System and method for predicting trajectory of non-stationary obstacles in an aerial movement volume

A method for navigating an aerial robotic device (ARD) from a first location to a second location in an aerial movement volume, comprising:

• detecting, by an object detection module, one or more non-stationary objects in the aerial movement volume and generating a first object record detailing a location of the non-stationary object;
• comparing, by an object tracking module, the first object record with one or more second object records of one or more objects previously detected in the aerial movement volume and adding the location of the non-stationary object from the first object record to a first position of a tracking list of a substantially matching second object record, such that the tracking list includes a sequentially ordered list of current and previous trajectory points comprising a current location of corresponding non-stationary object and location of one or more previous detections of corresponding non-stationary object;
• updating, by a trajectory prediction module, a prediction list of the substantially matching second object record with one or more next trajectory points predicted over a pre-defined time period T, wherein N samples of a first object record are acquired at a pre-defined rate Δt over the pre-defined time period, and wherein for each current sample, the updating of the prediction list comprises:
• filtering out measurement noise in the current and previous trajectory points of the tracking list to generate a filtered tracking list of one or more filtered trajectory points;
• determining a velocity vector of corresponding object in the current sample based on the filtered trajectory points;
• determining an acceleration vector of corresponding object in the current sample based on the velocity vector of corresponding object in the current and previous samples;
• determining longitudinal and normal components of the acceleration vector in the current sample relative to the velocity vector in the current sample;
• determining an acceleration vector of the corresponding object in a next sample based on a magnitude of current longitudinal and normal components of the acceleration vector, and a phase of the velocity vector in the next sample;
• and
• predicting a trajectory point of the corresponding object in the next sample, based on the velocity and acceleration vectors predicted in the next sample;
• and
• navigating the ARD from the first location to the second location in a presence of a non-stationary object based on the updated prediction list of the non-stationary object.

A non-transitory computer readable medium configured to store a program causing a computer to navigate an aerial robotic device (ARD) from a first location to a second location in an aerial movement volume, said program configured to:

• detect a non-stationary object in the aerial movement volume, and generate a first object record detailing a location of the non-stationary object;
• compare the first object record with one or more second object records of one or more objects previously detected in the aerial movement volume, and add the location of the non-stationary object from the first object record to a first position of a tracking list of a substantially matching second object record, such that the tracking list includes a sequentially ordered list of current and previous trajectory points comprising a current location of corresponding non-stationary object and locations of one or more previous detections of the corresponding non-stationary object;
• filter out measurement noise in the current and previous trajectory points of the tracking list to generate a filtered tracking list of one or more filtered trajectory points;
• determine a velocity vector of corresponding object in the current sample based on filtered trajectory points of corresponding object in the current and previous samples;determine an acceleration vector of corresponding object in the current sample based on the velocity vector of corresponding object in the current and previous samples;
• determine longitudinal and normal components of the acceleration vector in the current sample relative to the velocity vector in the current sample;
• determine an acceleration vector of the corresponding object in a next sample based on magnitude of current longitudinal and normal components of the acceleration vector, and a phase of the velocity vector in the next sample;
• and
• predict a trajectory point of the corresponding object in the next sample, based on the velocity and acceleration vectors predicted in the next sample;
• and
• navigate the ARD from the first location to the second location in a presence of a non-stationary object based on the updated prediction list of the non-stationary object.