Awesome
<!-- README.md RVO2 Library C# SPDX-FileCopyrightText: 2008 University of North Carolina at Chapel Hill SPDX-License-Identifier: CC-BY-SA-4.0 Creative Commons Attribution-ShareAlike 4.0 International Public License You are free to: * Share -- copy and redistribute the material in any medium or format * ShareAlike -- If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original * Adapt -- remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: * Attribution -- You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. * No additional restrictions -- You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Notices: * You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. * No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material. Please send all bug reports to <geom@cs.unc.edu>. The authors may be contacted via: Jur van den Berg, Stephen J. Guy, Jamie Snape, Ming C. Lin, Dinesh Manocha Dept. of Computer Science 201 S. Columbia St. Frederick P. Brooks, Jr. Computer Science Bldg. Chapel Hill, N.C. 27599-3175 United States of America <https://gamma.cs.unc.edu/RVO2/> -->Optimal Reciprocal Collision Avoidance for C#
https://gamma.cs.unc.edu/RVO2/
We present a formal approach to reciprocal collision avoidance, where multiple independent mobile robots or agents need to avoid collisions with each other without communication among agents while moving in a common workspace. Our formulation, optimal reciprocal collision avoidance (ORCA), provides sufficient conditions for collision-free motion by letting each agent take half of the responsibility of avoiding pairwise collisions. Selecting the optimal action for each agent is reduced to solving a low-dimensional linear program, and we prove that the resulting motions are smooth. We test our optimal reciprocal collision avoidance approach on several dense and complex simulation scenarios workspaces involving thousands of agents, and compute collision-free actions for all of them in only a few milliseconds.
RVO2 Library C# is an open-source C# .NET 6 implementation of our algorithm in two dimensions. It has a simple API for third-party applications. The user specifies static obstacles, agents, and the preferred velocities of the agents. The simulation is performed step-by-step via a simple call to the library. The simulation is fully accessible and manipulable during runtime.
SPDX-FileCopyrightText: 2008 University of North Carolina at Chapel Hill
SPDX-License-Identifier: Apache-2.0
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
https://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Please send all bug reports to geom@cs.unc.edu.
The authors may be contacted via:
Jur van den Berg, Stephen J. Guy, Jamie Snape, Ming C. Lin, Dinesh Manocha
Dept. of Computer Science
201 S. Columbia St.
Frederick P. Brooks, Jr. Computer Science Bldg.
Chapel Hill, N.C. 27599-3175
United States of America