The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move. One embodiment further advantageously uses multiple particles to maintain multiple hypotheses with respect to localization and mapping. Further advantageously, one embodiment maintains the particles in a relatively computationally-efficient manner, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems.

Apparatus and methods for a modular robotic device with artificial intelligence that is receptive to training controls. In one implementation, modular robotic device architecture may be used to provide all or most high cost components in an autonomy module that is separate from the robotic body. The autonomy module may comprise controller, power, actuators that may be connected to controllable elements of the robotic body. The controller may position limbs of the toy in a target position. A user may utilize haptic training approach in order to enable the robotic toy to perform target action(s). Modular configuration of the disclosure enables users to replace one toy body (e.g., the bear) with another (e.g., a giraffe) while using hardware provided by the autonomy module. Modular architecture may enable users to purchase a single AM for use with multiple robotic bodies, thereby reducing the overall cost of ownership.

An example method includes receiving, by a mobile robotic device, power from a battery of a first battery pack in order to operate within an environment. The method further includes establishing a first data channel between the mobile robotic device and the first battery pack. The method also includes using the first data channel to transfer sensor data acquired by the mobile robotic device during operation to a local data storage component of the first battery pack. The method additionally includes navigating, by the mobile robotic device, to a battery exchange station to transfer the first battery pack containing the battery and the local data storage component with the sensor data to the battery exchange station. The method further includes after transferring the first battery pack to the battery exchange station, receiving a second battery pack from the battery exchange station to continue operation within the environment.

An example method includes receiving, by a mobile robotic device, power from a battery of a first battery pack in order to operate within an environment. The method further includes establishing a first data channel between the mobile robotic device and the first battery pack. The method also includes using the first data channel to transfer sensor data acquired by the mobile robotic device during operation to a local data storage component of the first battery pack. The method additionally includes navigating, by the mobile robotic device, to a battery exchange station to transfer the first battery pack containing the battery and the local data storage component with the sensor data to the battery exchange station. The method further includes after transferring the first battery pack to the battery exchange station, receiving a second battery pack from the battery exchange station to continue operation within the environment.

A method for load balancing of robots includes: receiving, by a first task server configured to manage a first spatial region, a task to be performed by a robot; determining, by the first task server, that the task cannot efficiently be performed within the first spatial region; finding, by the first task server, a second task server configured to manage a second spatial region to which the task can be assigned; and sending, by the first task server, the task to the second task server.

A coverage robot including a chassis, multiple drive wheel assemblies disposed on the chassis, and a cleaning assembly carried by the chassis. Each drive wheel assembly including a drive wheel assembly housing, a wheel rotatably coupled to the housing, and a wheel drive motor carried by the drive wheel assembly housing and operable to drive the wheel. The cleaning assembly including a cleaning assembly housing, a cleaning head rotatably coupled to the cleaning assembly housing, and a cleaning drive motor carried by cleaning assembly housing and operable to drive the cleaning head. The wheel assemblies and the cleaning assembly are each separately and independently removable from respective receptacles of the chassis as complete units.

A method for guiding a robot equipped with a camera to facilitate three-dimensional (3D) reconstruction through sampling based planning includes recognizing and localizing an object in a two-dimensional (2D) image. The method also includes computing 3D depth maps for the localized object. A 3D object map is constructed from the depth maps. A sampling based structure is grown around the 3D object map and a cost is assigned to each edge of the sampling based structure. The sampling based structure may be searched to determine a lowest cost sequence of edges that may, in turn be used to guide the robot.

Methods and systems are described for controlling movement and an applied force at the tip of the continuum robot that includes a plurality of independently controlled segments along its length. An estimated force at the tip of the continuum robot is determined based on measurements of loads and positions of each segment. A reference position command and a force command are received from a user interface. The reference position command indicates a desired movement for the distal end of the continuum robot and the force command indicates a desired force to be applied by the tip of the continuum robot to a tissue surface. The position of the continuum robot is adjusted to cause the tip of the continuum robot to apply the desired force to the tissue surface based on the estimated force at the tip of the continuum robot, the reference position command, and the force command.

A mobile collaborative robot includes a force sensor which is provided on a robot support on a carriage, a robot information acquisition unit which acquires robot information including posture information of the robot main body and load information of a load acting on the robot main body, a force calculation unit which calculates the external force acting on the robot support based on the robot information, and a judgment unit which judges that the mobile collaborative robot has come into contact with a human when a difference between the external force detected by the force sensor and the external force calculated by the force calculation unit is above a predetermined value or when a difference between the amount of change in the detected external force and the amount of change in the calculated external force is above a predetermined value 1.

The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move. One embodiment further advantageously uses multiple particles to maintain multiple hypotheses with respect to localization and mapping. Further advantageously, one embodiment maintains the particles in a relatively computationally-efficient manner, thereby permitting the SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems.