An autonomously moving transport system comprising a control apparatus, an obstacle recognition device and a drive unit, wherein the drive unit is configured to move the autonomously moving transport system along a travel route with a specific travel parameter. The obstacle recognition device is configured to detect an object in a monitored zone and to transmit corresponding object information to the control apparatus. The control apparatus is configured to divide the monitored zone into a travel corridor and at least one first secondary corridor. The control apparatus is configured to determine, based on the object information, whether the detected object is located in the travel corridor or in the at least one first secondary corridor. The control apparatus is configured to adapt a travel parameter differently when the object is in the first secondary corridor than when the object is located in the travel corridor.

An autonomously moving transport system comprising a control apparatus, an obstacle recognition device and a drive unit, wherein the drive unit is configured to move the autonomously moving transport system along a travel route with a specific travel parameter. The obstacle recognition device is configured to detect an object in a monitored zone and to transmit corresponding object information to the control apparatus. The control apparatus is configured to divide the monitored zone into a travel corridor and at least one first secondary corridor. The control apparatus is configured to determine, based on the object information, whether the detected object is located in the travel corridor or in the at least one first secondary corridor. The control apparatus is configured to adapt a travel parameter differently when the object is in the first secondary corridor than when the object is located in the travel corridor.

The present invention provides a robot dynamic obstacle avoidance method based on a multimodal spiking neural network. The present invention realizes a robot obstacle avoidance method in a dynamic environment by fusing laser radar data and processed event camera data and combining with the intrinsic learnable threshold of the spiking neural network for a scenario comprising dynamic obstacles. It solves the difficulty of failure of obstacle avoidance due to the difficulty in perceiving the dynamic obstacles in the obstacle avoidance task of a robot. The present invention helps the robot to fully perceive the static information and the dynamic information of the environment, uses the learnable threshold mechanism of the spiking neural network for efficient reinforcement learning training and decision making, and realizes autonomous navigation and obstacle avoidance in the dynamic environment. An event data enhanced model is combined to better adapt to the dynamic environment for obstacle avoidance.

A moving object detection system and method is provided. The system includes an input module capturing a point cloud comprising measurements of distances to points on one or more objects and a detection module receiving the point cloud captured by the input module and configured to determine whether the objects are moving objects. The determination of moving objects is performed by determining whether currently measured points occlude any previously measured points, and/or whether the currently measured points recursively occlude any previously measured points, and/or whether the currently measured points are recursively occluded by any previously measured points.

A moving object detection system and method is provided. The system includes an input module capturing a point cloud comprising measurements of distances to points on one or more objects and a detection module receiving the point cloud captured by the input module and configured to determine whether the objects are moving objects. The determination of moving objects is performed by determining whether currently measured points occlude any previously measured points, and/or whether the currently measured points recursively occlude any previously measured points, and/or whether the currently measured points are recursively occluded by any previously measured points.

A control system controls a fleet of autonomous vehicles which are adapted to travel along driving paths in an area. The fleet of vehicles comprises at least two vehicles, each vehicle utilizes a first and a second driving behaviour when driving along a driving path. The first driving behaviour is associated with driving closer to an edge of a road section, in relation to the second driving behaviour, which is associated with driving further away from the edge and closer to a center line of the road section. The control system predicts if a meeting between the at least two vehicles along the road section will occur, and when it is predicted that the meeting will occur, command at least one of the at least two vehicles to utilize the first driving behaviour to thereby allow and/or facilitate for the vehicles to pass each other along the road section.

A control system controls a fleet of autonomous vehicles which are adapted to travel along driving paths in an area. The fleet of vehicles comprises at least two vehicles, each vehicle utilizes a first and a second driving behaviour when driving along a driving path. The first driving behaviour is associated with driving closer to an edge of a road section, in relation to the second driving behaviour, which is associated with driving further away from the edge and closer to a center line of the road section. The control system predicts if a meeting between the at least two vehicles along the road section will occur, and when it is predicted that the meeting will occur, command at least one of the at least two vehicles to utilize the first driving behaviour to thereby allow and/or facilitate for the vehicles to pass each other along the road section.

A method in accordance with at least some embodiments of the present technology includes determining first hazard information about a human in an environment at a first time. The method further includes decelerating a mobile robot in the environment based at least partially on the first hazard information. The method further includes determining second hazard information about the human at a second time after the first time. The method further includes reconfiguring the mobile robot based at least partially on the second hazard information. Reconfiguring the mobile robot includes moving the mobile robot from a standing configuration to a non-standing configuration. The method further includes determining third hazard information about the human at a third time after the second time. Finally, the method includes causing a safe operating stop of the mobile robot based at least partially on the third hazard information.

A method in accordance with at least some embodiments of the present technology includes determining first hazard information about a human in an environment at a first time. The method further includes decelerating a mobile robot in the environment based at least partially on the first hazard information. The method further includes determining second hazard information about the human at a second time after the first time. The method further includes reconfiguring the mobile robot based at least partially on the second hazard information. Reconfiguring the mobile robot includes moving the mobile robot from a standing configuration to a non-standing configuration. The method further includes determining third hazard information about the human at a third time after the second time. Finally, the method includes causing a safe operating stop of the mobile robot based at least partially on the third hazard information.

A method in accordance with at least some embodiments of the present technology includes determining first hazard information about a human in an environment at a first time. The method further includes decelerating a mobile robot in the environment based at least partially on the first hazard information. The method further includes determining second hazard information about the human at a second time after the first time. The method further includes reconfiguring the mobile robot based at least partially on the second hazard information. Reconfiguring the mobile robot includes moving the mobile robot from a standing configuration to a non-standing configuration. The method further includes determining third hazard information about the human at a third time after the second time. Finally, the method includes causing a safe operating stop of the mobile robot based at least partially on the third hazard information.