Introduces of robot soccer’s competition software of Harbin Institute of Technology (HIT), the concept of running range and the method of calculating the running range for both the opponent and our teammates accordin...Introduces of robot soccer’s competition software of Harbin Institute of Technology (HIT), the concept of running range and the method of calculating the running range for both the opponent and our teammates according to the distances between the ball and robot soccers, and therefore the method of calculating the angle that the robot passes or shoots the ball according to the running ranges of both sides. And gives the examples of passing the ball when the ball’s position is in the backcourt and shooting the ball when the ball’s position is in the frontcourt.展开更多
A new ball passing strategy for robot soccer is proposed in this paper. With introduce of a new algorithm on ball passing, the optimum strategy is confirmed to be more efficient and exact when passing a ball. Question...A new ball passing strategy for robot soccer is proposed in this paper. With introduce of a new algorithm on ball passing, the optimum strategy is confirmed to be more efficient and exact when passing a ball. Questions of role switching in multi-intelligent agent cooperation in robot soccer are described based on Generalized Stochastic Petri-Net （GSPN）. Results of computer simulation have confirmed the feasibility and efficiency of above Petri-net method.展开更多
<strong>Introduction</strong><strong>:</strong> The key to success is finding the perfect mixture of tactical patterns and sudden breaks of them, which depends on the behavior of the opponent t...<strong>Introduction</strong><strong>:</strong> The key to success is finding the perfect mixture of tactical patterns and sudden breaks of them, which depends on the behavior of the opponent team and is not easy to estimate by just watching matches. According to the specific tactical team behavior of “attack vs. defense” professional football matches are investigated based on a simulation approach, professional football matches are investigated according to the specific tactical team behavior of “attack vs. defense.” <strong>Methods:</strong> The formation patterns of all the sample games are categorized by SOCCER<span style="white-space:nowrap;">©</span> for defense and attack. Monte Carlo-Simulation can evaluate the mathematical, optimal strategy. The interaction simulation between attack and defense shows optimal flexibility rates for both tactical groups. <strong>Approach: </strong>A simulation approach based on 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and optimize such strategic team behavior in professional soccer. <strong>Results:</strong> The results revealed that both attack and defense have optimal planning rates to be more successful. The more complex the success indicator, the more successful attacking player groups get. The results also show that defensive player groups always succeed in attacking groups below a specific planning rate value.<strong> Conclusion:</strong> Groups are always succeeding. The simulation-based position data analysis shows successful strategic behavior patterns for attack and defense. Attacking player groups need very high flexibility to be successful (stay in ball possession). In contrast, defensive player groups only need to be below a defined flexibility rate to be guaranteed more success.展开更多
文摘Introduces of robot soccer’s competition software of Harbin Institute of Technology (HIT), the concept of running range and the method of calculating the running range for both the opponent and our teammates according to the distances between the ball and robot soccers, and therefore the method of calculating the angle that the robot passes or shoots the ball according to the running ranges of both sides. And gives the examples of passing the ball when the ball’s position is in the backcourt and shooting the ball when the ball’s position is in the frontcourt.
文摘A new ball passing strategy for robot soccer is proposed in this paper. With introduce of a new algorithm on ball passing, the optimum strategy is confirmed to be more efficient and exact when passing a ball. Questions of role switching in multi-intelligent agent cooperation in robot soccer are described based on Generalized Stochastic Petri-Net （GSPN）. Results of computer simulation have confirmed the feasibility and efficiency of above Petri-net method.
文摘<strong>Introduction</strong><strong>:</strong> The key to success is finding the perfect mixture of tactical patterns and sudden breaks of them, which depends on the behavior of the opponent team and is not easy to estimate by just watching matches. According to the specific tactical team behavior of “attack vs. defense” professional football matches are investigated based on a simulation approach, professional football matches are investigated according to the specific tactical team behavior of “attack vs. defense.” <strong>Methods:</strong> The formation patterns of all the sample games are categorized by SOCCER<span style="white-space:nowrap;">©</span> for defense and attack. Monte Carlo-Simulation can evaluate the mathematical, optimal strategy. The interaction simulation between attack and defense shows optimal flexibility rates for both tactical groups. <strong>Approach: </strong>A simulation approach based on 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and optimize such strategic team behavior in professional soccer. <strong>Results:</strong> The results revealed that both attack and defense have optimal planning rates to be more successful. The more complex the success indicator, the more successful attacking player groups get. The results also show that defensive player groups always succeed in attacking groups below a specific planning rate value.<strong> Conclusion:</strong> Groups are always succeeding. The simulation-based position data analysis shows successful strategic behavior patterns for attack and defense. Attacking player groups need very high flexibility to be successful (stay in ball possession). In contrast, defensive player groups only need to be below a defined flexibility rate to be guaranteed more success.
