Sensor-based probabilistic roadmaps for car-like robots

Author(s):  
A. Sanchez ◽  
R. Zapata
2014 ◽  
Vol 28 (22) ◽  
pp. 1519-1531 ◽  
Author(s):  
Mika T. Rantanen ◽  
Martti Juhola

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 642 ◽  
Author(s):  
Ángel Madridano ◽  
Abdulla Al-Kaff ◽  
David Martín ◽  
and Arturo de la de la Escalera

The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.


2004 ◽  
Vol 23 (7-8) ◽  
pp. 729-746 ◽  
Author(s):  
Thierry Siméon ◽  
Jean-Paul Laumond ◽  
Juan Cortés ◽  
Anis Sahbani

2012 ◽  
Vol 241-244 ◽  
pp. 1922-1930
Author(s):  
Yu Tian Liu

In this paper, we used a probabilistic roadmaps(PRM) method to plan a motion path for a 4 degrees of freedom(DOF) robot in static workspace. This methods includes two phases: a learning phase and a query phase. In learning phase, a roadmap is constructed and stored as a graph , in which stores all of the random collision-free configurations in free configuration space denoted by and keeps all of the edges corresponding to feasible paths between these configurations. In query phase, the algorithm tries to connect any given initial and goal configuration to the nodes in the graph. And then the Dijkstra's algorithm searches for a shortest path to concatenate these two nodes. The experiment result demonstrates that this method applying to this 4 degrees of freedom robot works well.


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