Robot formation motion planning using Fast Marching

Garrido, Santiago; Moreno, Luís; Lima, Pedro U.

doi:10.1016/j.robot.2011.05.011

Cited by 47 publications

(38 citation statements)

References 20 publications

(20 reference statements)

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“…Moreover, the modifications to the algorithm to improve the behaviour of the formation or decrease its computational cost proposed in our previous work [6] can also be applied in the method described here.…”

Section: Discussionmentioning

confidence: 99%

“…The algorithm described next is an adaptation of the one proposed in [6] to the FM 2 path planner method. This change is motivated by the fact that FM 2 is an improvement of the VFM planning method.…”

Section: Robot Formation Path Planning With Fast Marching Squarementioning

confidence: 99%

“…It is a base which assures the correct navigation of a robot formation through different environments, avoiding obstacles and adapting to narrow passages. In [6] many additional techniques are proposed to improve the time or behaviour performance of the algorithm. These techniques such as maximum energy 9 configuration, using a tube around the path to decrease the computational cost, or adding springs can be applied to this algorithm with very similar results.…”

Section: Base Algorithmmentioning

confidence: 99%

“…Many other works have been carried out, such as using virtual potential fields to influence the location of each robot during movement in simple formations [6] or in very populated groups [3]. Other techniques are based on those virtual potentials, such as the inclusion of springs and dampers [7,8] to create virtual forces that are transformed into velocity commands.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm proposed herein focuses on a dynamic leader-follower architecture. In a previous paper [6], we tested how robot formations behave under the Voronoi Fast Marching (VFM) method. The results obtained motivated an intensive study on how this method behaves when dealing with uncertainty in the position of robots and obstacles.…”

Section: Introductionmentioning

confidence: 99%

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Planning robot formations with fast marching square including uncertainty conditions

Gómez

Lumbier

Garrido

et al. 2013

Robotics and Autonomous Systems

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This paper presents a novel algorithm to solve the robot formation path planning problem working under uncertainty conditions such as errors the in robot's positions, errors when sensing obstacles or walls, etc. The proposed approach provides a solution based on a leaderfollowers architecture (real or virtual leaders) with a prescribed formation geometry that adapts dynamically to the environment. The algorithm described herein is able to provide safe, collision-free paths, avoiding obstacles and deforming the geometry of the formation when required by environmental conditions (e.g. narrow passages). To obtain a better approach to the problem of robot formation path planning the algorithm proposed includes uncertainties in obstacles' and robots' positions. The algorithm applies the Fast Marching Square (FM 2 ) method to the path planning of mobile robot formations, which has been proved to work quickly and efficiently. The FM 2 method is a path planning method with no local minima that provides smooth and safe trajectories to the robots creating a time function based on the properties of the propagation of the electromagnetic waves and depending on the environment conditions. This method allows to easily include the uncertainty reducing the computational cost significantly. The results presented here show that the proposed algorithm allows the formation to react to both static and dynamic obstacles with an easily changeable behavior.

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Section: Discussionmentioning

confidence: 99%

Section: Robot Formation Path Planning With Fast Marching Squarementioning

confidence: 99%

Section: Base Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Planning robot formations with fast marching square including uncertainty conditions

Gómez

Lumbier

Garrido

et al. 2013

Robotics and Autonomous Systems

Self Cite

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Path Planning and Trajectory Planning Algorithms: A General Overview

Gasparetto

Boscariol

Lanzutti³

et al. 2015

Mechanisms and Machine Science

307

147

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Path planning and trajectory planning are crucial issues in the field of Robotics and, more generally, in the field of Automation. Indeed, the trend for robots and automatic machines is to operate at increasingly high speed, in order to achieve shorter production times. The high operating speed may hinder the accuracy and repeatability of the robot motion, since extreme performances are required from the actuators and the control system. \ud Therefore, particular care should be put in generating a trajectory that could be executed at high speed, but at the same time harmless for the robot, in terms of avoiding excessive accelerations of the actuators and vibrations of the mechanical structure. Such a trajectory is defined as smooth.\ud For such reasons, path planning and trajectory planning algorithms assume an increasing significance in robotics. Path planning algorithms generate a geometric path, from an initial to a final point, passing through pre-defined via-points, either in the joint space or in the operating space of the robot, while trajectory planning algorithms take a given geometric path and endow it with the time information. Trajectory planning algorithms are crucial in Robotics, because defining the times of passage at the via-points influences not only the kinematic properties of the motion, but also the dynamic ones. Namely, the inertial forces (and torques), to which the robot is subjected, depend on the accelerations along the trajectory, while the vibrations of its mechanical structure are basically determined by the values of the jerk (i.e. the derivative of the acceleration).\ud Path planning algorithms are usually divided according to the methodologies used to generate the geometric path, namely:\ud - roadmap techniques\ud - cell decomposition algorithms\ud - artificial potential methods.The algorithms for trajectory planning are usually named by the function that is optimized, namely:\ud - minimum time\ud - minimum energy\ud - minimum jerk.\ud Examples of hybrid algorithms, which optimize more than a single function, are also found in the scientific literature.\ud In this Chapter, the general problem of path planning and trajectory planning will be addressed, and an extended overview of the algorithms belonging to the categories mentioned above will be carried out, with references to the numerous contributions to this field

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Hexapod Walking Robot Locomotion

Tedeschi

Carbone

2015

Motion and Operation Planning of Robotic Systems

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Path planning, gait planning and trajectory planning are assuming an increasing significance for Hexapod Walking robots and more generally, in legged robotics. Indeed, the trend for walking robots is to improve the speed, the stability, the navigation autonomy and the energy efficiency. In this Chapter it will be addressed the problem of Hexapod Walking Robots (HWR) locomotion. An overview of the State of the Art is carried out with references to the numerous contributions to this field. It will be provided a background on the topics of path planning, gait and trajectory planning for HWR locomotion. Special attention will be given to the hexapod gaits, starting from their classification together with a detailed description of most common ones. A case of study is described as referring to previous experiences at LARM in Cassino. Examples of a path planning, gait and trajectory planning are provided through kinematic and dynamic features of Cassino Hexapod leg operation.

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Robot formation motion planning using Fast Marching

Cited by 47 publications

References 20 publications

Planning robot formations with fast marching square including uncertainty conditions

Planning robot formations with fast marching square including uncertainty conditions

Path Planning and Trajectory Planning Algorithms: A General Overview

Hexapod Walking Robot Locomotion

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