A model predictive controller for robots to follow a virtual leader

Gu, Dongbing; Hu, Huosheng

doi:10.1017/s0263574708005316

Cited by 29 publications

(8 citation statements)

References 29 publications

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“…t o denotes the duration that the robots spent colliding with obstacles as they moved through the obstacles. The definition of n and d i j are the same as in (5). The smaller the cost of C f m and C po , the better the system's performance.…”

Section: Simulations and Discussionmentioning

confidence: 99%

A Simple Scheme for Formation Control Based on Weighted Behavior Learning

Lin

Hwang

Wang

2014

IEEE Trans. Neural Netw. Learning Syst.

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Several correlated issues of autonomy and simplicity regarding formation control for robots with a self-awareness mechanism in unstructured environments are considered. To achieve autonomy and simplicity, a hybrid scheme is derived for robot maneuvering based on a multibehavioral system. The system holds some self-awareness capabilities ensuring precision and robustness in the presence of internal and external disturbances within the limited capacity of interrobot communication. This is to ensure that the robots can march and simultaneously maintain their assigned formation and avoid hazardous collisions on the way to their destination. These self-awareness capabilities are achieved through a layered reinforcement learning algorithm. At the bottom level, robots are equipped with a set of primitive behaviors learned prior to team formation. The high-level combined behavior is generated by multiplying the outputs of each primitive behavior by its weight, and then summing and normalizing the results. The weights keep adaptively adjusting to the proposed reinforcement learning method. Once a robot receives a command providing the formation shape and the location of the destination, the robot approaches the destination autonomously and keeps an appropriate distance from its neighbors to maintain the assigned pattern. Leadership was given to a robot occupying the lead position. The volunteer leader takes the responsibility for keeping the formation, reporting its existence to the other robots, and resetting the position assignment after passing obstacles. Simulations show the practicality and performance of the proposed approach in both static and dynamic obstacle environments.

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Section: Simulations and Discussionmentioning

confidence: 99%

A Simple Scheme for Formation Control Based on Weighted Behavior Learning

Lin

Hwang

Wang

2014

IEEE Trans. Neural Netw. Learning Syst.

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“…Other authors expand upon this approach with other control strategies, such as dynamic feedback linearisation [43], model predictive control [44], [45], and first and second order sliding mode control [46].…”

Section: Leader-follower Approachesmentioning

confidence: 99%

Distributed autonomy and formation control of a drifting swarm of autonomous underwater vehicles

Rypkema¹

2015

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Recent advances in autonomous underwater vehicle (AUV) technology have led to their widespread acceptance and adoption for use in scientific, commercial, and defence applications in the underwater domain. At the same time, research progress in swarm robotics has seen swarm intelligence algorithms in use with greater effect on real-world robots in the field. A group of AUVs utilizing swarm intelligence concepts has the potential to address issues more effectively than a single AUV, and such a group can potentially open up new areas of application. Examples include the monitoring and tracking of highly dynamic oceanographic phenomena such as phytoplankton blooms and the use of an AUV swarm as a virtual acoustic receiver for sea-bottom seismic surveying or the monitoring of naturally occurring acoustic radiation from cracking ice. However, the limitations of the undersea environment places unique constraints on the use of existing swarm robotics approaches with AUVs. In particular, algorithms must be distributed and robust in the face of localization error and degraded communications.This work presents an investigation into one particular swarm strategy for a group of AUVs, termed formation control, with consideration to the constraints of the underwater domain. Four formation control algorithms, each developed and tested within the MOOS-IvP framework, are presented. In addition, a 'formation quality' metric is introduced. This metric is used in conjunction with a measure of formation energy expenditure to compare the efficacy of each behaviour during construction of a desired formation, and formation maintenance while it drifts in ocean currents. This metric is also used to compare robustness of each algorithm in the presence of vehicle failure and changing communication rate.

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“…A classical leader-following approach was presented in [6], and a similar strategy was also applied in [7] for implementing the separation-bearing control and separation-separation control. Another studies and applications can be found in [8], and several control techniques, such as model predictive control [9] and sliding mode control [10], were employed to implement the leader-following strategy. The leader-following approach is decentralized, conceptually simple and widely employed in the multivehicle formation control.…”

Section: A Formation Control Strategiesmentioning

confidence: 99%