Multi-mode filter target tracking method for mobile robot using multi-agent reinforcement learning

Li, Xiaofeng; Ren, Jie; Li, Yunbo

doi:10.1016/j.engappai.2023.107398

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Many ways have been proposed to search for and identify a known or unknown number of dynamic or static targets from measurements taken by mobile agents in robotics [7][8][9][10][11][12][13][14][15], control [16][17][18][19], reinforcement learning [20][21][22][23][24][25][26][27][28][29], multi-target filtering [5,[12][13][14]18,23,[30][31][32][33][34], etc. Among all these fields, we focus here on multi-target filtering with an intensity function representation because this framework best accommodates our setting.…”

Section: Introductionmentioning

confidence: 99%

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

Yousuf,

Lendek,

Buşoniu

2024

Sensors

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Consider a drone that aims to find an unknown number of static targets at unknown positions as quickly as possible. A multi-target particle filter uses imperfect measurements of the target positions to update an intensity function that represents the expected number of targets. We propose a novel receding-horizon planner that selects the next position of the drone by maximizing an objective that combines exploration and target refinement. Confidently localized targets are saved and removed from consideration along with their future measurements. A controller with an obstacle-avoidance component is used to reach the desired waypoints. We demonstrate the performance of our approach through a series of simulations as well as via a real-robot experiment in which a Parrot Mambo drone searches from a constant altitude for targets located on the floor. Target measurements are obtained on-board the drone using segmentation in the camera image, while planning is done off-board. The sensor model is adapted to the application. Both in the simulations and in the experiments, the novel framework works better than the lawnmower and active-search baselines.

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Section: Introductionmentioning

confidence: 99%

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

Yousuf,

Lendek,

Buşoniu

2024

Sensors

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“…More precisely, our objective is to: Create a multi-agent system that simulates the interactions between generators, loads, and other components inside a power grid. [11][12][13][14][15] Examine the aptitude of agents to acquire knowledge by using reinforcement learning methods in order to adjust to dynamic operational circumstances. Assess the effectiveness of the suggested Multi-Agent Reinforcement Learning (MARL) method in relation to the stability of the system, economic efficiency, and its capacity to handle dynamic disruptions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Reinforcement Learning for Power System Operation and Control

Jain,

Sridevi,

Dabral

et al. 2024

E3S Web Conf.

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This study investigates the use of Multi-Agent Reinforcement Learning (MARL) to enhance the efficiency of power system operation and control. The simulated power system environment is represented as a multi-agent system, where intelligent agents are used to mimic generators and loads. The MARL framework utilizes Q-learning algorithms to allow agents to independently adjust their activities in accordance with changing operating circumstances. The resulting simulated data represents a wide-ranging power grid scenario, including buses with different generator capacity, load needs, and transmission line capacities. The findings indicate a significant improvement in the stability of the system via Multi-Agent Reinforcement Learning (MARL), since the agents’ capacity to learn and adapt enables them to quickly alter the outputs of generators and meet the needs of the load, so ensuring that voltage and frequency levels remain within acceptable limits. The MARL framework significantly improves economic efficiency by enabling actors to optimize their behaviors in order to reduce the total costs of the system. The agility of the MARL-based control method is emphasized by the decrease in response time to dynamic disturbances, as agents demonstrate quick and efficient reactions to unforeseen occurrences. The favorable results highlight the potential of MARL as a decentralized decision-making model in power systems, providing advantages in terms of stability, economic efficiency, and the capacity to respond to disruptions. Although the research uses artificial data in a controlled setting, the observed enhancements indicate the flexibility and efficacy of the MARL framework. Future research should prioritize the integration of more practical situations and tackling computational obstacles to further confirm the suitability and expandability of Multi-Agent Reinforcement Learning (MARL) in actual power systems.

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Motion Tracking Control Based on an Omnidirectional Mobile Robot

Wang,

2024

Lecture Notes in Electrical Engineering

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Multi-mode filter target tracking method for mobile robot using multi-agent reinforcement learning

Cited by 8 publications

References 25 publications

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

Exploration-Based Planning for Multiple-Target Search with Real-Drone Results

Multi-Agent Reinforcement Learning for Power System Operation and Control

Motion Tracking Control Based on an Omnidirectional Mobile Robot

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