Mathematical Modeling and Intelligent Algorithm for Multirobot Path Planning

10.1155/2017/1465158Mathematical Problems in Engineering2017146515

IEEE ACCESS SPECIAL SECTION EDITORIAL: REAL-TIME MACHINE LEARNING APPLICATIONS IN MOBILE ROBOTICS

In the last ten years, advances in machine learning methods have brought tremendous developments to the field of robotics. The performance in many robotic applications such as robotics grasping, locomotion, human–robot interaction, perception and control of robotic systems, navigation, planning, mapping, and localization has increased since the appearance of recent machine learning methods. In particular, deep learning methods have brought significant improvements in a broad range of robot applications including drones, mobile robots, robotics manipulators, bipedal robots, and self-driving cars. The availability of big data and more powerful computational resources, such as graphics processing units (GPUs), has made numerous robotic applications feasible which were not possible previously

Social potential model to simulate emergent behaviour for swarm robots

Swarm robotics has a wide range of applications in numerous fields from space and sub-sea exploration to the deployment of teams of interacting artificial agents in disposal systems. In this paper, we introduce a model to simulate the emergent behaviour of multi-agent robot systems, based on principles from physical mechanics. The model is based on mutual interactions among the swarm individuals. The main elements of these interactions are repulsion forces, attraction forces, alignment forces and dissipative forces generated by the swarm members. Using statistical tools, which are used to investigate simulated group behaviour, we discuss the importance of introducing some dissipation to the system as well as the effect of the interaction parameters on various components of the model

Decentralized Discrete-Time Formation Control for Multirobot Systems

Inspired from the collective behavior of biological entities for the group motion coordination, this paper analyzes the formation control of mobile robots in discrete time where each robot can sense only the position of certain team members and the group behavior is achieved through the local interactions of robots. The main contribution is an original formal proof about the global convergence to the formation pattern represented by an arbitrary Formation Graph using attractive potential functions. The analysis is addressed for the case of omnidirectional robots with numerical simulations

Scheduling for finite time consensus

We study the problem of link scheduling for discrete-time agents to achieve average consensus in finite time under communication constraints. We provide necessary and sufficient conditions under which finite time consensus is possible. Furthermore, we prove bounds on the consensus time and exhibit provably optimal communication policies. We also discuss the dual problem of designing communication schedules given a fixed consensus-time requirement