Aerial vehicles, their structures and methods of locomotion are described. An aerial vehicle may include a fuselage having an x-axis, a plurality of flexible structures emanating from the fuselage that take the form of a feather, wing and/or tentacle, at least one motor, and at least one propeller driven by one or more motors. Each flexible structure may extend from a fuselage in any direction and are used to enhance the observability of the aircraft by moving and/or oscillating within a frequency band and at a magnitude that is more easily observed by and catches the human eye.

Provided is a self-propelled cleaning robot that can efficiently clean a flat surface even if a step is formed. The self-propelled cleaning robot that self-travels on a structure to clean a flat surface of the structure, the structure being installed in an outdoor location, the robot includes: a robot main body (2) in which a self-propelled moving mechanism (4) is provided; a cleaning unit (10) that is provided in a front portion and/or a rear portion of the robot main body (2); and a controller (30) that controls activation of the moving mechanism (4). At this point, the controller (30) includes an attitude controller (35) that detects an attitude of the robot main body (2), the attitude controller (35) includes a floating detection sensor (36) that detects floating in one of the front portion and the rear portion of the robot main body (2), and, when the floating detection sensor (36) detects the floating in one of the front portion and the rear portion of the robot main body (2), the controller (30) controls the activation of the moving mechanism (4) such that the cleaning unit (10) passes through a place where the floating is detected after the floating is eliminated.

A mobile apparatus includes circuitry to control the mobile apparatus to perform teaching travel and autonomous travel, generate; for each of nodes on a travel route independently for external sensors, calculation information for calculating a deviation between a node passed in the teaching travel and a point passed in the autonomous travel; store in the memory the calculation information in association with the node and the external sensor; calculate, for each node independently for the external sensors, the deviation based on the calculation information and a of the extern sensor value in the autonomous travel; determine, for each node independently for the external sensors, the calculated deviation as a position and posture of the node with reference to the position and posture of the mobile apparatus; integrate the positions and postures of the node determined independently for the external sensors; and control the mobile apparatus to autonomously travel.

An information processing device includes a route information acquisition unit configured to acquire information about a route on which a movable apparatus moves; an environment information acquisition unit configured to acquire environment information on the route for moving the movable apparatus; an estimation unit configured to estimate a self-position and orientation of the movable apparatus and store a result of the estimation in the environment information; an accuracy calculation unit configured to calculate an accuracy for estimating a self-position and orientation of the movable apparatus on the basis of the environment information; and a route setting unit configured to update a route of the movable apparatus according to an accuracy calculated by the accuracy calculation unit.

A method and apparatus are disclosed for a clutter tidying robot utilizing floor segmentation for its mapping and navigation system, whereby a perception module and navigation module transform lidar and image data from lidar sensors and cameras of a robot sensing system using segmentation and pseudo-laserscan or point cloud transformations to generate global and local maps. The robot pose and maps are transmitted to a robot brain that directs an action module to produce robot action commands controlling the operation of a clutter tidying robot using the pose and map data. In this manner multi-stage planning and sophisticated obstacle avoidance techniques may be incorporated into autonomous robot operations.

A device for steering an agricultural machine having independently steerable axles includes first and second steerable axle interface to couple with a first steering mechanism of a first steerable axle and a second a second steering mechanism of a second steerable axle. The device includes a planning module having a guidance path for the agricultural machine, and a steering control module to coordinate steering of the steering mechanisms. The steering control module includes a translational comparator to determine a translational difference between a location of the agricultural machine relative to the guidance path, an angular comparator to determine an angular difference between an angular orientation of the agricultural machine relative to the guidance path, and a translation steering controller to actuate the first and second steering mechanisms according to the translational difference. The device includes an angular steering controller to actuate the second steering mechanism according to the angular difference.

An information processing apparatus according to an embodiment of the present technology includes: a calculation unit. The calculation unit calculates a self-position of an own device that moves with a moving object, in accordance with a first movement state of a moving object and a second movement state of the own device, on a basis of first movement information relating to the moving object and second movement information relating to the own device. As a result, it is possible to improve detection accuracy. Further, it is possible to improve the accuracy and reliability of the self-position. Since no displacement of the self-position occurs even in a movement space, it is possible for a drone flying in the air to avoid collision with obstacles in the movement space.

An electrical system for an aircraft is disclosed, comprising: at least one processor configured to: receive first sensor data from at least one inertial sensor of the aircraft, wherein the first sensor data is indicative of a state of the aircraft, receive second sensor data from at least one of an airspeed sensor indicating an airspeed of the aircraft or a propeller speed sensor indicating a propeller speed of at least one propeller of the aircraft, and determine the state of the aircraft based on the first sensor data, wherein determining the state of the aircraft comprises filtering aircraft state measurements based on the second sensor data to lessen influence of propeller vibrations on at least one aircraft signal. The at least one processor is further configured to control the aircraft based on a pilot input command and the determined state of the aircraft.

Example computer-implemented methods and systems for anomaly-sensing based multi-agent navigation are disclosed. One example computer-implemented method includes: receiving relative distance data specifying distance between at least one pair of agents of a plurality of agents, each of a subset of the plurality of agents having an anomaly sensor subsystem; receiving anomaly data from at least one anomaly sensor subsystem of one of the plurality of agents; obtaining pre-surveyed map data; and determining global pose data of the plurality of agents based on the relative distance data and based on comparing the anomaly data to the pre-surveyed map data.

Embodiments of the present disclosure may include a method for optimizing flight of an unmanned aerial vehicle (UAV) including a payload, the method including receiving one or more human-initiated flight instructions. Embodiments may also include determining a UAV context based at least in part on Inertial Measurement Unit (IMU) data from the UAV. Embodiments may also include receiving payload identification data. Embodiments may also include accessing a laden flight profile based at least in part on the payload identification data. Embodiments may also include determining one or more laden flight parameters. In some embodiments, the one or more laden flight parameters may be based at least in part on the one or more human-initiated flight instructions, the UAV context, and the laden flight profile.