Papers by Mohamed Abdelkader
IEEE Access, 2022
This paper proposes an innovative hazard identification and risk assessment mapping model for Urb... more This paper proposes an innovative hazard identification and risk assessment mapping model for Urban Search and Rescue (USAR) environments, concentrating on a 3D mapping of the environment and performing grid-level semantic labeling to recognize all hazards types found in the scene and to distinguish their risk severity level. The introduced strategy employs a deep learning model to create semantic segments for hazard objects in 2D images and create semantically annotated point clouds that encapsulate occupancy and semantic annotations such as hazard type and risk severity level. After that, a 3D semantic map that provides situational awareness about the risk in the environment is built using the annotated point cloud. The proposed strategy is evaluated in a realistic simulated indoor environment, and the results show that the system successfully generates a risk assessment map. Further, an open-source package for the proposed approach is provided online for testing and reproducibility.
Sensors
Autonomous robots require control tuning to optimize their performance, such as optimal trajector... more Autonomous robots require control tuning to optimize their performance, such as optimal trajectory tracking. Controllers, such as the Proportional–Integral–Derivative (PID) controller, which are commonly used in robots, are usually tuned by a cumbersome manual process or offline data-driven methods. Both approaches must be repeated if the system configuration changes or becomes exposed to new environmental conditions. In this work, we propose a novel algorithm that can perform online optimal control tuning (OCTUNE) of a discrete linear time-invariant (LTI) controller in a classical feedback system without the knowledge of the plant dynamics. The OCTUNE algorithm uses the backpropagation optimization technique to optimize the controller parameters. Furthermore, convergence guarantees are derived using the Lyapunov stability theory to ensure stable iterative tuning using real-time data. We validate the algorithm in realistic simulations of a quadcopter model with PID controllers using...
Unmanned Aerial Systems, 2021
We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable ... more We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision based object detection and localization, passive aerial grasping with our novel design, GPS based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments, and by comparing our results with some recent, similar works.
2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018
We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanne... more We present a novel gripper design for autonomous aerial transport of ferrous objects with unmanned aerial vehicles (UAVs). The proposed design uses permanent magnets for grasping, and a novel dual-impulsive release mechanism, for achieving drop. The gripper can simultaneously lift up to four objects of arbitrary shape, in fully autonomous mode, with a 100% rate of successful drops. We optimize the system subject to realistic constraints, such as the simplicity of design and its sturdiness to aerial maneuvers, payload limits for multirotor UAVs, reliability of autonomous grasping irrespective of the environment of operation, active power consumption of the gripper, and its comparison with the existing technologies. We describe the design concepts, and the hardware, and perform extensive experiments in both indoor and outdoor environments, with two multi-rotor configurations. Several results, showcasing superior performance of the proposed system are provided as well.
IFAC-PapersOnLine, 2017
We present a framework for distributed, energy efficient, and real time implementable algorithms ... more We present a framework for distributed, energy efficient, and real time implementable algorithms for path planning in multi-agent systems. The proposed framework is presented in the context of a motivating example of capture the flag which is an adversarial game played between two teams of autonomous agents called defenders and attackers. We start with the centralized formulation of the problem as a linear program because of its computational efficiency. Then we present an approximation framework in which each agent solves a local version of the centralized linear program by communicating with its neighbors only. The premise in this work is that for practical multi-agent systems, real time implementability of distributed algorithms is more crucial then global optimality. Thus, instead of verifying the proposed framework by performing offline simulations in MATLAB, we run extensive simulations in a robotic simulator V-REP, which includes a detailed dynamic model of quadrotors. Moreover, to create a realistic scenario, we allow a human operator to control the attacker quadrotor through a joystick in a single attacker setup. These simulations authenticate that the proposed framework is real time implementable and results in a performance that is comparable with the global optimal solution under the considered scenarios.
IEEE Transactions on Control Systems Technology, 2020
Robots in swarms take advantage of localization infrastructure such as a motion capture system or... more Robots in swarms take advantage of localization infrastructure such as a motion capture system or GPS sensors to obtain their global position, which can then be communicated to other robots for swarm coordination. However, the availability of localization infrastructure need not be guaranteed, e.g., in GPS denied environments. Likewise, the communication overhead associated with broadcasting locations may be undesirable. For reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, we propose an onboard relative localization framework for multi-robot systems. The setup consists of an anchor robot with three onboard ultrawideband (UWB) sensors and a tag robot with a single onboard UWB sensor. The anchor robot utilizes the three UWB sensors to estimate the tag robot's location by using its onboard sensing and computational capabilities solely, without explicit inter-robot communication. Because the anchor UWB sensors lack the physical separation that is typical in fixed UWB localization systems, we introduce filtering methods to improve estimation of the tag's location. In particular, we utilize a mixture Monte-Carlo Localization (MCL) approach to capture maneuvers of the tag robot with acceptable precision. We validate the effectiveness of our algorithm with simulations as well as indoor and outdoor field experiments on a two-drone setup. The proposed mixture MCL algorithm yields highly accurate estimates for various speed profiles of the tag robot and demonstrates superior performance over the standard particle filter and the extended Kalman filter.
Towards Real-Time Distributed Planning in Multi-Robot Systems Mohamed Abdelkader Recently, there ... more Towards Real-Time Distributed Planning in Multi-Robot Systems Mohamed Abdelkader Recently, there has been an increasing interest in robotics related to multi-robot applications. Such systems can be involved in several tasks such as collaborative search and rescue, aerial transportation, surveillance, and monitoring, to name a few. There are two possible architectures for the autonomous control of multi-robot systems. In the centralized architecture, a master controller communicates with all the robots to collect information. It uses this information to make decisions for the entire system and then sends commands to each robot. In contrast, in the distributed architecture, each robot makes its own decision independent from a central authority. While distributed architecture is a more portable solution, it comes at the expense of extensive information exchange (communication). The extensive communication between robots can result in decision delays because of which distributed archite...
ArXiv, 2018
Robots in a swarm take advantage of a motion capture system or GPS sensors to obtain their global... more Robots in a swarm take advantage of a motion capture system or GPS sensors to obtain their global position. However, motion capture systems are environment-dependent and GPS sensors are not reliable in occluded environments. For a reliable and versatile operation in a swarm, robots must sense each other and interact locally. Motivated by this requirement, here we propose an on-board localization framework for multi-robot systems. Our framework consists of an anchor robot with three ultrawideband (UWB) sensors and a tag robot with a single UWB sensor. The anchor robot utilizes the three UWB sensors as a localization infrastructure and estimates the tag robot's location by using its on-board sensing and computational capabilities solely, without explicit inter-robot communication. We utilize a dual Monte-Carlo localization approach to capture the agile maneuvers of the tag robot with an acceptable precision. We validate the effectiveness of our algorithm with simulations and indoo...
2018 IEEE Conference on Control Technology and Applications (CCTA), 2018
We present a real-time implementation of a distributed motion planning framework that is based on... more We present a real-time implementation of a distributed motion planning framework that is based on model predictive control with one step prediction horizon and submodular function minimization. In particular, our focus is to evaluate the real-time performance of this distributed motion coordination framework. For performance evaluation, we develop a realistic simulation environment for the challenging setup of capture the flag game, which is played between two teams. We consider a scenario in which each team has four quadcopters and the game is played in an arena with multiple obstacles. We develop the simulation setup primarily in Gazebo with software in the loop. The software in the loop is the autopilot software, which is used to stabilize and control the motion of each quadcopter. The motion plan for the defense team is computed by minimizing submodular potential functions using the distributed and online algorithm presented in our previous work. Based on extensive simulations under various conditions, we verify that the proposed approach can be used effectively for real-time distributed control of multiagent systems over discrete input space.
IFAC-PapersOnLine, 2016
In this work, a cascade structure of a timescale separated integral sliding mode and model predic... more In this work, a cascade structure of a timescale separated integral sliding mode and model predictive control is proposed as a viable alternative for fault-tolerant control. A multi-variable sliding mode control law is designed as the inner loop of the flight control system. Subspace identification is carried out on the aircraft in closed loop. The identified plant is then used for model predictive controllers in the outer loop. The overall control law demonstrates improved robustness to measurement noise, modeling uncertainties, multiple faults and severe wind turbulence and gusts. In addition, the flight control system employs filters and dead-zone nonlinear elements to reduce chattering and improve handling quality. Simulation results demonstrate the efficiency of the proposed controller using conventional fighter aircraft without control redundancy.
2014 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS), 2014
ABSTRACT Floods are one of the most commonly occurring natural disasters, and caused more than 12... more ABSTRACT Floods are one of the most commonly occurring natural disasters, and caused more than 120,000 fatalities in the world between 1991 and 2005. Most of these casualties are caused by the lack of a reliable real-time flash flood monitoring system. Given the area to monitor, unmanned aerial vehicles (UAVs) appear as the most promising solutions for this task.
2014 International Conference on Unmanned Aircraft Systems (ICUAS), 2014
ABSTRACT Floods are the most common natural disasters, causing thousands of casualties every year... more ABSTRACT Floods are the most common natural disasters, causing thousands of casualties every year in the world. In particular, flash flood events are particularly deadly because of the short timescales on which they occur. Unmanned air vehicles equipped with mobile microsensors could be capable of sensing flash floods in real time, saving lives and greatly improving the efficiency of the emergency response. However, of the main issues arising with sensing floods is the difficulty of planning the path of the sensing agents in advance so as to obtain meaningful data as fast as possible. In this particle, we present a fast numerical scheme to quickly compute the trajectories of a set of UAVs in order to maximize the accuracy of model parameter estimation over a time horizon. Simulation results are presented, a preliminary testbed is briefly described, and future research directions and problems are discussed.
Current Robotics Reports, 2021
Purpose of Review Currently, there is a large body of research on multi-agent systems addressing ... more Purpose of Review Currently, there is a large body of research on multi-agent systems addressing their different system theoretic aspects. Aerial swarms as one type of multi-agent robotic systems have recently gained huge interest due to their potential applications. However, aerial robot groups are complex multidisciplinary systems and usually research works focus on specific system aspects for particular applications. The purpose of this review is to provide an overview of the main motivating applications that drive the majority of research works in this field, and summarize fundamental and common algorithmic components required for their development. Recent Findings Most system demonstrations of current aerial swarms are based on simulations, some have shown experiments using few 10 s of robots in controlled indoor environment, and limited number of works have reported outdoor experiments with small number of autonomous aerial vehicles. This indicates scalability issues of current swarm systems in real world environments. This is mainly due to the limited confidence on the individual robot's localization, swarm-level relative localization, and the rate of exchanged information between the robots that is required for planning safe coordinated motions. Summary This paper summarizes the main motivating aerial swarm applications and the associated research works. In addition, the main research findings of the core elements of any aerial swarm system, state estimation and mission planning, are also presented. Finally, this paper presents a proposed abstraction of an aerial swarm system architecture that can help developers understand the main required modules of such systems.
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Papers by Mohamed Abdelkader