A system for a boat includes a first guideway installed under a main deck of the boat, a first battery pack coupled to the first guideway under the main deck, and a first actuator configured to move the first battery pack along the first guideway. A controller is electrically and/or signally coupled with the first actuator. A user input device is electrically and/or signally coupled with the controller. The controller is configured to control the first actuator to move the first battery pack along the first guideway in response to an input to the user input device so as to relocate the weight of the first battery pack under the main deck.

A travel assistance system for an agricultural machine includes a traveling device included in an agricultural machine, an acquirer to acquire a travel route to be traveled by the agricultural machine and created on a field representing an agricultural field, a corrector to correct the travel route acquired by the acquirer, and a controller configured or programmed to control the traveling device based on the travel route corrected by the corrector. The travel route includes lines connected to each other and the corrector includes a creator to create an auxiliary line extending from an ending portion of a first line of the lines to a starting portion of a second line of the lines connected to the ending portion of the first line, the auxiliary line being inclined relative to the second one of the lines.

In an abnormality determination device for determining presence or absence of an abnormality of a 6-axis inertial measurement sensor installed in a vehicle to detect a forward-backward acceleration, a lateral acceleration, a vertical acceleration, a roll rate, a pitch rate, and a yaw rate of the vehicle, the abnormality determination device includes: a 3-axis inertial measurement sensor that detects the forward-backward acceleration, the lateral acceleration, and the yaw rate; and an abnormality determination unit that determines presence or absence of an abnormality of the 6-axis inertial measurement sensor, wherein the abnormality determination unit determines the presence or absence of an abnormality of the 6-axis inertial measurement sensor by comparing the forward-backward acceleration, the lateral acceleration, and the yaw rate acquired by the 6-axis inertial measurement sensor with the forward-backward acceleration, the lateral acceleration, and the yaw rate acquired by the 3-axis inertial measurement sensor, respectively.

In various examples, a machine learning modelsuch as a deep neural network (DNN)may be trained to use image data and/or other sensor data as inputs to generate two-dimensional or three-dimensional trajectory points in world space, a vehicle orientation, and/or a vehicle state. For example, sensor data that represents orientation, steering information, and/or speed of a vehicle may be collected and used to automatically generate a trajectory for use as ground truth data for training the DNN. Once deployed, the trajectory points, the vehicle orientation, and/or the vehicle state may be used by a control component (e.g., a vehicle controller) for controlling the vehicle through a physical environment. For example, the control component may use these outputs of the DNN to determine a control profile (e.g., steering, decelerating, and/or accelerating) specific to the vehicle for controlling the vehicle through the physical environment.

In various examples, a current claimed set of points representative of a volume in an environment occupied by a vehicle at a time may be determined. A vehicle-occupied trajectory and at least one object-occupied trajectory may be generated at the time. An intersection between the vehicle-occupied trajectory and an object-occupied trajectory may be determined based at least in part on comparing the vehicle-occupied trajectory to the object-occupied trajectory. Based on the intersection, the vehicle may then execute the first safety procedure or an alternative procedure that, when implemented by the vehicle when the object implements the second safety procedure, is determined to have a lesser likelihood of incurring a collision between the vehicle and the object than the first safety procedure.

A method executable by an autonomous mobile device includes moving in a work environment, obtaining environmental data acquired by a sensing device, and determining whether the sensing device is in a suspected ineffective state based on the environmental data. The method also includes based on a determination that the sensing device is in the suspected ineffective state, rotating at a same location for a first predetermined spin angle. The method also includes obtaining an estimated rotation angle based on one or more motion parameters acquired by a dead reckoning sensor, comparing the estimated rotation angle with the first predetermined spin angle, and based on a determination that a difference between the estimated rotation angle and the first predetermined spin angle is greater than a first predetermined threshold value, executing escape instructions to move backwardly for a first predetermined distance and move along a curve or a folded line.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

An unmanned aerial vehicle (UAV) or drone executes a neural network to assist with detecting and responding to attacks. The neural network may monitor, in real time, the data stream from a plurality of onboard sensors during navigation and may communicate with a high-altitude pseudosatellite (HAPS) platform. For example, if the neural network detects a cyber-attack but determines that it does not interfere with external communications, it may shift navigation control of the drone to the HAPS.

An outdoor robotic tool comprising a first part and a second part, wherein the first part supports the second part through a suspension arrangement. The suspension arrangement comprises a first component, which comprises at least one magnetic member; and a second component, which comprises at least one magnetic member. The first component is attached to the first part, wherein the second component is attached to the second part, wherein at least one of the magnetic members of suspension arrangement is a permanent magnet; and wherein a magnetic member of the first component is positioned so as to magnetically interact with a magnetic member of the second component when in use. A magnetic field sensing unit may be present that comprises a control unit and a magnetic field sensor. A method for detecting the alignment of the first part relative to the second part, wherein the method comprises detecting the magnetic field using the magnetic field sensing unit.

The described positional awareness techniques employing sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to find new area to cover by a robot encountering an unexpected obstacle traversing an area in which the robot is performing an area coverage task. The sensory data are gathered from an operational camera and one or more auxiliary sensors.