Collision-Free Formation Control with Decentralized Connectivity Preservation for Nonholonomic-Wheeled Mobile Robots

Poonawala, Hasan A.; Satici, Aykut C.; Eckert, Hazen; Spong, Mark W.

doi:10.1109/tcns.2014.2378876

Cited by 95 publications

(34 citation statements)

References 14 publications

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“…In [29], the potential function is changed to be time varying in order to meet the kinematic requirements of a non-holonomic robot. The authors in [30] extended their multi-robot scheme using APF to be applied on non-holonomic platforms. The controller is decoupled, the robots are initially supposed to turn their headings according to the potential forces, then move accordingly.…”

Section: Proposed Schemementioning

confidence: 99%

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Harik

Korsæth

2018

Robotics

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Abstract:The key factor for autonomous navigation is efficient perception of the surroundings, while being able to move safely from an initial to a final point. We deal in this paper with a wheeled mobile robot working in a GPS-denied environment typical for a greenhouse. The Hector Simultaneous Localization and Mapping (SLAM) approach is used in order to estimate the robots' pose using a LIght Detection And Ranging (LIDAR) sensor. Waypoint following and obstacle avoidance are ensured by means of a new artificial potential field (APF) controller presented in this paper. The combination of the Hector SLAM and the APF controller allows the mobile robot to perform periodic tasks that require autonomous navigation between predefined waypoints. It also provides the mobile robot with a robustness to changing conditions that may occur inside the greenhouse, caused by the dynamic of plant development through the season. In this study, we show that the robot is safe to operate autonomously with a human presence, and that in contrast to classical odometry methods, no calibration is needed for repositioning the robot over repetitive runs. We include here both hardware and software descriptions, as well as simulation and experimental results.

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Section: Proposed Schemementioning

confidence: 99%

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Harik

Korsæth

2018

Robotics

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“…For example, formations of Unmanned Aerial Vehicles (UAVs), Autonomous Underwater Vehicles (AUVs), and wheeled mobile robots are widely used for tracking, search, surveillance, oceanic explorations, etc. In addition to the basic requirement of maintaining formation in terms of connectivity preservation [7], one can utilize heterogeneity in the controller gains so that the formation of these vehicles can be made to move in a desired direction, thus helping to explore an area of interest more effectively. Secondly, while implementing the control law physically for the homogeneous gains case, it is impossible to get identical controller gain for each agent.…”

Section: Introductionmentioning

confidence: 99%

Synchronization of multi-agent systems with heterogeneous controllers

Jain

Ghose

2017

Nonlinear Dyn

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This paper studies the synchronization of a multiagent system where the agents are coupled through heterogeneous controller gains. Synchronization refers to the situation where all the agents in a group have a common velocity direction. We generalize existing results and show that by using heterogeneous controller gains, the final velocity direction at which the system of agents synchronize can be controlled. The effect of heterogeneous gains on the reachable set of this final velocity direction is further analyzed. We also show that for realistic systems, a limited control force to stabilize the agents to the synchronized condition can be achieved by confining these heterogeneous controller gains to an upper bound. Simulations are given to support the theoretical findings.

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“…In some formation control problems, robots can have fixed roles. It means either fixed positions in the topology or hierarchical roles as leaders and followers . For those studies, when the robots operate in complex environments, and the team scale needs to be changed, the system parameters need to be redesigned.…”

Section: Introductionmentioning

confidence: 99%

“…Limited sensing is assumed in , and limited communication range is studied in . The fixed topologies in can also lead to distributed systems. These algorithms are useful in reducing the computational burden, but they all require at least limited communication.…”

Section: Introductionmentioning

confidence: 99%

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Scalable formation control in stealth with limited sensing range

Shi

Lim

2016

Int. J. Robust. Nonlinear Control

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In this paper, a protocol and a control law are designed for a single robot so that a team of such robots can interact and cooperate to reach the displacements from an eligible reference formation. Each robot is equipped with displacement sensors of limited sensing ranges. Communication channels are assumed to be unavailable to the team, and each robot works in stealth mode. The team is scalable such that new robots can be recruited, and existing robots can be dismissed. In order for the team size to be scalable, the extended formation based on relative displacement is established as the reference formation. Thus, using the extended formation as a reference, the control law and the protocol could be flexible. As potential conflicts deflect the robot team from the desired formation, the control law is designed to expose the conflicts to the involved neighboring robots such that the protocol can resolve them. A numerical example is given to illustrate how an extended formation is designed, and a simulation example is conducted to demonstrate the performance and merits of the proposed techniques.

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Collision-Free Formation Control with Decentralized Connectivity Preservation for Nonholonomic-Wheeled Mobile Robots

Cited by 95 publications

References 14 publications

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse

Synchronization of multi-agent systems with heterogeneous controllers

Scalable formation control in stealth with limited sensing range

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