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Maynad, Vincentius Charles
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking Maynad, Vincentius Charles; Nugraha, Yurid Eka; Alkaff, Abdullah
Journal of Robotics and Control (JRC) Vol 5, No 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22560

Abstract

This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.
Cooperative Formation and Obstacle Avoidance Control for Multi-UAV Based on Guidance Route and Artificial Potential Field Sahal, Mochammad; Maynad, Vincentius Charles; Bilfaqih, Yusuf
Journal of Robotics and Control (JRC) Vol 5, No 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23577

Abstract

Research on cooperative control of multi-UAV systems has gained significant attention in the flight control field, with a particular focus on formation control and obstacle avoidance due to their complexity and importance. This paper introduces an approach to a group of quadcopter control by integrating fuzzy controller, guidance route, and Artificial Potential Field (APF) methods. The quadcopter dynamic model, featuring six degrees of freedom, is controlled using a fuzzy state feedback controller in its inner loop. From the outer loop, the formation-making is guided by an easy-to-use and versatile guidance route approach while obstacle avoidance is tackled using the optimal APF method. There are two avoidance strategies that can be compared and analyzed, called "total avoidance" and "minimal avoidance", both individually and as a "combined" strategy. Simulations in various environments with different obstacle sizes show that all control algorithms can accomplish the tasks effectively. Both strategies have their own strength in terms of path length and formation maintenance. A formation performance index, which is calculated based on the difference between the desired position and the actual position of each quadcopter, is used to quantify the effectiveness of the method. A smaller value means better formation maintenance. The total avoidance strategy achieved an average index of 0.8000 and the minimal avoidance strategy reached 1.2227. These metrics highlight the trade-offs of each strategy in maintaining optimal formation. These findings offer valuable insights for the development of more robust multi-UAV systems, with potential applications in autonomous delivery services, surveillance, and environmental monitoring.
Three-Dimensional Coordination Control of Multi-UAV for Partially Observable Multi-Target Tracking Maynad, Vincentius Charles; Nugraha, Yurid Eka; Alkaff, Abdullah
Journal of Robotics and Control (JRC) Vol. 5 No. 5 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i5.22560

Abstract

This research deals with multi-UAV systems to track partially observable multi-targets in noisy three-dimensional environments, which are commonly encountered in defense and surveillance systems. It is a far extension from previous research which focused mainly on two-dimensional, fully observable, and/or perfect measurement settings. The targets are modeled as linear time-invariant systems with Gaussian noise and the pursuers UAV are represented in a standard six-degree-of-freedom model. Necessary equations to describe the relationship between observations regarding the target and the pursuers states are derived and represented as the Gauss-Markov model. Partially observable targets require the pursuers to maintain belief values for target positions. In the presence of a noisy environment, an extended Kalman filter is used to estimate and update those beliefs. A Decentralized Multi-Agent Reinforcement Learning (MARL) algorithm known as soft Double Q-Learning is proposed to learn the coordination control among the pursuers. The algorithm is enriched with an entropy regulation to train a certain stochastic policy and enable interactions among pursuers to foster cooperative behavior. The enrichment encourages the algorithm to explore wider and unknown search areas which is important for multi-target tracking systems. The algorithm was trained before it was deployed to complete several scenarios. The experiments using various sensor capabilities showed that the proposed algorithm had higher success rates compared to the baseline algorithm. A description of the many distinctions between two-dimensional and three-dimensional settings is also provided.
Cooperative Formation and Obstacle Avoidance Control for Multi-UAV Based on Guidance Route and Artificial Potential Field Sahal, Mochammad; Maynad, Vincentius Charles; Bilfaqih, Yusuf
Journal of Robotics and Control (JRC) Vol. 5 No. 6 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i6.23577

Abstract

Research on cooperative control of multi-UAV systems has gained significant attention in the flight control field, with a particular focus on formation control and obstacle avoidance due to their complexity and importance. This paper introduces an approach to a group of quadcopter control by integrating fuzzy controller, guidance route, and Artificial Potential Field (APF) methods. The quadcopter dynamic model, featuring six degrees of freedom, is controlled using a fuzzy state feedback controller in its inner loop. From the outer loop, the formation-making is guided by an easy-to-use and versatile guidance route approach while obstacle avoidance is tackled using the optimal APF method. There are two avoidance strategies that can be compared and analyzed, called "total avoidance" and "minimal avoidance", both individually and as a "combined" strategy. Simulations in various environments with different obstacle sizes show that all control algorithms can accomplish the tasks effectively. Both strategies have their own strength in terms of path length and formation maintenance. A formation performance index, which is calculated based on the difference between the desired position and the actual position of each quadcopter, is used to quantify the effectiveness of the method. A smaller value means better formation maintenance. The total avoidance strategy achieved an average index of 0.8000 and the minimal avoidance strategy reached 1.2227. These metrics highlight the trade-offs of each strategy in maintaining optimal formation. These findings offer valuable insights for the development of more robust multi-UAV systems, with potential applications in autonomous delivery services, surveillance, and environmental monitoring.
Co-Authors Abdullah Alkaff Nugraha, Yurid Eka Sahal, Mochammad Yusuf Bilfaqih