1. A path planning method for self-driving of autonomous system, which is executed by an agent, the method comprising following steps of:S1: acquiring a path optimization function of the agent based on the driving space of the agent, a speed of the agent, and a current position of the agent;

S2: converting, based on fixed-point theorems, the path optimization function of the agent into an equivalent fixed-point equation;

S3: acquiring a complete simplex sequence based on the fixed-point equation; and

S4: determining, based on the complete simplex sequence, an initial population size and an initial position of particles for particle swarm optimization to obtain the best path planning of the agent, and conducting the self-driving of the autonomous system according to the best path planning;

wherein the step S2 is specifically:

based on

constructing a fixed-point equation F(X)=X?f(X), wherein, according to the fixed-point theorems, f(X*)=0 if X* is a solution to the fixed-point equation, so that a minimum value of the path optimization function y=f(X) at the point X* is obtained;where:

f(X) is the path optimization function of the agent;

X is an n-dimensional optimization variable; and

gi(X) is m constraint functions in a function feasible region;

according to the fixed-point theorems, introducing an approximate fixed point to replace a precise fixed point: let any ?>0, considering X as an approximate fixed point of the f(X) if |X?f?(X)|
S301: dividing a search space of the fixed-point equation, specifically:

in an n-dimensional Euclidean space Rn, dividing a search space of the fixed-point equation into uniform polyhedrons by a n-family straight line xi=mhi (1=1,2, . . . ,n), where m is precision control;

S302: processing the divided search space by a simplicial algorithm to obtain simplexes, wherein:

for the Euclidean space Rn, N={1,2, . . . ,n}, ? is the permutation of N, and n basis vectors u1, . . . ,un of Rn, which are n columns in an identity matrix of order n, satisfy the following condition: u=u1+ . . . +un=(1, . . . ,1); let K10 be a set of integer points in Rn, if y0? K10, a n-dimensional simplex is denoted by k1(y0,?), where yi=yi?1+u?(i), i?N, and a set of k1(y0,?) is denoted by K1; and

S303: labeling the simplexes to output a complete simplex sequence, specifically:

labeling the simplexes by integer labeling or vector labeling, obtaining a complete simplex sequence satisfying labeling requirements according to a logistic discrimination, and using a value range of the complete simplex sequence as an updated search space;

wherein the step S4 is specifically:

in an n-dimensional solution space, if the position of each particle is a complete simplex sequence xi=(xi1,xi2, . . . ,xin), the number of complete simplex sequences represents the population size, the flying speed is vi=(vi1,vi2, . . . ,vin), the best position of individual particles is denoted by pbest=(pi1,pi2, . . . ,pin), the best global position is gbest=(pg1,pg2, . . . ,pgn), for the current particle, adjusting the current position xid and the current speed vid of this particle according to the following formulae:

where:

pid is the current best position of individual particles;

pgd is the current best global position;

vmax represents the maximum flying speed of particles;

?vmax represents the minimum flying speed of particles;

? is an inertia weight; and

c1 and c2 are acceleration constants.