Control of vehicle movement by application of geometric algebra and state and error estimation
A method and system for controlling movement of a vehicle. Movement, orientation, and position data of the vehicle is collected. A model of kinematics of the vehicle and its environment is created and a Theory of World model is produced and updated. The model includes geometric algebra multivectors. Errors and noise are stored as geometrically meaningful first-class objects within the multivectors. Geometric algebra operations are used to manipulate the model during operation. Error and noise data are propagated and manipulated using geometric algebra operations to reflect measurement and processing errors or noise. The models are used in generation of control data with a primary intent of ensuring stability. Operations such as intersections are used to compare position, orientation, and movement of the vehicle against position, orientation, and movement of objects in its environment. System tasks include, but are not limited to, kinematics, inverse kinematics, collision avoidance, and dynamics.
1. A method of controlling a vehicle having a chassis, an inertial measurement unit (IMU), a propulsion system, a transceiver, and a processor, the method comprising, via the processor:
obtaining vehicle inertia data via the IMU;
obtaining vehicle state data and vehicle control data via the transceiver, the vehicle control data corresponding to pilot inputs;
determining a control aspect of the vehicle control data, the control aspect being derived from control signals corresponding to the pilot inputs;
determining dynamic parameters of the vehicle based on at least one of the vehicle inertia data, vehicle state data, and vehicle control data;
incorporating the dynamic parameters and the control aspect into geometric algebra multivectors;
calculating a movement control decision based on the dynamic parameters and the control aspect via the geometric algebra multivectors;
generating a movement control instruction signal based on the movement control decision; and
controlling movement of the vehicle by sending the movement control instruction signal to the propulsion system such that the propulsion system effects the movement control decision.
2. The method of claim 1 , further comprising the step of determining at least one of errors and noise associated with the vehicle inertia data and the vehicle state data and propagating the at least one of errors and noise into the geometric algebra multivectors.
3. The method of claim 2 , wherein the errors are persistent errors.
4. The method of claim 1 , wherein the control aspect is a pilot-induced correction.
5. The method of claim 4 , wherein the pilot-induced correction counters an error in the dynamic parameters.
6. The method of claim 4 , wherein the control aspect is a pilot-induced right-stick correction.
7. The method of claim 6 , wherein the pilot-induced correction counters air movement drift.
8. The method of claim 4 , further comprising the step of determining at least one of errors and noise associated with the vehicle inertia data and the vehicle state data and propagating the at least one of errors and noise into the geometric algebra multivectors.
9. A control system for a vehicle having a propulsion system, an inertial measurement unit (IMU), and a transceiver configured to receive at least one of vehicle state data and vehicle control data, the vehicle control data corresponding to pilot inputs, the control system comprising:
a processor configured to:
obtain vehicle inertia data via the IMU;
obtain the at least one of vehicle state data and vehicle control data via the transceiver;
determine a control aspect of the vehicle control data, the control aspect being derived from control signals corresponding to the pilot inputs;
determine dynamic parameters of the vehicle based on at least one of the vehicle inertia data, vehicle state data, and vehicle control data;
incorporate the dynamic parameters and the control aspect into geometric algebra multivectors;
calculate a movement control decision based on the dynamic parameters and the control aspect via the geometric algebra multivectors;
generate a movement control instruction signal based on the movement control decision; and
control movement of the vehicle by sending the movement control instruction signal to the propulsion system such that the propulsion system effects the movement control decision.
10. The system of claim 9 , the processor being further configured to determine at least one of errors and noise associated with the vehicle inertia data and the vehicle state data and propagate the at least one of errors and noise into the geometric algebra multivectors.
11. The system of claim 9 , wherein the control aspect is a pilot-induced correction.
12. The system of claim 11 , wherein the pilot-induced correction counters an error in the dynamic parameters.
13. The system of claim 11 , wherein the control aspect is a pilot-induced right-stick correction.
14. The system of claim 13 , wherein the pilot-induced correction counters air movement-induced drift.
15. The system of claim 11 , the processor being further configured to determine at least one of errors and noise associated with the vehicle inertia data and the vehicle state data and propagate the at least one of errors and noise into the geometric algebra multivectors.
16. A vehicle comprising:
a propulsion system for propelling the vehicle;
an inertial measurement unit (IMU) configured to generate vehicle inertia data;
a proximity sensor configured to detect objects near the vehicle;
a camera configured to collect optical data near the vehicle;
a transceiver configured to receive vehicle state data and vehicle control data, the vehicle control data corresponding to pilot inputs; and
a processor configured to:
obtain the vehicle inertia data via the IMU, object proximity data via the proximity sensor, and the optical data from the camera;
receive vehicle state data and vehicle control data via the transceiver;
generate a single, consistent local predictive model based on the vehicle inertia data, the object proximity data, the optical data, the vehicle state data, and the vehicle control data;
determine a control aspect of the vehicle control data, the control aspect being derived from control signals corresponding to the pilot inputs;
determine dynamic parameters of the vehicle based on at least one of the vehicle inertia data, vehicle state data, and vehicle control data;
incorporate the dynamic parameters and the control aspect into geometric algebra multivectors;
calculate a movement control decision based on the dynamic parameters and the control aspect via the geometric algebra multivectors;
generate a movement control instruction signal based on the movement control decision; and
control movement of the vehicle by sending the movement control instruction signal to the propulsion system such that the propulsion system effects the movement control decision.
17. The vehicle of claim 16 , wherein the vehicle is an in-air vehicle.
18. The vehicle of claim 16 , wherein the vehicle is at least one of an on-the-ground vehicle, an on-water vehicle, an underwater vehicle, an under-ground vehicle, or an in-space vehicle.
19. The vehicle of claim 16 , wherein the vehicle is remotely controlled by an off-board pilot.