Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Lin, Chun Hui; Wang, Shyh-Hau; Cheng, Lin

doi:10.3390/s18124181

Cited by 7 publications

(6 citation statements)

References 25 publications

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“…When solving an MRS based cooperative object transportation problem with grasping strategy, the leader/follower configuration is very popular [5], [6], [18]- [20]. In general, a leader robot is responsible for initiating and directing the transportation, while the follower robots coordinate their actions with respect to the leader's guidance.…”

Section: Related Workmentioning

confidence: 99%

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

et al. 2020

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Object transportation could be a challenging problem for a single robot due to the oversize and/or overweight issues. A multi-robot system can take the advantage of increased driving power and more flexible configuration to solve such a problem. However, an increased number of individuals also changed the dynamics of the system which makes control of a multi-robot system more complicated. Even worse, if the whole system is sitting on a centralized decision making unit, the data flow could be easily overloaded due to the upscaling of the system. In this research, we propose a decentralized control scheme on a multi-robot system with each individual equipped with a deep Q-network (DQN) controller to perform an oversized object transportation task. DQN is a deep reinforcement learning algorithm, thus does not require the knowledge of system dynamics, instead, it enables the robots to learn appropriate control strategies through trial-and-error style interactions within the task environment. Since analogous controllers are distributed on the individuals, the computational bottleneck is avoided systematically. We demonstrate such a system in a scenario of carrying an oversized rod through a doorway by a two-robot team. The presented multi-robot system learns abstract features of the task and cooperative behaviors are observed. The decentralized DQN-style controller is showing strong robustness against uncertainties. In addition, We propose a universal metric to assess the cooperation quantitatively.

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Section: Related Workmentioning

confidence: 99%

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

et al. 2020

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“…Reinforcement learning is a computational technique that uses a reward incentive to train a system to produce the desired outcome. This has been used by Lin et al [12] to train an algorithm based on bee colony behavior to perform a two-robot, object-carrying task. Genetic algorithms utilize random processes and evolutionary selection to produce more successful results after each generation.…”

Section: Introductionmentioning

confidence: 99%

“…In the previously cited works performing an objectcarrying task [8,9,12,14], the control architecture was based on a leader-follower paradigm, where the leader makes the path planning decisions for the robot team and the follower(s) rely on the leader for direction. In a decentralized architecture, each agent in the robot team is independent and determines its own actions without the need for explicit communication.…”

Section: Introductionmentioning

confidence: 99%

Genetic fuzzy-based method for training two independent robots to perform a cooperative task

Barth

Sun

Zhang

et al. 2021

Intel Serv Robotics

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When two skilled human workers cooperate on a task, such as moving a sofa through a tight doorway, they often infer what needs to be done without explicit communication because they have learned cooperation skills from their prior work or training. This paper extends that concept to a two-robot team. The robots are given the task to carry a large payload through a narrow doorway while avoiding obstacles within the room. System dynamics and sensor noise were included in the study. Each robot is independently controlled with the knowledge of the goal location, its own position, and the pose of the payload. The decentralized control uses a Genetic Fuzzy System for each robot to learn its own decision-making skill through a training process without a pre-planned motion trajectory. The introduction of a genetic algorithm adds efficiency to the process of determining the shape of the fuzzy logic membership functions by using an evolutionary search algorithm to tune each parameter in the fuzzy system simultaneously. The contribution of this paper is to illustrate how genetic training can tune a simple, decentralized Fuzzy Logic System based on a given scenario and then be used, unaltered, for a scenario beyond that for which it was trained. The extended scenarios introduce unknown obstacles, new sizes and mass properties for the robots and payload, and random initial positions. The effectiveness of this approach for a 2D case is determined by dynamic simulation with results starting at a 95% success rate for the baseline scenario and 84% for the scenario that was extended furthest from how it was originally trained.

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“…Results are obtained for multi-agent systems with external disturbances and unknown input nonlinearities in [ 28 ]. Lin et al [ 29 ] considered the cooperative navigation control of mobile robots in an unknown environment using a neural fuzzy controller. The work of [ 30 ] considered unknown input Bouc-Wen hysteresis control problem and handled it using adaptive control.…”

Section: Introductionmentioning

confidence: 99%

Quantization-Mitigation-Based Trajectory Control for Euler-Lagrange Systems with Unknown Actuator Dynamics

Lyu

Yang

Kolaric

2020

Sensors

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In this paper, we investigate a trajectory control problem for Euler-Lagrange systems with unknown quantization on the actuator channel. To address such a challenge, we proposed a quantization-mitigation-based trajectory control method, wherein adaptive control is employed to handle the time-varying input coefficients. We allow the quantized signal to pass through unknown actuator dynamics, which results in the coupled actuator dynamics for Euler-Lagrange systems. It is seen that our method is capable of driving the states of networked Euler-Lagrange systems to the desired ones via Lyapunov’s direct method. In addition, the effectiveness and advantage of our method are validated with a comparison to the existing controller.

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Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

Cited by 7 publications

References 25 publications

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

Decentralized Control of Multi-Robot System in Cooperative Object Transportation Using Deep Reinforcement Learning

Genetic fuzzy-based method for training two independent robots to perform a cooperative task

Quantization-Mitigation-Based Trajectory Control for Euler-Lagrange Systems with Unknown Actuator Dynamics

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