to output a notification indicating that the manual operation of the working device is required, when the controller determines that the manual operation of the working device is required.

An agricultural harvesting system includes one or more processors and memory storing instructions, executable by the one or more processors, that, when executed by the one or more processors, cause the one or more processors to perform steps comprising: obtaining harvesting logistics data; identifying, based, at least, on the harvesting logistics data, one or more harvest zones, each harvest zone indicative of a respective area of a worksite to be harvested; selecting, based, at least, on the one or more harvest zones, one or more unloading zones, each unloading zone indicative of a respective area at the worksite at which a material receiving machine is to be positioned to receive harvested material; and generating a control signal based, at least, on the one or more unloading zones.

A computer implemented method includes obtaining historical operation data relative to a plurality of historical operations, the historical operation data including historical machine data, historical worksite data, historical productivity data, and historical logistics data; obtaining current operation data relative to an underway or upcoming operation, the current operation data including current machine data and current worksite data; generating, based on the obtained historical operation data and the obtained current operation data, an operation plan output relative to the underway or upcoming operation, the operation plan output including one or more of: (i) one or more machine routes; (ii) one or more sub-operation locations; (iii) one or more operation plan maps; or a combination of (i), (ii), and (iii); and generating control signals to control one or more mobile agricultural work machines based on the operation plan output.

The operations of a computer-implemented method include obtaining a topological map of an environment including a series of waypoints and a series of edges. Each edge topologically connects a corresponding pair of adjacent waypoints. The edges represent traversable routes for a robot. The operations include determining, using the topological map and sensor data captured by the robot, one or more candidate alternate edges. Each candidate alternate edge potentially connects a corresponding pair of waypoints that are not connected by one of the edges. For each respective candidate alternate edge, the operations include determining, using the sensor data, whether the robot can traverse the respective candidate alternate edge without colliding with an obstacle and, when the robot can traverse the respective candidate alternate edge, confirming the respective candidate alternate edge as a respective alternate edge. The operations include updating, using nonlinear optimization and the confirmed alternate edges, the topological map.