According to one embodiment, an information processor includes a memory and processing circuitry. The circuitry receives area information indicating a second area in a first area around a movable body apparatus and third areas in the first area, wherein the movable body apparatus is movable in the second area and an object is present in each of the third areas. The circuitry receives movement information including at least one of a velocity, a movement direction or an acceleration of the apparatus. The circuitry acquires evaluation values each indicative of a damage to be caused when the apparatus collides with each object in the third areas, and determines, based on the evaluation values, a position corresponding to a first object which causes a least damage.

An autonomous mobile device (AMD) builds up electrostatic charges from moving and generates heat from the operation of internal components. In addition to possible user discomfort, electrostatic discharges may damage sensors and electronics. Electrostatic charges are dissipated from the AMD using an electrostatic dissipation structure and conductive wheels. A conductive path between a chassis ground, the electrostatic dissipation structure, and the conductive wheels improves the dissipation of electrostatic charges. Electrostatic charges are also dissipated from components by mounting the components using conductive materials. Sensors may be affixed to a support structure that is affected by thermal expansion. Thermal expansion may distort precise positioning of sensors, reducing accuracy of sensor data. An elastomeric foam may be used to mount sensors to a support structure, allowing for thermal expansion without distorting the positioning of the sensors.

A self-propelled device is disclosed that includes a center of mass drive system. The self-propelled device includes a substantially cylindrical body and wheels, with each wheel having a diameter substantially equivalent to the body. The self-propelled device may further include an internal drive system with a center of mass below a rotational axis of the wheels. Operation and maneuvering of the self-propelled device may be performed via active displacement of the center of mass.

A system is disclosed for autonomously removing waste from a plurality of receptacles at separate locations within a service area. The system may include a service vehicle, and a plurality of transporters. The plurality of transporters may be configured to autonomously move the plurality of receptacles to the service vehicle.

A remote-controlled powder fire extinguishing system includes a robotic mobile unit including a powder storage unit, a powder fluidization system, and a powder distribution system, the mobile unit being configured to distribute a fluidized powder onto a location affected by a fire, a remote control center, operable outside of the location affected by the fire, including a controller programmed to remotely communicate with and control the mobile unit, the powder storage unit, the powder fluidization system, and the powder distribution system, and a device configured to provide wireless connection between the mobile unit and the remote control center.

A method for operating a plurality of vehicles includes sending over a communication network and to a first vehicle navigation data for an autonomous navigation of the first vehicle in a parking facility, the first vehicle being assigned as a guide vehicle to a second vehicle that is to autonomously follow the first vehicle, a target signal being sent to the second vehicle via the communication network while the second vehicle is following the guide vehicle during the autonomous navigation of the guide vehicle in the parking facility, the signal indicating that the second vehicle is to terminate the following and to park at a target position.

The invention regards a service robot system comprising a robot and a learning method for the system. The robot has a drive system and at least one effector. The system's processing unit determines a task to be executed by the robot and controls the drive system and the effector according to the task. The processing unit automatically retrieves action definition candidates from a database, evaluates the retrieved action definition candidates with respect to a success score indicating a likelihood that an action according to the action definition candidate contributes to successfully fulfilling the task, executes an action according to the action definition candidate having the highest probability equal to or above a predefined threshold, and sends a request for assistance via a communication interface if the success score is less than a preset threshold. The system will learn from instructions and additional information received in response to such request.

An operation model construction system includes a data acquisition unit that acquires operating data and external environment data of a moving object, an associated data accumulation unit that accumulates associated data obtained by classifying the external environment data into plural items and associating the operating data with the respective items, and an operation model construction unit that constructs plural operation models to operate the moving object, based on the associated data.

A method for obstacle avoidance in degraded environments of robots based on intrinsic plasticity of an SNN is disclosed. A decision network in a synaptic autonomous learning module takes lidar data, distance from a target point and velocity at a previous moment as state input, and outputs the velocity of left and right wheels of the robot through the autonomous adjustment of the dynamic energy-time threshold, so as to carry out autonomous perception and decision making. The method solves the difficulty of the lack of intrinsic plasticity in the SNN, which leads to the difficulty of adapting to degraded environments due to the homeostasis imbalance of the model, is successfully deployed in mobile robots to maintain a stable trigger rate for autonomous navigation and obstacle avoidance in degraded, disturbed and noisy environments, and has validity and applicability on different degraded scenes.

The present disclosure is directed to a network of autonomous vehicles, such as autonomous ground vehicles (“AGVs”) that deliver items to and/or from a destination location and/or perform a service. For example, a community (e.g., neighborhood, apartment complex) may include a plurality of autonomous vehicles that deliver payloads to different locations (e.g., homes, apartments) within the community for use or consumption. Some of the payloads are community items (e.g., microwave, stove top, cooking utensils) that are shared by members of the community and transferred between locations within the community by autonomous vehicles.