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With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data…
This paper addresses the online motion planning problem of mobile robots under complex high-level tasks. The robot motion is modeled as an uncertain Markov Decision Process (MDP) due to limited initial knowledge, while the task is specified…
As Multi-Robot Systems (MRS) become more affordable and computing capabilities grow, they provide significant advantages for complex applications such as environmental monitoring, underwater inspections, or space exploration. However,…
As unmanned systems such as Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) become increasingly important to applications like urban sensing and emergency response, efficiently recruiting these autonomous devices to…
Network traffic analysis increasingly uses complex machine learning models as the internet consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows can easily arrive faster than model inference rates. The…
We develop a Markov decision process (MDP) framework to autonomously make guidance decisions for satellite collision avoidance maneuver (CAM) and a reinforcement learning policy gradient (RL-PG) algorithm to enable direct optimization of…
This study investigates the issue of task allocation in Human-Machine Collaboration (HMC) within the context of Industry 4.0. By integrating philosophical insights and cognitive science, it clearly defines two typical modes of human…
Predictive planning is a key capability for robots to efficiently and safely navigate populated environments. Particularly in densely crowded scenes, with uncertain human motion predictions, predictive path planning, and control can become…
Cyber-physical systems (CPS), in most instances, represent systems of systems with an informationally decentralized structure such as emerging mobility systems, networked control systems, sustainable manufacturing, smart power grids, power…
This review explores the application of intelligent optimization algorithms to Multi-Objective Optimal Power Flow (MOPF) in enhancing modern power systems. It delves into the challenges posed by the integration of renewables, smart grids,…
Typical schedulers in multi-tenancy environments make use of reactive, feedback-oriented mechanisms based on performance counters to avoid resource contention but suffer from detection lag and loss of performance. In this paper, we address…
This paper is concerned with multi-modal data fusion (MMDF) under unexpected modality failures in nonlinear non-Gaussian dynamic processes. An efficient framework to tackle this problem is proposed. In particular, a notion termed modality…
This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low Earth orbit. The inspection task is represented by a global…
In recent years, human casualties and damage to resources caused by emergent incidents have become a serious problem worldwide. In this paper, we model the emergency decision-making problem and use Multi-agent System (MAS) to solve the…
Beyond data collection, future mobile crowdsensing (MCS) in complex applications must satisfy diverse requirements, including reliable task completion, budget and quality constraints, and fluctuating worker availability. Besides raw-data…
Human long duration exploration missions (LDEMs) raise a number of technological challenges. This paper addresses the question of the crew autonomy: as the distances increase, the communication delays and constraints tend to prevent the…
Accurate human motion prediction (HMP) is critical for seamless human-robot collaboration, particularly in handover tasks that require real-time adaptability. Despite the high accuracy of state-of-the-art models, their computational…
Drone fleets with onboard cameras coupled with computer vision and DNN inferencing models can support diverse applications. One such novel domain is for one or more buddy drones to assist Visually Impaired People (VIPs) lead an active…
We present model predictive selection (MPS), a new method for selecting the stable closed-loop (CL) equilibrium attitude-error quaternion (AEQ) of an uncrewed aerial vehicle (UAV) during the execution of high-speed yaw maneuvers. In this…
High-speed flight vehicles, which travel much faster than the speed of sound, are crucial for national defense and space exploration. However, accurately predicting their behavior under numerous, varied flight conditions is a challenge and…