Advanced movement through vegetation with an autonomous vehicle

1. A method of automatically operating automated vehicles moving through vegetation obstacles with minimal damage, comprising:

using at least one processor for:

receiving at least one image depicting at least one vegetation obstacle blocking at least partially a path of an automated vehicle executing a mission;

analyzing the at least one image to extract at least one of a plurality of obstacle attributes of the at least one vegetation obstacle;

computing a plurality of movement patterns for operating the automated vehicle to cross the at least one vegetation obstacle based on at least one of a plurality of vehicle attributes of the automated vehicle with respect to at least one of the plurality of obstacle attributes, each of the plurality of movement patterns defining at least one of a plurality of movement parameters of the automated vehicle, at least one of the computed movement patterns comprising engaging the vegetation obstacle with at least one mechanical element of the automated vehicle, for clearing a way for the automated vehicle to cross;

selecting one of the plurality of movement patterns estimated to reduce a cost of damage to the automated vehicle and/or to the at least one vegetation obstacle;

outputting instructions for operating the automated vehicle to move through the at least one vegetation obstacle according to the selected movement pattern; and

in real-time, during a time period when the automated vehicle is moving through the at least one vegetation obstacle according to the selected movement pattern, capturing sensory data by at least one sensor deployed to monitor interaction between the automated vehicle and the at least one vegetation obstacle during said time period during which the automated vehicle is moving through the at least one vegetation obstacle and adjusting at least one of the plurality of movement parameters defined by the selected movement pattern according to the captured sensory data.

2. The method of claim 1 , wherein the automated vehicle is a member of a group consisting of: a ground vehicle, an aerial vehicle, and a naval vehicle.

3. The method of claim 1 , wherein crossing the at least one vegetation obstacle comprises: bending at least part of the at least one vegetation obstacle, breaking at least part of the at least one vegetation obstacle, and cutting at least part of the at least one vegetation obstacle.

4. The method of claim 1 , wherein each of the plurality of obstacle attributes is a member of a group consisting of: a type, a size, a length, a width, a height, a weight, a flexibility, and a position with respect to the path.

5. The method of claim 1 , wherein each of the plurality of vehicle attributes is a member of a group consisting of: a vehicle type, a vehicle structure, a speed range, a maneuverability, a material of at least one part of the automated vehicle, a location of at least one reinforced part of the automated vehicle, and a location of at least one vulnerable part of the automated vehicle.

6. The method of claim 1 , wherein each of the plurality of movement parameters is a member of a group consisting of: a movement direction, an approach path, an approach angle, a contact point, a vehicle orientation, a speed, an acceleration and an altitude.

7. The method of claim 1 , further comprising at least one of the plurality of movement patterns includes using at least one mechanical element of the automated vehicle to clear the at least one vegetation obstacle and/or part thereof, the at least one mechanical element comprising at least one member of a group consisting of: a mechanical arm, and a cutting element.

8. The method of claim 1 , wherein the mission comprises at least one agricultural operation and the at least one vegetation obstacle is formed by at least one crop producing plant.

9. The method of claim 1 , wherein the cost of damage further comprises a cost of damage to at least one another vehicle operated to cross the at least one vegetation obstacle following the automated vehicle.

10. The method of claim 1 , wherein the cost of damage further comprises a cost of recurring damage to at least one vehicle operated to cross the at least one vegetation obstacle multiple times during the mission.

11. The method of claim 1 , wherein the cost of damage further comprises a cost of recurring damage to at least one vehicle operated to cross the at least one vegetation obstacle in a plurality of future missions.

12. The method of claim 1 , further comprising:

predicting a number and/or type of vehicles estimated to cross the at least one vegetation obstacle, and

adjusting the cost of damage according to at least one vehicle attributes of the vehicles predicted to cross the at least one vegetation obstacle.

13. The method of claim 1 , wherein the cost of damage further comprises a cost of long term damage to vegetation infrastructure of the at least one vegetation obstacle, the long term damage includes loss of future crop.

14. The method of claim 1 , wherein the cost of damage is computed using at least one trained machine learning (ML) model configured to:

estimate a damage to the automated vehicle and/or to the at least one vegetation obstacle based on analysis of the at least one vehicle attribute of the automated vehicle with respect to the plurality of obstacle attributes, and

estimate a cost of the estimated damage.

15. The method of claim 1 , further comprising adjusting at least one of the plurality of movement patterns according to analysis of at least one additional image depicting at least one additional vegetation obstacle at least partially blocking the path of the automated vehicle further down the path beyond the at least one vegetation obstacle.

16. A system for automatically operating automated vehicles moving through vegetation obstacles with minimal damage, comprising:

at least one processor configured to execute a code, the code comprising:

code instructions to receive at least one image depicting at least one vegetation obstacle blocking at least partially a path of an automated vehicle executing a mission;

code instructions to analyze the at least one image to extract at least one of a plurality of obstacle attributes of the at least one vegetation obstacle;

code instructions to compute a plurality of movement patterns for operating the automated to cross the at least one vegetation obstacle based on at least one of a plurality of vehicle attributes of the automated vehicle with respect to at least one of the plurality of obstacle attributes, each of the plurality of movement patterns defining at least one of a plurality of movement parameters of the automated vehicle, at least one of the computed movement patterns comprising engaging the vegetation obstacle with at least one mechanical element of the automated vehicle, for clearing a way for the automated vehicle to cross;

code instructions to select one of the plurality of movement patterns estimated to reduce a cost of damage to the automated vehicle and/or to the at least one vegetation obstacle;

code instructions to output instructions for operating the automated vehicle to move through the at least one vegetation obstacle according to the selected movement pattern; and

code instructions to capture sensory data during a time period when the automated vehicle is moving through the at least one vegetation obstacle, by at least one sensor deployed to monitor interaction between the automated vehicle and the at least one vegetation obstacle during said time period during which the automated vehicle is moving through the at least one vegetation obstacle and to adjust at least one of the plurality of movement parameters defined by the selected movement pattern according to the captured sensory data.

17. A computer program product for automatically operating automated vehicles moving through vegetation obstacles with minimal damage, comprising:

at least one computer readable storage media having thereon:

first program instructions executable by at least one processor to cause the at least one processor to receive at least one image depicting at least one vegetation obstacle blocking at least partially a path of an automated vehicle executing a mission;

second program instructions executable by the at least one processor to cause the at least one processor to analyze the at least one image to extract at least one of a plurality of obstacle attributes of the at least one vegetation obstacle;

third program instructions executable by the at least one processor to cause the at least one processor to compute a plurality of movement patterns for operating the automated to cross the at least one vegetation obstacle based on at least one of a plurality of vehicle attributes of the automated vehicle with respect to at least one of the plurality of obstacle attributes, each of the plurality of movement patterns defines at least one of a plurality of movement parameters of the automated vehicle and at least one of the computed movement patterns comprising engaging the vegetation obstacle with at least one mechanical element of the automated vehicle, for clearing a way for the automated vehicle to cross;

fourth program instructions executable by the at least one processor to cause the at least one processor to select one of the plurality of movement patterns estimated to reduce a cost of damage to the automated vehicle and/or to the at least one vegetation obstacle;

fifth program instructions executable by the at least one processor to cause the at least one processor to output instructions for operating the automated vehicle to move through the at least one vegetation obstacle according to the selected movement pattern; and

sixth program instructions executable by the at least one processor to cause the at least one processor to capture sensory data during a time period when the automated vehicle is moving through the at least one vegetation obstacle, by at least one sensor deployed to monitor interaction between the automated vehicle and the at least one vegetation obstacle during said time period during which the automated vehicle is moving through the at least one vegetation obstacle and to adjust at least one of the plurality of movement parameters defined by the selected movement pattern according to the captured sensory data.