Method, apparatus, device, and storage medium for controlling guide robot

1. A method for controlling a guide robot, comprising:

acquiring a state of the guide robot, a state of a user, and a position of an obstacle, the guide robot and the user being connected with a rigid object;

generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user;

generating a collision-free global path based on the position of the obstacle;

generating a control command based on the state update equation for the combined system and the collision-free global path; and

driving the guide robot to move based on the control command,

wherein the acquiring a state of a user comprises:

determining an annular region with the guide robot as a center of a circle and the length of the rigid object as a radius for use as a candidate position region of the user; and

clustering a scanned point in the candidate position region using a k-means clustering algorithm to obtain a position of the user.

2. The method according to claim 1 , wherein the generating a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user comprises:

generating a state update equation for the guide robot based on the state of the guide robot;

generating a state update equation for the user based on the state of the guide robot, the state of the user, and a length of the rigid object;

generating a state update matrix by combining the state update equation for the guide robot and the state update equation for the user; and

generating the state update equation for the combined system based on the state update matrix.

3. The method according to claim 2 , wherein the generating a collision-free global path based on the position of the obstacle comprises:

regionalizing a map to generate a global grid map;

marking occupancies of grids in the global grid map based on the position of the obstacle, to determine an available grid;

finding a shortest path from a grid corresponding to an initial position to a grid corresponding to a target position in the available grid using a graph search technique; and

interpolating a point between neighbouring points on the shortest path to generate the collision-free global path.

4. The method according to claim 2 , wherein the generating a control command based on the state update equation for the combined system and the collision-free global path comprises:

designing a cost function and a collision-free constraint using a model predictive control technology to compute a collision-free local motion control quantity, wherein the cost function is used for penalizing errors of states and control effects, and the collision-free constraint is linearized using sequential convex optimization at each iteration.

5. The method according to claim 2 , wherein the acquiring a state of the guide robot comprises:

acquiring a scanned point scanned by a lidar mounted on the guide robot; and

positioning the guide robot using a particle filtering technology in combination with the scanned point and an occupancy map.

6. The method according to claim 2 , wherein the acquiring a position of an obstacle comprises:

acquiring a scanned point scanned by a lidar mounted on the guide robot; and

comparing the scanned point with an occupancy map to obtain the position of the obstacle.

7. The method according to claim 1 , wherein the generating a collision-free global path based on the position of the obstacle comprises:

regionalizing a map to generate a global grid map;

marking occupancies of grids in the global grid map based on the position of the obstacle, to determine an available grid;

finding a shortest path from a grid corresponding to an initial position to a grid corresponding to a target position in the available grid using a graph search technique; and

interpolating a point between neighbouring points on the shortest path to generate the collision-free global path.

8. The method according to claim 1 , wherein the generating a control command based on the state update equation for the combined system and the collision-free global path comprises:

designing a cost function and a collision-free constraint using a model predictive control technology to compute a collision-free local motion control quantity, wherein the cost function is used for penalizing errors of states and control effects, and the collision-free constraint is linearized using sequential convex optimization at each iteration.

9. The method according to claim 8 , wherein the designing a cost function and a collision-free constraint using a model predictive control technology to compute a collision-free local motion control quantity comprises:

selecting a check point from the guide robot and the user;

establishing a local grid map centered on the check point;

marking occupancies of grids in the local grid map based on the position of the obstacle, to determine a feasible region of the check point;

determining a local target state based on the collision-free global path; and

generating the collision-free local motion control quantity based on the feasible region and the local target state.

10. The method according to claim 1 , wherein the acquiring a state of the guide robot comprises:

acquiring a scanned point scanned by a lidar mounted on the guide robot; and

positioning the guide robot using a particle filtering technology in combination with the scanned point and an occupancy map.

11. The method according to claim 1 , wherein the acquiring a position of an obstacle comprises:

acquiring a scanned point scanned by a lidar mounted on the guide robot; and

comparing the scanned point with an occupancy map to obtain the position of the obstacle.

12. The method according to claim 11 , wherein the comparing the scanned point with the occupancy map to obtain the position of the obstacle comprises:

determining neighbouring pixels of the scanned point in the occupancy map;

determining, in response to a pixel among the neighbouring pixels being occupied status, that the scanned point is not an obstacle point; and

determining, in response to no pixel among the neighbouring pixels being occupied status, that the scanned point is the obstacle point.

13. An electronic device, comprising:

one or more processors; and

a storage apparatus, storing one or more programs thereon,

the one or more programs, when executed by the one or more processors, causing the one or more processors to:

acquire a state of a guide robot, a state of a user, and a position of an obstacle, the guide robot and the user being connected with a rigid object;

generate a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user;

generate a collision-free global path based on the position of the obstacle;

generate a control command based on the state update equation for the combined system and the collision-free global path; and

drive the guide robot to move based on the control command,

wherein the state of the user is acquired by:

determining an annular region with the guide robot as a center of a circle and the length of the rigid object as a radius for use as a candidate position region of the user; and

clustering a scanned point in the candidate position region using a k-means clustering algorithm to obtain a position of the user.

14. The electronic device according to claim 13 , wherein to generate a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user comprises:

generating a state update equation for the guide robot based on the state of the guide robot;

generating a state update equation for the user based on the state of the guide robot, the state of the user, and a length of the rigid object;

generating a state update matrix by combining the state update equation for the guide robot and the state update equation for the user; and

generating the state update equation for the combined system based on the state update matrix.

15. The electronic device according to claim 13 , wherein to generate a collision-free global path based on the position of the obstacle comprises:

regionalizing a map to generate a global grid map;

marking occupancies of grids in the global grid map based on the position of the obstacle, to determine an available grid;

finding a shortest path from a grid corresponding to an initial position to a grid corresponding to a target position in the available grid using a graph search technique; and

interpolating a point between neighbouring points on the shortest path to generate the collision-free global path.

16. The electronic device according to claim 13 , wherein to generate a control command based on the state update equation for the combined system and the collision-free global path comprises:

designing a cost function and a collision-free constraint using a model predictive control technology to compute a collision-free local motion control quantity, wherein the cost function is used for penalizing errors of states and control effects, and the collision-free constraint is linearized using sequential convex optimization at each iteration.

17. The electronic device according to claim 16 , wherein the designing a cost function and a collision-free constraint using a model predictive control technology to compute a collision-free local motion control quantity comprises:

selecting a check point from the guide robot and the user;

establishing a local grid map centered on the check point;

marking occupancies of grids in the local grid map based on the position of the obstacle, to determine a feasible region of the check point;

determining a local target state based on the collision-free global path; and

generating the collision-free local motion control quantity based on the feasible region and the local target state.

18. A non-transitory computer-readable medium, storing a computer program thereon, wherein the computer program, when executed by a processor, to:

acquire a state of the guide robot, a state of a user, and a position of an obstacle, the guide robot and the user being connected with a rigid object;

generate a state update equation for a combined system of the guide robot and the user based on the state of the guide robot and the state of the user;

generate a collision-free global path based on the position of the obstacle;

generate a control command based on the state update equation for the combined system and the collision-free global path; and

drive the guide robot to move based on the control command,

wherein the state of the user is acquired by:

determining an annular region with the guide robot as a center of a circle and the length of the rigid object as a radius for use as a candidate position region of the user; and

clustering a scanned point in the candidate position region using a k-means clustering algorithm to obtain a position of the user.