RTS Game Project

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RTS Game Project
Anton Larsson Martin Andersson Bo Isaksson Markus
Eliasson Fredrik Andersson
Martin Samuelsson Nebez Kassem Martin
Olsson Lukas Groschowski
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Project Info

• Conquer the Hood is a real time strategy game set
  in an urban ghetto-environment. You, the player,
  control one of the gangs fighting for dominance
  in the city.
• With heavy emphasis on role playing-style
  level-ups and experience use, the key to success
  is upgrading your troops and develop your
  leader-characters to fit your playing-style.

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The Gang

• The Gang
• 4 Leaders
• 3 Characters
• Goons
• Weapons for Leaders
• Tiger striped Dual guns
• Brass knuckles Machete/kukiri
• Grenades
• Sniper rifle
• Pimped lcd-equipped Mini gun
• Leopard spotted rocket launcher

• Weapons for Goons
• Baseball Bat
• Shotgun
• Rifle

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The playground

• The quadratic game-board consists out of a number
  of zones. Taking over these zones gives you
  experience, money and more Goons.
• One can choose to fortify these zones to gain
  advantage in battle. Symbols over the zone
  indicate what attributes it has. You can also
  move your spawn point.

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Game Modes

• Singleplayer (against AI players)
• Multiplayer (up to four players online)
• Maps (choose between city maps)

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Anton Larsson

• All inputs
• Insert new characters
• Statistics
• Winning conditions

• Gameplay with network
• Script
• Weapons

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Anton Larsson
Popupmenu

• PopupMenu
• Upgrades
• MiniMap

Preview
Stats
GoonInfo
WeaponShop
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Martin Andersson

• What was my goal
• Learn more about engine programming
• What Ive done
• Intergrating third party libraries (OGRE, OpenAL)
• Writing shaders
• Writing material scripts
• Conclusion
• Learned a lot
• The exporter is a troublemaker

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Bo Isaksson

• Unit AI
• The basic AI for the Units.
• Alignment is decided by the Team-AI.
• All units have a goal-queue in which they store
  their goals, so that they can continue with
  previous goals if they for example got distracted
  by an enemy they had to kill.
• Think
• The goal that's always in the bottom of the
  queue. The wanted goal of the unit is chosen.
• Attack
• Checks if enemy units are in the range and
  attacks the most appropriate, patrols the zones
  with the highest agg-value.
• Conquer
• Goes off to conquer the zones that the team
  prioritizes, modifies zone prio with group-id.
• Zone-prio group-id unit prio.
• Defend
• Defends the fortified zone with highest prio,
  attacks enemies in the area.

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Bo Isaksson

• Team AI
• Originally intended to be based on fuzzy logic.
• Maybe not fuzzy in the traditional sense but
  decisions are made with several variables in
  different equations.
• Grouping
• Depending on the grades of expansiveness,
  aggressiveness and defensiveness the team is
  divided in a number of groups assigned with
  different tasks.
• Zone priorities
• Depending on the teams alignment, prios for
  zones are different.
• Budget plans
• Depending on the teams alignment, money for
  different weapons and upgrades is saved and used
  when needed.
• Exp is used in the same fashion.
• Upgrades
• The team-AI upgrades units in a way that it sees
  appropriate based on units different stats.

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Markus Eliasson

• What was my goal
• Get pathfinding to work in the game
• Learn and get more experience in both programming
  and working in a larger project.
• What Ive done
• Implementing pathfinding to work with a larger
  number of units and working with netowrk.
• Unit AI behaviors, (eg. Starts attacking when
  enemies get close)
• Conclusion
• Learned a lot

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Fredrik AnderssonNetwork

• What was my goal
• Get multiplayer to work.
• Learn about lots of stuff.
• What Ive done
• Implemented network support for our RTS.
• Deterministic simulation.
• Speed Control.
• Conclusion
• More people.
• Learned a lot about networking and working in a
  bigger project.

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Martin Samuelsson

• Roads
• Buildings
• Weapons
• Props
• Zone icons
• Backlog

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Martin Samuelsson
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Nebez Kassem

• Environment
• Buildings
• Props
• Misc GUI and Promo
• Unforeseen tasks

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Nebez Kassem
Nebez Kassem
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Martin Olsson
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Martin Olsson

• Character modeling low High polly
• Texturing (Diffuse, Normal, Specular)
• Big guy
• Normal guy
• Small guy
• Goon
• Misc. Project related tasks.

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• Conclusions
• What went wrong
• What went right
• If we could do it again