Finite-time multi-agent deployment: A nonlinear PDE motion planning approach

Automatica ◽  
2011 ◽  
Vol 47 (11) ◽  
pp. 2534-2542 ◽  
Author(s):  
Thomas Meurer ◽  
Miroslav Krstic
Keyword(s):  
Motion Planning ◽  
Finite Time ◽  
Nonlinear Pde ◽  
Planning Approach ◽  
Multi Agent

scholarly journals Cloud-Supported Coverage Control for Persistent Surveillance Missions

2017 ◽  
Vol 139 (8) ◽  
Author(s):  
Jeffrey R. Peters ◽  
Sean J. Wang ◽  
Amit Surana ◽  
Francesco Bullo
Keyword(s):  
Motion Planning ◽  
Finite Time ◽  
Mobile Agents ◽  
Pareto Optimal ◽  
Coverage Control ◽  
Control Scheme ◽  
Multi Agent ◽  
Time Invariant ◽  
Modular Framework ◽  
Effective Pairing

A cloud-supported coverage control scheme is proposed for multi-agent, persistent surveillance missions. This approach decouples assignment from motion planning operations in a modular framework. Coverage assignments and surveillance parameters are managed on the cloud and transmitted to mobile agents via unplanned and asynchronous exchanges. These updates promote load-balancing, while also allowing effective pairing with typical path planners. Namely, when paired with a planner satisfying mild assumptions, the scheme ensures that (i) coverage regions remain connected and collectively cover the environment, (ii) regions may go uncovered only over bounded intervals, (iii) collisions (sensing overlaps) are avoided, and (iv) for time-invariant event likelihoods, a Pareto optimal configuration is produced in finite time. The scheme is illustrated in simulated missions.

Some AI Based Approaches on Mobile Robots Motion Planning

2010 ◽  
Vol 166-167 ◽  
pp. 101-108 ◽  
Author(s):  
Adrian Burlacu ◽  
Marius Kloetzer ◽  
Doru Panescu
Keyword(s):  
Artificial Intelligence ◽  
Path Planning ◽  
Motion Planning ◽  
Mobile Robots ◽  
Artificial Vision ◽  
Temporal Logics ◽  
Artificial Intelligence Techniques ◽  
Planning Approach ◽  
Multi Agent

This contribution presents some important issues on mobile robots path planning. While it is hard to find a unique architecture for all the applications involving mobile robots, specific approaches can provide suitable solutions. Thus, three distinct structures are discussed, all making use of certain artificial intelligence techniques. They address the use and integration of artificial vision, a planning approach based on temporal logics, and a multi agent scheme. The three methods refer cases of mobile robots evolving in environments where various types of sensorial information can be obtained. Each of the proposed solutions determines advantages when it is used for a certain class of tasks.

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