Related papers: GPS Spoofing Attacks on AI-based Navigation System…

Accurate control of autonomous marine robots still poses challenges due to the complex dynamics of the environment. In this paper, we propose a Deep Reinforcement Learning (DRL) approach to train a controller for autonomous surface vessel…

In this paper, we present an autonomous navigation system for goal-driven exploration of unknown environments through deep reinforcement learning (DRL). Points of interest (POI) for possible navigation directions are obtained from the…

We present a novel algorithm (DeepMNavigate) for global multi-agent navigation in dense scenarios using deep reinforcement learning (DRL). Our approach uses local and global information for each robot from motion information maps. We use a…

Reconfigurable intelligent surface (RIS) has recently gained popularity as a promising solution for improving the signal transmission quality of wireless communications with less hardware cost and energy consumption. This letter offers a…

Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigation strategies…

Image retrieval (IR) has emerged as a promising approach for self-localization in unmanned aerial vehicles (UAVs). However, IR-based methods face several challenges: 1) Pre- and post-processing incur significant computational and storage…

Deep reinforcement learning (DRL) is vulnerable to adversarial perturbations. Adversaries can mislead the policies of DRL agents by perturbing the state of the environment observed by the agents. Existing attacks are feasible in principle,…

For high-level Autonomous Vehicles (AV), localization is highly security and safety critical. One direct threat to it is GPS spoofing, but fortunately, AV systems today predominantly use Multi-Sensor Fusion (MSF) algorithms that are…

Deep reinforcement learning (RL) has brought many successes for autonomous robot navigation. However, there still exists important limitations that prevent real-world use of RL-based navigation systems. For example, most learning approaches…

Deep Reinforcement Learning (DRL) systems are increasingly used in safety-critical applications, yet their security remains severely underexplored. This work investigates backdoor attacks, which implant hidden triggers that cause malicious…

Prior research has demonstrated that global positioning system (GPS) spoofing attacks on phasor measurement units (PMUs) can cripple power system operation. This paper provides an experimental evidence of the feasibility of such an attack…

Autonomous Ground Vehicles (AGVs) are essential tools for a wide range of applications stemming from their ability to operate in hazardous environments with minimal human operator input. Effective motion planning is paramount for successful…

Recently, Unmanned Aerial Vehicles (UAVs) are employed for a plethora of civilian applications. Such flying vehicles can accomplish tasks under the pilot's eyesight within the range of a remote controller, or autonomously according to a…

In this paper, we propose a novel mechanism to normalize metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defense system. We name this system DRLDO, for Deep…

Deep Reinforcement Learning (DRL) enables cognitive Autonomous Ground Vehicle (AGV) navigation utilizing raw sensor data without a-priori maps or GPS, which is a necessity in hazardous, information poor environments such as regions where…

In this paper, we propose reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicles (UAVs) networks that can utilise both advantages of UAV's agility and RIS's reflection for enhancing the network's performance. To aim at…

This paper develops a Deep Reinforcement Learning (DRL)-agent for navigation and control of autonomous surface vessels (ASV) on inland waterways. Spatial restrictions due to waterway geometry and the resulting challenges, such as high flow…

Embodied artificial intelligence (AI) tasks shift from tasks focusing on internet images to active settings involving embodied agents that perceive and act within 3D environments. In this paper, we investigate the target-driven visual…

Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate drastically different outcomes remains an open problem with…

Deep Reinforcement Learning (DRL) is considered a potential framework to improve many real-world autonomous systems; it has attracted the attention of multiple and diverse fields. Nevertheless, the successful deployment in the real world is…