CN113065694A - Tactical action rule intelligent routing algorithm based on immersive human-computer interaction simulation system - Google Patents
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Abstract
The invention discloses a tactical action rule intelligent routing algorithm based on an immersive human-computer interaction simulation system, which comprises the following steps: step 1: summarize the AI's needs into actions, decisions and strategies; step 2: constructing a model base, a knowledge base and an action rule base according to the characteristics of the simulation object; and step 3: for a group of vehicles in the three-dimensional simulation battlefield environment, constructing a model base, a knowledge base and an action rule base according to the characteristics of a simulation object; and 4, step 4: dividing the control behaviors of the squad group into: split, queue, and aggregate; and 5: the individual's steering behavior is divided into: approaching, departing, arriving, pursuing, evading, wandering randomly, following and avoiding obstacles; step 6: combining the control behaviors of the teams with the control behaviors of the individuals; and 7: and dynamically planning the path of the action of the individual at an action layer. The invention realizes dynamic path navigation, improves the richness of actions, meets various teams' tactics, and can adapt to the changing terrain.
Description
Technical Field
The invention relates to the field of military training equipment, in particular to a tactical action rule intelligent routing algorithm based on an immersive human-computer interaction simulation system.
Background
In order to meet military training requirements, troops are equipped with various army tactic simulation training devices of army equipment, the path searching and navigation algorithm of an artificial intelligence body in the existing simulation training device software system adopts a pre-path baking mode, the navigation path is single, tactic team shapes are avoided, actions such as roundabout, packet copy, concealment and the like based on tactic rules are avoided, the troops can not well meet the requirement of performing troop tactic training or tactic deduction, and after the terrain changes, the path can not be used any more, the navigation path baking is needed again, and the troops are not favorable for rapidly forming fighting capacity.
Disclosure of Invention
In order to solve the problems, the invention aims to provide an intelligent tactical action rule routing algorithm based on an immersive human-computer interaction simulation system, which can realize dynamic path navigation, has a complete tactical formation and rich actions, can meet various tactics of teams, and can adapt to a changed terrain.
In order to achieve the purpose, the invention adopts the technical scheme that: an intelligent tactical action rule routing algorithm based on an immersive human-computer interaction simulation system comprises the following steps:
step 1: the AI requirements are summarized with three basic capabilities:
and (4) action: the ability of the vehicle to operate;
and (3) decision making: ability to decide how to act;
strategy: ability to tactical rule thinking;
the three basic abilities divide the behavior of the AI into three layers, namely an action layer, a decision layer and a strategy layer; the algorithm used by the action and decision layer is for individuals, and the algorithm used by the strategy layer is for team groups;
step 2: constructing a model base, a knowledge base and an action rule base corresponding to the real world simulation object according to the characteristics of the real world simulation object;
and step 3: for a group of vehicles in the three-dimensional simulation battlefield environment, constructing a model base, a knowledge base and an action rule base corresponding to the characteristics of a real world simulation object according to the characteristics of the real world simulation object;
and 4, step 4: dividing the control behaviors of the squad group into: split, queue, and aggregate;
and 5: the individual's steering behavior is divided into: approaching, departing, arriving, pursuing, evading, wandering randomly, following and avoiding obstacles;
step 6: combining the control behaviors of the squad with the control behaviors of the individuals, and controlling the mutual exclusion problem among the individual behaviors through state identification, wherein the control logic is realized in a decision layer by adopting an FSM (finite state machine);
and 7: dynamically planning the path of the action of the individual on an action layer; the algorithm for dynamic planning of the path employs D × Lite.
The tactical action rule intelligent routing algorithm based on the immersive human-computer interaction simulation system realizes dynamic path navigation, has complete tactical formation, improves action richness, meets various sub-formation tactics of troops, and can adapt to changed terrain.
Drawings
FIG. 1 is a schematic diagram of the AI architecture model of the present invention.
FIG. 2 is a schematic diagram of the algorithm operation principle of the present invention.
FIG. 3 is an algorithmic work flow diagram of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1 and fig. 2, an intelligent tactical action rule routing algorithm based on an immersive human-computer interaction simulation system includes the following steps:
step 1: the AI requirements are summarized with three basic capabilities:
and (4) action: the ability of the vehicle to operate;
and (3) decision making: ability to decide how to act;
strategy: ability to tactical rule thinking;
the three basic abilities divide the behavior of the AI into three layers, namely an action layer, a decision layer and a strategy layer; the algorithm used by the action and decision layer is for individuals, and the algorithm used by the strategy layer is for team groups;
step 2: constructing a model base, a knowledge base and an action rule base corresponding to the real world simulation object according to the characteristics of the real world simulation object;
and step 3: for a group of vehicles in the three-dimensional simulation battlefield environment, constructing a model base, a knowledge base and an action rule base corresponding to the characteristics of a real world simulation object according to the characteristics of the real world simulation object;
and 4, step 4: dividing the control behaviors of the squad group into: split, queue, and aggregate;
and 5: the individual's steering behavior is divided into: approaching, departing, arriving, pursuing, evading, wandering randomly, following and avoiding obstacles;
step 6: combining the control behaviors of the squad with the control behaviors of the individuals, and controlling the mutual exclusion problem among the individual behaviors through state identification, wherein the control logic is realized in a decision layer by adopting an FSM (finite state machine);
and 7: dynamically planning the path of the action of the individual on an action layer; the algorithm for dynamic planning of the path employs D × Lite.
Principle of D × Lite algorithm: according to the known environment information, the unknown part is regarded as a free space, a global optimal path from the target point to the starting point is planned, namely, a 'path field' information is established, and a preferential basis is provided for the increment to approach the target point. The algorithm D is reverse-search, so g(s), h(s) have new definitions, i.e. representing the cost value from the target point to the current s point and the heuristic value from the current s point to the starting point, respectively. g records the front nodes of the grid nodes and has the calculation formula of
rhs(s) records g(s) of successor nodes of the grid node, with the formula:
d _ star Lite also introduces k(s) values for comparison when evaluating the evaluation values of grid points, where k(s) comprises two values [ k (s 1); k (s2) ], satisfying the following equations, respectively:
k1(s)＝min(g(s)，rhs(s))+h(sstart，S)
k2(s)＝min(g(s)，rhs(s))
the following formula can be derived:
the invention adopts an intelligent routing algorithm based on tactical action rules of an immersive human-computer interaction simulation system to realize the intelligent routing of an artificial intelligent agent, and the routing is searched from a target position to an initial position. When a new obstacle exists in the path, the new obstacle does not affect the path to the target for the path nodes in the range from the target position to the new obstacle.
The algorithm flow of the invention is shown in fig. 3, and comprises 6 steps:
step 1: assuming that the unknown regions are all free spaces, path planning is incrementally implemented on the basis of this, and heuristic values h from the starting point to the target point E3 are calculated.
Step 2: the shortest distance of the target point to each node is found by minimizing the value of rhs.
And step 3: when the moving vehicle moves forward according to the planned path, the position point to which the moving vehicle arrives is set as a starting point.
And 4, step 4: when the path changes or the key value needs to be updated, the heuristic value from the target point to the new starting point and the path consumption are updated.
And 5: as the moving vehicle is continuously close to the target point, the consumption of the endpoint is corrected according to the current position of the vehicle, and the heuristic value of the position point is continuously reduced.
Step 6: and smoothing the path according to the vehicle speed by adopting a difference algorithm.
Claims (1)
1. An intelligent tactical action rule routing algorithm based on an immersive human-computer interaction simulation system is characterized by comprising the following steps:
step 1: the AI requirements are summarized with three basic capabilities:
and (4) action: the ability of the vehicle to operate;
and (3) decision making: ability to decide how to act;
strategy: ability to tactical rule thinking;
the three basic abilities divide the behavior of the AI into three layers, namely an action layer, a decision layer and a strategy layer; the algorithm used by the action and decision layer is for individuals, and the algorithm used by the strategy layer is for team groups;
step 2: constructing a model base, a knowledge base and an action rule base corresponding to the real world simulation object according to the characteristics of the real world simulation object;
and step 3: for a group of vehicles in the three-dimensional simulation battlefield environment, constructing a model base, a knowledge base and an action rule base corresponding to the characteristics of a real world simulation object according to the characteristics of the real world simulation object;
and 4, step 4: dividing the control behaviors of the squad group into: split, queue, and aggregate;
and 5: the individual's steering behavior is divided into: approaching, departing, arriving, pursuing, evading, wandering randomly, following and avoiding obstacles;
step 6: combining the control behaviors of the squad with the control behaviors of the individuals, and controlling the mutual exclusion problem among the individual behaviors through state identification, wherein the control logic is realized in a decision layer by adopting an FSM (finite state machine);
and 7: dynamically planning the path of the action of the individual on an action layer; the algorithm for dynamic planning of the path employs D × Lite.
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