Formation control of multiple wheeled mobile robots based on model predictive control

Nfaileh, Najla; Alipour, Khalil; Tarvirdizadeh, Bahram; Hadi, Alireza

doi:10.1017/s0263574722000121

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…Their control law consists of individual tracking errors and coordination tracking errors for leader-follower pairs. Another study uses a similar approach but in more complex environments with obstacles [34]. In this study, two separate control algorithms based on the model predictive control (MCP) scheme were proposed, namely the linear and non-linear MPC.…”

Section: Introductionmentioning

confidence: 99%

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

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In recent years, autonomous mobile platforms have seen an increase in usage in several applications. One of which is street-sweeping. Although street-sweeping is a necessary process due to health and cleanliness, fleet operations are difficult to plan optimally. Since each vehicle has several constraints (battery, debris, and water), path planning becomes increasingly difficult to perform manually. Additionally, in real-world applications vehicles may become inactive due to a breakdown, which requires real-time scheduling technology to update the paths for the remaining vehicles. In this paper, the fleet street-sweeping problem can be solved using the proposed lower-level and higher-level path generation methods. For the lower level, a Smart Selective Navigator algorithm is proposed, and a modified genetic algorithm is used for the higher-level path planning. A case study was presented for Uchi Park, South Korea, where the proposed methodology was validated. Specifically, results generated from the ideal scenario (all vehicles operating) were compared to the breakdown scenario, where little to no difference in the overall statistics was observed. Additionally, the lower-level path generation could yield solutions with over 94% area coverage.

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Section: Introductionmentioning

confidence: 99%

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Parsons,

Baghyari,

Seo

et al. 2024

Robotics

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“…Numerous control approaches have been used to address the coordination of wheeled mobile robots in a formation. To name a few, behavior-based [1][2][3], potential-field [4,5], consensus-based [6,7], leader-follower [8][9][10] and virtual-structure [11][12][13] strategies.…”

Section: Introductionmentioning

confidence: 99%

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Damani,

Benselama,

Hedjar

2023

Archive of Mechanical Engineering

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In this paper, an adaptive distributed formation controller for wheeled nonholonomic mobile robots is developed. The dynamical model of the robots is first derived by employing the Euler-Lagrange equation while taking into consideration the presence of disturbances and uncertainties in practical applications. Then, by incorporating fractional calculus in conjunction with fast terminal sliding mode control and consensus protocol, a robust distributed formation controller is designed to assure a fast and finite-time convergence of the robots towards the required formation pattern. Additionally, an adaptive mechanism is integrated to effectively counteract the effects of disturbances and uncertain dynamics. Moreover, the suggested control scheme's stability is theoretically proven through the Lyapunov theorem. Finally, simulation outcomes are given in order to show the enhanced performance and efficiency of the suggested control technique.

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“…A multirobot system is a system containing multiple robots, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), that are moving around in a given environment to accomplish tasks cooperatively. Compared with their single-robot counterparts, multirobot systems can increase functionalities, improve efficiency, enhance adaptability, and provide robustness [1][2][3]. Multirobot systems have been applied to deal with labor-consuming or dangerous missions, such as assembly, disaster rescue, environmental protection, traffic monitoring, military reconnaissance, cargo delivery, and many other fields [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

et al. 2022

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Collision avoidance is critical in multirobot systems. Most of the current methods for collision avoidance either require high computation costs (e.g., velocity obstacles and mathematical optimization) or cannot always provide safety guarantees (e.g., learning-based methods). Moreover, they cannot deal with uncertain sensing data and linguistic requirements (e.g., the speed of a robot should not be large when it is near to other robots). Hence, to guarantee real-time collision avoidance and deal with linguistic requirements, a distributed and hybrid motion planning method, named Fuzzy-VO, is proposed for multirobot systems. It contains two basic components: fuzzy rules, which can deal with linguistic requirements and compute motion efficiently, and velocity obstacles (VOs), which can generate collision-free motion effectively. The Fuzzy-VO applies an intruder selection method to mitigate the exponential increase of the number of fuzzy rules. In detail, at any time instant, a robot checks the robots that it may collide with and retrieves the most dangerous robot in each sector based on the predicted collision time; then, the robot generates its velocity in real-time via fuzzy inference and VO-based fine-tuning. At each time instant, a robot only needs to retrieve its neighbors’ current positions and velocities, so the method is fully distributed. Extensive simulations with a different number of robots are carried out to compare the performance of Fuzzy-VO with the conventional fuzzy rule method and the VO-based method from different aspects. The results show that: Compared with the conventional fuzzy rule method, the average success rate of the proposed method can be increased by 306.5%; compared with the VO-based method, the average one-step decision time is reduced by 740.9%.

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Formation control of multiple wheeled mobile robots based on model predictive control

Cited by 7 publications

References 44 publications

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Advanced Path Planning for Autonomous Street-Sweeper Fleets under Complex Operational Conditions

Formation control of nonholonomic wheeled mobile robots using adaptive distributed fractional order fast terminal sliding mode control

Cooperative collision avoidance in multirobot systems using fuzzy rules and velocity obstacles

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