General Path Planning Methodology for Leader-Follower Robot Formations

This paper describes a robust algorithm for mobile robot formations based on the Voronoi Fast Marching path planning method. This is based on the propagation of a wave throughout the model of the environment, the wave expanding faster as the wave's distance from obstacles increases. This method provides smooth and safe trajectories and its computational efficiency allows us to maintain a good response time. The proposed method is based on a local-minima-free planner; it is complete and has an O(n) complexity order where n is the number of cells of the map. Simulation results show that the proposed algorithm generates good trajectories.Comunidad de Madri

Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching

For navigation in complex environments, a robot need s to reach a compromise between the need for having efficient and optimized trajectories and t he need for reacting to unexpected events. This paper presents a new sensor-based Path Planner w hich results in a fast local or global motion planning able to incorporate the new obstacle information. In the first step the safest areas in the environment are extracted by means of a Vorono i Diagram. In the second step the Fast Marching Method is applied to the Voronoi extracted a reas in order to obtain the path. The method combines map-based and sensor-based planning o perations to provide a reliable motion plan, while it operates at the sensor frequency. The m ain characteristics are speed and reliability, since the map dimensions are reduced to an almost uni dimensional map and this map represents the safest areas in the environment for moving the robot. In addition, the Voronoi Diagram can be calculated in open areas, and with all kind of shaped obstacles, which allows to apply the proposed planning method in complex environments wher e other methods of planning based on Voronoi do not work.This work has been supported by the CAM Project S2009/DPI-1559/ROBOCITY2030 I

A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance

A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment

Concerns regarding the influence of the marine environment, such as surface currents and winds, on autonomous marine vehicles have been raised in recent years. A number of researchers have been working on the development of intelligent path planning algorithms to minimise the negative effects of environmental influences, however most of this work focuses on the platform of autonomous underwater vehicles (AUVs) with very little work on unmanned surface vehicles (USVs). This paper presents a novel multi-layered fast marching (MFM) method developed to generate practical trajectories for USVs when operating in a dynamic environment. This method constructs a synthetic environment framework, which incorporates the information of planning space and surface currents. In terms of the planning space, there are repelling and attracting forces, which are evaluated using an attractive/repulsive vector field construction process. The influence of surface currents is weighted against the obstacles in the planning space using a 4-regime risk strategy. A trajectory is then calculated using the anisotropic fast marching method. The complete algorithm has been tested and validated using simulated surface currents, and the performance of generated trajectories have been evaluated in terms of different optimisation criteria, such as the distance and energy consumption

Service Robots in Catering Applications: A Review and Future Challenges.

“Hello, I’m the TERMINATOR, and I’ll be your server today”. Diners might soon be feeling this greeting, with Optimus Prime in the kitchen and Wall-E then sending your order to C-3PO. In our daily lives, a version of that future is already showing up. Robotics companies are designing robots to handle tasks, including serving, interacting, collaborating, and helping. These service robots are intended to coexist with humans and engage in relationships that lead them to a better quality of life in our society. Their constant evolution and the arising of new challenges lead to an update of the existing systems. This update provides a generic vision of two questions: the advance of service robots, and more importantly, how these robots are applied in society (professional and personal) based on the market application. In this update, a new category is proposed: catering robotics. This proposal is based on the technological advances that generate new multidisciplinary application fields and challenges. Waiter robots is an example of the catering robotics. These robotic platforms might have social capacities to interact with the consumer and other robots, and at the same time, might have physical skills to perform complex tasks in professional environments such as restaurants. This paper explains the guidelines to develop a waiter robot, considering aspects such as architecture, interaction, planning, and executionpost-print13305 K

Path planning and collision avoidance for autonomous surface vehicles II: a comparative study of algorithms

Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios

A novel path planning approach for smart cargo ships based on anisotropic fast marching

Path planning is an essential tool for smart cargo ships that navigate in coastal waters, inland waters or other crowded waters. These ships require expert and intelligent systems to plan safe paths in order to avoid collision with both static and dynamic obstacles. This research proposes a novel path planning approach based on the anisotropic fast marching (FM) method to specifically assist with safe operations in complex marine navigation environments. A repulsive force field is specially produced to describe the safe area distribution surrounding obstacles based on the knowledge of human. In addition, a joint potential field is created to evaluate the travel cost and a gradient descent method is used to search for appropriate paths from the start point to the end point. Meanwhile, the approach can be used to constantly optimize the paths with the help of the expert knowledge in collision avoidance. Particularly, the approach is validated and evaluated through simulations. The obtained results show that it is capable of providing a reasonable and smooth path in a crowded waters. Moreover, the ability of this approach exhibits a significant contribution to the development of expert and intelligent systems in autonomous collision avoidance