Titlebook: Advances in Swarm Intelligence; 9th International Co Ying Tan,Yuhui Shi,Qirong Tang Conference proceedings 2018 Springer Nature Switzerland

cardiopulmonary

省略

Danny de Jesús Gómez-Ramírez,Alan Smaill by using multi-objective particle swarm optimization. Finally, results are compared with the hydrodynamic calculations. Result shows the efficiency of the method proposed in the paper in the optimal shape design of an underwater robot.

troponins

Félix Bou,Enric Plaza,Marco SchorlemmerS database and a robot can move accordingly while being able to detect the obstacles and adjust the path. Moreover, the mapping results can be shared among multi-robots to re-localize a robot in the same area without GPS assistance. It has been proved functioning well in the simulation environment of a campus scenario.

armistice

adhesive

Thomas B. Seiler,Wolfgang Wannenmacherugh the kinematic analysis. Moreover, an inverse kinematics based closed-loop controller is designed to achieve position tracking. Finally, a simulation and an experiment is carried out to validate the workspace and control algorithm respectively. The results show that this robot can follow a given trajectory with satisfactory accuracy.

Chandelier

patella

GROVE

收养

Transaction Flows in Multi-agent Swarm Systemsions is proposed, which allows obtaining a good degree of approximation of an investigated flow to Poisson flow with minimal costs of computing resources. That allows optimizing the information exchange processes between individual units of swarm intelligent systems.

烧烤

Deep Regression Models for Local Interaction in Multi-agent Robot Tasksin the environment along the sensor array, we propose the use of a recurrent neural network. The models are developed for different types of obstacles, free spaces and other robots. The scheme was successfully tested by simulation and on real robots for simple grouping tasks in unknown environments.