Group Trajectory Optimization using Swarm Intelligence

Group Trajectory Optimization using Swarm Intelligence

Nowadays, Industrials are relying more than ever on fully automated machines and the area of application of these machines will continue to increase. However, full automation is suffering from a high implementation and maintenance cost. That’s why it is important for the future research in automation to develop new methods and strategies in order to make full-automation more accessible.

On this regard,with this research, I want to implement a new strategy for fully automated network of vehicles  and see if it is possible to perform better than the existing strategies.

The strategy I want to implement is called Swarm Intelligence, it consist on using a lot of particles with simple behavior and make them search for an optimal solution. By making these particles share information and behave accordingly to the shared information, the particles can converge to a solution.

The Swarm Intelligence algorithm I am using is the “ant colony optimization” that applies well to trajectory optimization and has been used in previous research for congestion avoidance algorithm on roadway for example.

To evaluate my algorithm I will be using the m3pi module which is a little programmable vehicle. I will programm 3-4 of them and connect them to a network.

by Raphael

人間の操作特性に着目した パーソナリティの抽出



  1. 回転複合センサによる多層化マップ生成


  2. 複数の移動ロボット間での機械学習の共有による適応制御の効率化


  3. 洗濯物畳みロボットのためのインテリジェントワークベンチの開発


  4. ニューラルネットワーク学習結果の合成による人間モデル生成


  5. 人間の操作特性に着目した パーソナリティの抽出


  6. ダイナミクスを変化させることによるインタフェースの設計


  7. サブリミナルキャリブレーションによる機械操作熟達支援


  8. ロボットの動作に対する視覚的注意の変化の測定