Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots

Farina, Marcello; Perizzato, Andrea; Scattolini, Riccardo

doi:10.1016/j.robot.2015.06.007

Cited by 26 publications

(8 citation statements)

References 33 publications

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“…In distributed, the main issue is the task has to be distributed in a robust an efficient manner to ensure that every agent is able to perform its individual task cooperatively with another agents to achieve certain target. Distributing task among heterogeneous agents [11,15] is more crucial and complex comparing with homogeneous agents which are identical [20][21][22]59]. Limited sensing range and low bandwidth are also among physical constraints in distributed approach.…”

Section: Agent To Agentmentioning

confidence: 99%

“…They proved that their controller is more effective as compared to the time-based broadcast control. Finally, by having the intelligence, multi agent robot control is ready to be apply for an advance and complex multi-agents applications [3,36,49,59]. As an example, Jolly et al [53] and Candea et al [3] have proposed their own controller to let the soccer robots coordinate and play successfully.…”

Section: Intelligencesmentioning

confidence: 99%

See 1 more Smart Citation

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Ismail¹,

Sariff²

2019

Applications of Mobile Robots

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Research in the area of cooperative multi-agent robot systems has received wide attention among researchers in recent years. The main concern is to find the effective coordination among autonomous agents to perform the task in order to achieve a high quality of overall performance. Therefore, this paper reviewed various selected literatures primarily from recent conference proceedings and journals related to cooperation and coordination of multi-agent robot systems (MARS). The problems, issues, and directions of MARS research have been investigated in the literature reviews. Three main elements of MARS which are the type of agents, control architectures, and communications were discussed thoroughly in the beginning of this paper. A series of problems together with the issues were analyzed and reviewed, which included centralized and decentralized control, consensus, containment, formation, task allocation, intelligences, optimization and communications of multi-agent robots. Since the research in the field of multi-agent robot research is expanding, some issues and future challenges in MARS are recalled, discussed and clarified with future directions. Finally, the paper is concluded with some recommendations with respect to multi-agent systems.

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Section: Agent To Agentmentioning

confidence: 99%

Section: Intelligencesmentioning

confidence: 99%

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Ismail¹,

Sariff²

2019

Applications of Mobile Robots

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“…The load trajectory is expressed as a sequence of waypoints, which are the solution of a sequence of constrained optimization problems. This approach allows us to solve dierent coordination and cooperation problems [13], and it allows us to take into account operational constraints such as obstacles avoidance and UAV performance constraint. For this application, at each discrete time instant the new way-point is computed on the basis of the obstacle positions, the multi-copter cruise speed, the previous way-point and the target point.…”

Section: Contributionmentioning

confidence: 99%

“…The set Z depends on the algorithm applied in the outer control loop. For this reason, the set Z is computed after dening MPC optimization problem using the algorithm in [13].…”

Section: Reference Trajectory Generationmentioning

confidence: 99%

Model predictive control for a multi-body slung-load system

Tartaglione

D’Amato

Ariola

et al. 2017

Robotics and Autonomous Systems

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In this paper we present a multi-level and distributed control system, based on a robust Model Predictive Control (MPC) technique, for a multi-body slung-load system. In particular, we consider a swarm of autonomous multi-copters which are connected by wires to a suspended payload. The payload reference trajectory is obtained through a constrained optimization, then the reference trajectory for each UAV is derived on the basis of the known shape of the formation, while taking into account operational constraints such as collision avoidance and cruise speed. Trajectory tracking is performed by a multi-level flight control system based on a MPC technique and a PID control system. Numerical simulations have been performed in order to test the control system in realistic scenarios. In particular, the multi-copters are modeled by the six Degrees-of-Freedom (6DOF) model, the constraint forces on the wires are calculated using the Udwadia–Kalaba equation and the external disturbances (atmospheric turbulence and gust) are included in the simulation. Simulation results are encouraging, thus making the proposed system an appealing candidate for similar applications

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“…Yukun Lin et al [26] utilized a leader/follower multi-AUV control system to enable the AUVs to drive toward the target through a collision-free path. Mingzhi Chen et al [27] proposed a novel cooperative hunting algorithm for inhomogeneous MAUVs to achieve quick and active path pursuit and planning. Marcello Farina et al [28] proposed a distributed predictive control approach for robot coordination.…”

Section: Introductionmentioning

confidence: 99%

Multibody System-Based Adaptive Formation Scheme for Multiple Under-Actuated AUVs

Huang

Tang

Zhang

et al. 2020

Sensors

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Underwater vehicles’ coordination and formation have attracted increasing attention since they have great potential for real-world applications. However, such vehicles are usually under-actuated and with very limited communication capabilities. On the basis of the multibody system concept, a multiple autonomous underwater vehicle formation and communication link framework has been established with an adaptive and radial basis function (RBF) strategy. For acoustic communication, a packets transmission scheme with topology and protocol has been investigated on the basis of an acoustic communication framework and transmission model. Moreover, the cooperative localization errors caused by packet loss are estimated through reinforcement learning radial basis function neural networks. Furthermore, in order to realize formation cruising, an adaptive RBF formation scheme with magnitude reduced multi-layered potential energy functions has been designed on the basis of a time-delayed network framework. Finally, simulations and experiments have been extensively performed to validate the effectiveness of the proposed methods.

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Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots

Cited by 26 publications

References 33 publications

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

A Survey and Analysis of Cooperative Multi-Agent Robot Systems: Challenges and Directions

Model predictive control for a multi-body slung-load system

Multibody System-Based Adaptive Formation Scheme for Multiple Under-Actuated AUVs

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