Amazon Technologies, Inc.

Nissan North America, Inc.

Renault S.a.s.

Samer Nashed

Joseph W Durham

David Ilstrup

Anatoly Mitlin

Jong Jin Park

Embodiments herein describe a robotic system that uses range sensors to identify a vector map of an environment. The vector map includes lines that outline the shape of objects in the environment (e.g., shelves on the floor of a warehouse). The system identifies one or more line segments representing the boundary or outline of the objects in the environment using range data acquired by the range sensors. The robotic system can repeat this process at different locations as it moves in the environment. Because of errors and inaccuracies, line segments formed at different locations may not clearly align even when these line segments correspond to the same object. To account for this error, the robotic system match line segments identified at a first location with line segments identified at a second location. The matched line segments can be merged into a line that is stored in the vector map.

Physical models for hierarchical clustering and symbolic inference

An autonomous robot system to enable automated movement of goods and materials in a dynamic environment including one or more dynamic objects. The autonomous robot system includes an autonomous ground vehicle (AGV) including a vehicle management system. The vehicle management system provides real time resource planning and path optimization to enable the AGV to operate safely and efficiently alongside humans in a dynamic environment. The vehicle management system includes one or more processing devices to execute a moving object trajectory prediction module to predict a trajectory of a dynamic or moving object in a shared environment.

Autonomous machine motion planning in a dynamic environment

Traversing a vehicle transportation network, by a vehicle, may include determining vehicle operational information, determining a metric location estimate for the vehicle using the vehicle operational information, determining operational environment information of a portion of the vehicle transportation network, determining a topological location estimate for the vehicle within the vehicle transportation network using the metric location estimate and the operational environment information, and traversing the vehicle transportation network based on the topological location estimate for the vehicle. The operational environment information can include sensor data of a portion of the vehicle transportation network that is observable to the vehicle. To determine the metric location estimate, a non-linear loss function with a Kalman filter may mitigate effects of un-modeled sensor error(s). Techniques using Hidden Markov Models and the Earth Mover's Distance to determine the topological location estimate are also described.

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Amherst, MA, United States of America

Samer Nashed