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Found 10 results

  1. Hello guys, I just registered this site and heard from my lecturer that this a good site to talk about certain topics since my research topic are mostly programmer who are experienced with AI can answer the survey. The reason of the survey below is to understand which is suitable solution for 2d platformer pathfinding for AI and which one is easier to implement for 2D platformer. I would appreciate if you guys give your responses for the survey link shared and thank you for spending time answering the survey. Sorry if the survey is a bit hard to understand, I tried to make it understandable as best as I can. Again, thank you! https://goo.gl/forms/S0etAlAAHL6S5kTI2
  2. Hello hello. I'm in the preliminary design phase for a space based game, and I need some advice on how to approach the AI side of things. Here's the situation in a nutshell. Say I'm a space explorer with a spaceship, and I am competing with other space explorers to be the first one to discover things. I have a procedurally generated 2D top-down solar system, and to make things a little simpler, let's say all the planets in the system are static, meaning they are not orbiting their sun. But they all have their gravity wells of varying strength. As a player I have to negotiate newtonian physics around these planets, using engine thrust at the right amounts and timing, to get to where I want. That part is not a problem. I'm also willing to assume non-newtonian rotation so that AI and player do not need to account for appyling torque to get a correct bearing. So far I have not mentioned whether this is real-time or turn-based and that's because of my uncertainty around AI. Problem is I'm not sure how to approach the AI side of things either way. Ideally I'd like to have an AI that can optimize trajectory for speed and/or fuel efficiency, but I have been able to find precious little on the topic on the interwebs. The best I've found so far is the following article from a decade ago, and it does not really point to a solution: http://playtechs.blogspot.ca/2007/05/pathfinding-in-space.html If I can find a good resource on how to pull this off in realtime, I'd like to go that route. At the moment my fallback is using a turn based system for exploration and visualizing the system as a hex grid. Then using A* I could get AI agents to naively assume use of thrust to come to a stand still each time they want to change trajectory, and then add extra thrust to move in the next direction, but I'm worried this would be extremely under-optimized in terms of fuel efficiency and the reach of rival ships as a result. I could also factor in the AI ship's current velocity into the graph search, which would likely greatly increase the search space for pathfinding, but I haven't experimented with it yet to be able to say whether it's viable. Any thoughts?
  3. Hello, I'm trying to implement enemy pathfinding algorihtm, but i have problem with empty tile collision when moving enemy to node. For example this image shows how it should move like shown in example: But it stucks at last tile: It happens because enemy collides with right side of "air" tile and then it removes from node list because it "collided", but it works with not "air" tiles of course. How do fix this problem? Code: void Enemy::generateMoveToNode(AStar::Vec2i lastNode) { auto lastSave = AStar::Vec2i{ 0.0f, 0.0f }; while (!target.empty()) { if (target.back().y == lastNode.y) { lastSave = target.back(); target.pop_back(); } else { moveToNodes.push_back(lastSave); moveToNodes.push_back(target.back()); generateMoveToNode(target.back()); return; } } moveToNodes.push_back(lastSave); } void Enemy::updateTarget(std::shared_ptr<InputManager> inputManager) { if (moveToNodes.empty()) return; // Calculate half sizes. float halfWidthA = getSize(0) / 2.0f; float halfHeightA = getSize(1) / 2.0f; float halfWidthB = 32.0f / 2.0f; float halfHeightB = 32.0f / 2.0f; // Calculate centers. auto centerA = glm::vec2(getPosition(0) + halfWidthA, getPosition(1) + halfHeightA); auto centerB = glm::vec2((moveToNodes.front().x * 32.0f) + halfWidthB, (moveToNodes.front().y * 32.0f) + halfHeightB); // Calculate current and minimum-non-intersecting distances between centers. float distanceX = centerA.x - centerB.x; float distanceY = centerA.y - centerB.y; float minDistanceX = halfWidthA + halfWidthB; float minDistanceY = halfHeightA + halfHeightB; setKey(inputManager->getKeyBinding("Move Left"), false); setKey(inputManager->getKeyBinding("Move Right"), false); setKey(inputManager->getKeyBinding("Jump"), false); setKey(inputManager->getKeyBinding("Duck"), false); // If we are not intersecting at all, return (0, 0). if (abs(distanceX) >= minDistanceX || abs(distanceY) >= minDistanceY) { if (moveToNodes.front().y > ceil(getPosition(1) / 32.0f)) setKey(inputManager->getKeyBinding("Jump"), true); else if (moveToNodes.front().y < ceil(getPosition(1) / 32.0f)) { if (getCanClimb()) setKey(inputManager->getKeyBinding("Duck"), true); } else { if (moveToNodes.front().x < ceil(getPosition(0) / 32.0f)) setKey(inputManager->getKeyBinding("Move Left"), true); else if (moveToNodes.front().x > floor(getPosition(0) / 32.0f)) setKey(inputManager->getKeyBinding("Move Right"), true); } updateInput(inputManager); return; } // Calculate and return intersection depths. float depthX = distanceX > 0 ? minDistanceX - distanceX : -minDistanceX - distanceX; float depthY = distanceY > 0 ? minDistanceY - distanceY : -minDistanceY - distanceY; updateInput(inputManager); moveToNodes.erase(moveToNodes.begin()); } generateMoveToNode: recursive function to generate all nodes. updateTarget: updates enemy every frame to check if it hits node and then removes it from list and checks next till no nodes left.
  4. Here are the two main files: Path: https://pastebin.com/ZkrbJ78t AStar: https://pastebin.com/EVGazFtU A few things I've done to optimize the search: 1. Rather than a dictionary to keep track of scores and parents, I use a 2d array of objects that are mapped to the grid i.e. [x,y] represents grid point (x,y). Objects have F,G,H and Parent fields, these fields are reset to default values during each search. 2. Cheap weighted heuristic 3. Orthogonal neighbors (searching 4 neighbors rather than 8) Paths don't have to be beautiful because the point reduction smooths them out quite nice. In Path.cs you can see the two functions to reduce points, Incremental and Stretch. Stretch is much faster because it has the potential to reduce points all the way to the end right off the bat or early on thus reducing calls to the LineOfSight function, but it isn't so reliable around small areas/corners. The incremental function is reliable but it calls to the LineOfSight function for each and every point along the path, this causes the path reduction to be just about as slow as calculating the path itself. Line of Sight is determined by getting all points between 2 points and checking that they are all flagged as Walkable. My grid is dynamic and roughly 250x250. I tried out Theta* and JPS, both were slower than my current A*. I'm trying to avoid using a navmesh or worker thread, I suppose mostly just looking for ideas to tighten up my code
  5. When my program was generating navigation meshes for my geometry, and the extent is 5000x5000 (m), with a cell size of 5.0 meters, it has to generate 250,000 cells, which makes my computer halt, the target object is a truck, which has a radius of about 20 meters, should I give the cell size a value of 20, because I don't really want to use this navigation mesh on one type of vehicle only. I want to use it for vehicles like cars, which has only about 5 meters in radius... the generation speed is a problem while the memory consumption is another. Any ideas how to improve this? Thanks Jack
  6. 3D Rigger Mixamo Is Making Some Changes Originally founded in 2008, Mixamo has been a go-to source for 3D animation and rigging for nearly a decade. On August 28th, 2013, Mixamo launched a platform called Face Plus. It was designed to allow developers or animators turn facial expressions recorded with even a webcam into top-notch 3D animation. This is not unlike what Holotech Studios’ “FaceRig” does for streamers and video creators as well. Back this past May 2017, Adobe (publishers of the Mixamo platform since 2015) announced the end of an era. Mixamo’s Face Plus will be removed on August 22nd, 2017, as well as several other free features. According to the Adobe post on the Mixamo website, they lay out what is changing and what is staying the same. Set out as bullet points in the post, they seem to be cleaning house on the “free” features. But, they are keeping a small handful of their main key points, such as: Select and use characters from Mixamo’s Character Collection Upload 3D characters to get them ready to animate using the Auto-Rigger Apply animations to characters Download as an .fbx or .dae Noticeably, the list that’s getting 86’ed includes major tools and the ability to save assets from their site: The ability to save and retrieve assets in “My Assets”. The Face Plus facial animation plugin The Decimator polygon reduction tool The ability to download Control-rig scripts Download types for .bvh and .biped that streamline integration with 3rd party applications Mixamo forums will close and all help articles will be moved to Adobe’s HelpX Don't Worry! There's still time! They’re allowing people to still download the tools and plug-ins until August 22nd, as well as utilizing their easy-to-download features of saving Assets directly. Those that use Mixamo, utilize Face Plus, or even just do animation are highly encouraged to take advantage and download the tools for the next week. To download, sign into your (free) Adobe account at the Mixamo website and start downloading. Developers have only one week left to grab as much as they can. For a complete FAQ and how-to for the Mixamo massacre, check out the Adobe post from their Customer Care Team. UPDATE (8/15/17): Adobe would like to stress that Mixamo, itself, is not going anywhere, just the Face Plus features, the tools mentioned above, and a couple other features to make the platform more "streamlined and a little modernized". Once the dust is cleared next week from the changes, I'll reach out to Adobe from a more guided tour of the new set-up. Additionally, I have changed the title of this article to reflect the clarification. Apologies for the inconvenience to those that have already read the article.
  7. Hi I have a A* gridbased pathfind for a city builder I'm planning (think banished or stronghold). Units can walk in 8 directions from each tile. But I want more free movement. Look at the example pic below. The pathfind finds the path in step 0 to 19. But now I want to "simplify" the path so the unit goes straight from node marked 0 to 10 (and obviously also from 12 to 19; i forgot to mark this in the picture). Any good algoritm to "reduce" the path in this way? I understand I need to check if there is a unbroken "line of walk" between two nodes but if i start with checking 0 to 1, 0 to 2, 0 to 3 until i find that 0 to 12 doesnt work and stop there (so I throw away nodes marked 1 through 8) this would be VERY slow. Any idea?
  8. Hi In a game like stronghold with many workers going back and forth from a workplace to a dropoff-point for example, would I store the path and then use it backwards to not have to redo pathfinding all the time? If the moving units in the city doesnt block movement, very often I could just reuse the old path. Only if i run into a wall (if the player constructs a new building in the path of the old workers) will I recalculate the path. Workers in old age of empires for example, do they pathfind every time they go back to drop off gold in the storage? Thanks! Erik
  9. Hi, I was reading Game AI Pro how they implemented Supreme Commander path finding and came to one question. For the integration of cost field they are using Eikonal equation for traversing the areas. They recommended Fast Iterative Method and it's parallel version for solving this equation. However in all basic tutorials of flow field navigation that I found for integrating of the cost field is used simple grass/brush fire algorithm. My question is what would be the difference if they used in the game just grass fire algorithm? I guess the reason was some quality/performance trade of. Fortunately the Fast Iterative Method is very easy to implement so I can compare the performance, but what I don't understand is, what is the "quality" benefit.