Five Ai Limited

Svetlin Valentinov Penkov

Subramanian Ramamoorthy

Morris Antonello

Simon Lyons

Majd Hawasly

Alexandre Oliveira E Silva

Alejandro Bordallo Mico

Francisco Eiras

Francisco Maria Girbal Eiras

Andrew Blake

Roberto Antolin

Stefano Albrecht

Mihai Dobre

A computer-implemented method of planning a path for a mobile robot in the presence of a set of obstacles comprises: computing for each obstacle a probabilistic obstacle position distribution; computing at least one path-independent function as a combination of the probabilistic obstacle position distributions; and for at least one candidate path, determining an upper bound on the total probability of obstacle collision along that path, by aggregating the path-independent function based on an area defined by the candidate path and a mobile robot shape, wherein the path-independent function is independent of the candidate path.

Path planning in mobile robots

A computer-implemented method of predicting an external actor trajectory comprises receiving, at a computer, sensor inputs for detecting and tracking an external actor; applying object tracking to the sensor inputs, in order track the external actor, and thereby determine an observed trace of the external actor over a time interval; determining a set of available goals for the external actor; for each of the available goals, determining an expected trajectory model; and comparing the observed trace of the external actor with the expected trajectory model for each of the available goals, to determine a likelihood of that goal.

Autonomous vehicle planning and prediction

An autonomous vehicle (AV) planning method comprises: receiving sensor inputs pertaining to an AV; processing the AV sensor inputs to determine an encountered driving scenario; in an AV planner, executing a tree search algorithm to determine a sequence of AV manoeuvres corresponding to a path through a constructed game tree; and generating AV control signals for executing the determined sequence of AV manoeuvres; wherein the game tree has a plurality of nodes representing anticipated states of the encountered driving scenario, and the anticipated driving scenario state of each child node is determined by updating the driving scenario state of its parent node based on (i) a candidate AV manoeuvre and (ii) an anticipated behaviour of at least one external agent in the encountered driving scenario.

Autonomous vehicle planning

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Edinburgh, United Kingdom

Svetlin Valentinov Penkov