Adaptive optimal control via reinforcement learning for omni-directional wheeled robots

Sheikhlar, Arash; Fakharian, Ahmad

doi:10.1109/icciautom.2016.7483162

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…There have been studies that use Reinforcement Learning in most variations of the robotic soccer game [17], [18], [23], [32], [35]. However, as stated before, they either focus on the control part or the strategy part of the problem.…”

Section: Related Work a Robotic Soccermentioning

confidence: 99%

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Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

et al. 2022

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Reinforcement Learning (RL) has shown promising performance in environments for both robotic control and strategic decision making. However, they are usually treated as separate problems with different objectives. In this work, we propose the use of Reinforcement Learning to solve both control and strategic problems as one, in a multi-agent robotic soccer environment. We use the IEEE Very Small Size Soccer (VSSS) challenge from the Latin American Robotics Competition (LARC) as a study case. In the VSSS, two autonomous teams of wheeled robots compete by pushing the ball around to score goals. To unify both control and strategy problems, our approach gives full control of the actuators' speed to the RL algorithm whilst keeping the broader objective of winning the game. Our method achieves win rates as high as 93% against hand-coded heuristic strategies. In this work we contribute by developing an RL agent that can learn from self-play and generalize against new opponents. Our methodology uses multiagent Reinforcement Learning with self-play in order to build up the knowledge for complex tasks. We also developed a simulated environment for the robotic soccer game.

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Section: Related Work a Robotic Soccermentioning

confidence: 99%

“…Control-focused approaches often optimize a subsection of the game. Sheikhlar and Fakharian use RL to make an adaptive controller that improves path following in an omnidirectional robot [35]. Melo makes use of RL to control a simulated humanoid robot's joints to effectively kick the ball [23].…”

Section: ) Control-focused Approachesmentioning

confidence: 99%

Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

et al. 2022

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“…Independent of the type of path planning algorithm, the OMR structure is beneficial because it better resembles the material point model used for simplifying the modeling of robots in motion planning simulations. In [6][7][8][9], a four-wheel's dynamic and kinematic modeling, OMR was studied using the Lagrange framework. Sliding mode control allows robust control for OMRs employing mecanum-wheels and rejects disturbances caused by unmodeled dynamics [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs

Achirei

Mocanu

Popovici

et al. 2023

Sensors

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Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.

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Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

Azizi

Rastegarpanah

2021

Robotics

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Motion control in dynamic environments is one of the most important problems in using mobile robots in collaboration with humans and other robots. In this paper, the motion control of a four-Mecanum-wheeled omnidirectional mobile robot (OMR) in dynamic environments is studied. The robot’s differential equations of motion are extracted using Kane’s method and converted to discrete state space form. A nonlinear model predictive control (NMPC) strategy is designed based on the derived mathematical model to stabilize the robot in desired positions and orientations. As a main contribution of this work, the velocity obstacles (VO) approach is reformulated to be introduced in the NMPC system to avoid the robot from collision with moving and fixed obstacles online. Considering the robot’s physical restrictions, the parameters and functions used in the designed control system and collision avoidance strategy are determined through stability and performance analysis and some criteria are established for calculating the best values of these parameters. The effectiveness of the proposed controller and collision avoidance strategy is evaluated through a series of computer simulations. The simulation results show that the proposed strategy is efficient in stabilizing the robot in the desired configuration and in avoiding collision with obstacles, even in narrow spaces and with complicated arrangements of obstacles.

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Adaptive optimal control via reinforcement learning for omni-directional wheeled robots

Cited by 4 publications

References 10 publications

Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

Multiagent Reinforcement Learning for Strategic Decision Making and Control in Robotic Soccer Through Self-Play

Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs

Motion Planning and Control of an Omnidirectional Mobile Robot in Dynamic Environments

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