Five Ai Limited

Subramanian Ramamoorthy

John Redford

Majd Hawasly

Morris Antonello

Francisco Eiras

Svetlin Valentinov Penkov

Simon Lyons

Alexandre Oliveira E Silva

Alejandro Bordallo Mico

Francisco Maria Girbal Eiras

Andrew Blake

Roberto Antolin

Stefano Albrecht

Mihai Dobre

Svetlin-Valentinov Penkov

Rik Sarkar

Svet Penkov

Henry Pulver

Ludovico Carozza

A computer-implemented method of determining control actions for controlling a mobile robot comprises: receiving a set of scenario description parameters describing a scenario and a desired goal for the mobile robot therein; in a first constrained optimization stage, applying a first optimizer to determine a first series of control actions that substantially globally optimizes a preliminary cost function for the scenario, the preliminary cost function based on a first computed trajectory of the mobile robot, as computed by applying a preliminary robot dynamics model to the first series of control actions, and in a second constrained optimization stage, applying a second optimizer to determine a second series of control actions that substantially globally optimizes a full cost function for the scenario, the full cost function based on a second computed trajectory of the mobile robot, as computed by applying a full robot dynamics model to the second series of control actions; wherein initialization data of at least one of the first computed trajectory and the first series of control actions is used to initialize the second optimizer for determining the second series of control actions, and wherein the preliminary robot dynamic model approximates the full robot dynamics model, the cost functions embody similar objectives to each encourage achievement of the desired goal, and both are optimized with respect to similar hard constraints, such that the initialization data guides the second optimizer to the substantially globally-optimal second series of control actions.

Planning in mobile robots

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

One aspect herein provides a method of analysing driving behaviour in a data processing computer system, the method comprising: receiving at the data processing computer system driving behaviour data to be analysed, wherein the driving behaviour data records vehicle movements within a monitored driving area; analysing the driving behaviour data to determine a normal driving behaviour model for the monitored driving area; using object tracking to determine driving trajectories of vehicles driving in the monitored driving area; comparing the driving trajectories with the normal driving behaviour model to identify at least one abnormal driving trajectory; and extracting a portion of the driving behaviour data corresponding to a time interval associated with the abnormal driving trajectory.

Driving scenarios for autonomous vehicles

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

The invention provides a computer-implemented method of planning a path for a mobile robot such as an autonomous vehicle in the presence of K obstacles. The method uses, for each of the K obstacles, a shape Bk and a density function pk(x) representing the probabilistic position of the obstacle. The method repeats the following steps for at least two different paths A: &#x2014;choosing a path A, where A is the swept area of the robot within a given time interval; and&#x2014;calculating based on the density function of each obstacle and the swept path an upper bound on the total probability of at least one collision Fbetween the robot and the K obstacles. This allows a number of candidate paths to be ranked for safety. By precomputing factors of the computational steps over K obstacles, the computation per path is O(N), and not O(NK). A safety threshold can be used to filter out paths below that threshold. An operating path to control the robot can be selected from the remaining paths, optionally based on other factors such as comfort or efficiency.

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Bristol, United Kingdom

Subramanian Ramamoorthy