ISS properties of nonholonomic vehicles

SUMMARYThis paper presents vision-based control strategies for decentralized stabilization of unmanned vehicle formations. Three leader-follower formation control algorithms, which ensure asymptotic co-ordinated motion, are described and compared. The first algorithm is a full state feedback nonlinear controller that requires full knowledge of the leader's velocities and accelerations. The second algorithm is a robust state feedback nonlinear controller that requires knowledge of the rate of change of the relative position error. Finally, the third algorithm is an output feedback approach that uses a high-gain observer to estimate the derivative of the unmanned vehicles' relative position. Thus, this algorithm only requires knowledge of the leader-follower relative distance and bearing angle. Both data are computed using measurements from a single camera, eliminating sensitivity to information flow between vehicles. Lyapunov's stability theorybased analysis and numerical simulations in a realistic 3D environment show the stability properties of the control methodologies.

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“…with K defined in (18). Replacing (20) into (16), the error dynamics of the closed-loop system become…”

Section: Full State Feedback Formation Control (Fsfb)mentioning

confidence: 99%

An output feedback nonlinear decentralized controller for unmanned vehicle co‐ordination

Orqueda

Zhang

Fierro

2007

Intl J Robust & Nonlinear

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“…In research on robot formation control and motion planning, the problem formulations can be classified into three groups [2,10]: 1) A robot in a team is designated as the leader, and is first commanded to follow a predefined pose trajectory (position and orientation) to lead the team. The other robots in the team are moving by following the leader while having to satisfy a set of task-related geometric constraints [21]; 2) With the concept of virtual structure, the robot team is modeled as a single structure, in which the motion of each robot is translated from the desired global structure [7]; and 3) The robot team is desired to provide a group behavior, and each single robot's motion is subject to a weighted average of several behaviors [1].…”

Section: Introduction and Related Workmentioning

confidence: 99%

Herding by Caging: a Topological Approach towards Guiding Moving Agents via Mobile Robots

Varava

Hang

Kragić

et al. 2017

Robotics: Science and Systems XIII

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Abstract-In this paper, we propose a solution to the problem of herding by caging: given a set of mobile robots (called herders) and a group of moving agents (called sheep), we move the latter to some predefined location in such a way that they cannot escape from the robots while moving. We model the interaction between the herders and the sheep by assuming that the former exert virtual "repulsive forces" pushing the sheep away from them. These forces induce a potential field, in which the sheep move in a way that does not increase their potential. This enables the robots to partially control the motion of the sheep. We formalize this behavior geometrically by applying the notion of caging, widely used in robotic grasping. We show that our approach is provably correct in the sense that the sheep cannot escape from the robots. We propose an RRT-based motion planning algorithm, demonstrate its probabilistic completeness, and evaluate it in simulations.

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“…The leader motion can be determined by a calculated trajectory or by teleoperation; and the followers' motion is determined by tracking the leader with some geometrical restrictions. This motion can change dynamically over time, if necessary [2]. These leaders can be real robots or, as proposed in [3], virtual leaders.…”

Section: Introductionmentioning

confidence: 99%

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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ISS properties of nonholonomic vehicles

Cited by 18 publications

References 12 publications

An output feedback nonlinear decentralized controller for unmanned vehicle co‐ordination

An output feedback nonlinear decentralized controller for unmanned vehicle co‐ordination

Herding by Caging: a Topological Approach towards Guiding Moving Agents via Mobile Robots

Planning robot formations with fast marching square including uncertainty conditions

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