A reinforcement learning (RL) enabled intelligent motion planning for collision-free autonomous docking manoeuvre explicitly designed for a robotic floating satellite emulation platform is presented in this article. T...
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A reinforcement learning (RL) enabled intelligent motion planning for collision-free autonomous docking manoeuvre explicitly designed for a robotic floating satellite emulation platform is presented in this article. The Twin Delayed Deep Deterministic Policy Gradient-based RL algorithm involving deep neural network architecture in the actor-critic framework is considered to obtain the collision-free safe docking policy. The RL agents have been trained to perform a resilient target acquisition, ensuring its terminal position and velocity requirements while enabling the capability to avoid both static and dynamic obstacles. The resulting optimal policy is implemented as a feedback control law to enable computationally efficient onboard reactive motion planning for autonomous safe docking of the robotic floating satellite platform in a complex dense debris environment. The efficacy of the proposed motion planning scheme is validated with numerous simulation studies, where it is depicted that the trained RL-based planner has the potential to address the target acquisition with a sufficient degree of accuracy in the presence of both static and dynamic obstacle scenarios.
Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static a...
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Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specif...
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With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performan...
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Addressing driving fatigue is crucial among the various factors in traffic accidents. The origins of driver fatigue encompass prolonged driving, sleep deprivation, and the induction of stress inherent in driving. The ...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Addressing driving fatigue is crucial among the various factors in traffic accidents. The origins of driver fatigue encompass prolonged driving, sleep deprivation, and the induction of stress inherent in driving. The causes of such driver fatigue were categorized into physical factors, including sleep deprivation and prolonged driving, and perceptional-behavioral factors such as perception of driving conditions. Numerous studies have explored technologies to detect driver fatigue in terms of relevance to sleep states, eye movements, electroencephalography (EEG) activation levels, and their functional connectivity. It was also found differences in the occurrence of driver fatigue and related theta and alpha band activity based on driving conditions. However, the factors contributing to these differences remained unexplored. This study seeks to investigate the causal relationship between driver fatigue induced in different driving environments and the corresponding brain activity. We focus on perceptional load, particularly the perception of traffic objects within the driving condition, and physical loads, such as steering, acceleration, and braking, associated with variations in driving environments. In the experiment, we evaluate the relationship between such differences in physical and mental load and the occurrence of fatigue dependent on the driving condition with considering EEG activation patterns. In the results, we found that the amount of load related to driver perception and operation inherent in driving environments plays a crucial role in driver fatigue.
In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. ...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
In this article, we propose a novel communication-based action space enhancement for the D-MARL exploration algorithm to improve the efficiency of mapping an unknown environment, represented by an occupancy grid map. In general, communication between autonomous systems is crucial when exploring large and unstructured environments. In such real-world scenarios, data transmission is limited and relies heavily on inter-agent proximity and the attributes of the autonomous platforms. In the proposed approach, each agent’s policy is optimized by utilizing the heterogeneous-agent proximal policy optimization algorithm to autonomously choose whether to communicate or explore the environment. To accomplish this, multiple novel reward functions are formulated by integrating inter-agent communication and exploration. The investigated approach aims to increase efficiency and robustness in the mapping process, minimize exploration overlap, and prevent agent collisions. The D-MARL policies trained on different reward functions have been compared to understand the effect of different reward terms on the collaborative attitude of the homogeneous agents. Finally, multiple simulation results are provided to prove the efficacy of the proposed scheme.
The translation application was developed using Python and Visual Studio Code as the primary programming environment. Technological progress has led to the younger generation forsaking the Batak language in their dail...
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Mobile edge computing (MEC) and blockchain technology are two rapidly evolving areas in computerscience that have the potential to significantly impact the design and operation of distributed systems. MEC involves th...
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A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from a...
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This article introduces a novel method for converting 3D voxel maps, commonly utilized by robots for localization and navigation, into 2D occupancy maps for both unmanned aerial vehicles (UAVs) and unmanned ground veh...
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