Path planning and navigation for a mobile machine is disclosed. A path planning method plans a path for the mobile machine having a plurality of sensors by: receiving, from each of the sensors of the mobile machine, sensor data; creating, based on the received sensor data from each of the sensors, a plurality of local sensor layers each corresponding to the received sensor data from each of the sensors; creating a local map by integrating all the created local sensor layers; inflating the local map; creating a global costmap by fusing an inflated global map and the inflated local map; planning, according to the costmap, the path for navigating the mobile machine; and providing the planned path to the mobile machine for navigating the mobile machine using the planned path.

A method may include obtaining an artificial intelligence (AI) model configured to identify series of tasks to be performed by a robot in accordance with general parameters. The method may include obtaining data indicating a particular parameter corresponding to a particular environment. The method may include identifying, using the AI model, the particular parameter as corresponding to the particular environment. The particular parameter may be used by the AI model to identify the series of tasks to be performed by the robot such that the series of tasks are performed in accordance with the general and the particular parameters. The method may include identifying, using the AI model and the particular parameter, a series of tasks to be performed by the robot to complete an operation in the particular environment. The method may include causing the robot to autonomously perform the series of tasks in the particular environment.

An autonomous moving system according to the present disclosure is an autonomous moving system including an autonomous moving body that moves autonomously. The autonomous moving system includes a control unit that executes control of movement of the autonomous moving body including collision control, a setting unit that sets a defense space around the autonomous moving body, for executing the collision control, a detecting unit that detects obstructions in a vicinity of the autonomous moving body, and a classifying unit that classifies obstructions that are detected. The setting unit changes a range of the defense space, based on the obstructions that are classified by the classifying unit, and the control unit executes control of movement of the autonomous moving body including the collision control in at least one of when the obstruction is inside the defense space and when the obstruction is predicted to enter the defense space.

A mobile robot control system according to an embodiment is a mobile robot control system configured to control a mobile robot, a shape of the mobile robot having long sides and short sides in a top view, and the mobile robot control system including: an area setting unit configured to set a virtual bumper area defined by a first area and a second area, the first area being defined by a plurality of circles arranged along the long-side direction and the second area being formed by moving the first area in accordance with a moving speed of the mobile robot; and a control unit configured to control the mobile robot so as to decelerate or stop when the nearby object enters the virtual bumper area. Control may be performed using a machine learning model generated by supervised learning or the like.

A control system according to the present disclosure performs system control for controlling a system including a mobile robot that is autonomously movable and operable by a user. The mobile robot includes at least one light-emitting unit. The system control includes mode switch control of switching between an autonomous movement mode and a user operation mode, and the light emission control of controlling the light-emitting unit to emit light in different light emission patterns associated with a plurality of predetermined conditions. The light emission control includes controlling the light-emitting unit to emit light in different light emission patterns, for at least one of the predetermined conditions, depending on whether the mobile robot is in the autonomous movement mode or the user operation mode.