Browsing by Author "Benmoussa, Yahia"

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Differential Drive Mobile Robot Energy Model Integration into ROS–Based Simulation Environment
(2019) Touzout, Walid; Benazzouz, Djamel; Ouelmokhtar, Hand; Benmoussa, Yahia
Nowadays, mobile robots are used in different applications however they are constrained by the limitation of their batteries making reducing energy consumption a significant challenge for mobile robots’ community. Thus, energy consumption modelling is therefore becoming an important approach to reducing energy cost. However, power reduction techniques evaluation and approbation may require much hardware configuration and can be time consuming especially in case of huge scenarios. We present in this paper a methodology used to enrich the Robot Operating System (ROS) infrastructure with modelling, monitoring, and energy management tools by integrating an energy consumption model of a differential drive mobile robot into ROS and Gazebo-based simulator. The simulation results illustrate the instantaneous power consumption of the robot for three different scenarios. Thereafter, the total energy consumption can be monitored without any hardware requirement at the end of each scenario
Energy-Aware HEVC software decoding on mobile heterogeneous Multi-Cores architectures
(Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 2022) Khernache, Mohammed Bey Ahmed; Boukhobza, Jalil; Benmoussa, Yahia; Menard, Daniel
Video content is becoming increasingly omnipresent on mobile platforms thanks to advances in mobile heterogeneous architectures. These platforms typically include limited rechargeable batteries which do not improve as fast as video content. Most state-of-the-art studies proposed solutions based on parallelism to exploit the GPP heterogeneity and DVFS to scale up/down the GPP frequency based on the video workload. However, some studies assume to have information about the workload before to start decoding. Others do not exploit the asymmetry character of recent mobile architectures. To address these two challenges, we propose a solution based on classification and frequency scaling. First, a model to classify frames based on their type and size is built during design-time. Second, this model is applied for each frame to decide which GPP cores will decode it. Third, the frequency of the chosen GPP cores is dynamically adjusted based on the output buffer size. Experiments on real-world mobile platforms show that the proposed solution can save more than 20% of energy (mJ/Frame) compared to the Ondemand Linux governor with less than 5% of miss-rate. Moreover, it needs less than one second of decoding to enter the stable state and the overhead represents less than 1% of the frame decoding time
Energy-based USV maritime monitoring using multi-objective evolutionary algorithms
(Elsevier, 2022) Ouelmokhtar, Hand; Benmoussa, Yahia; Benazzouz, Djamel; Ait Chikh, Mohamed Abdessamad; Lemarchand, Laurent
This study addresses the monitoring mission problem using an USV equipped with an on-board LiDAR allowing to monitor regions inside its coverage radius. The problem is formulated as a bi-objective coverage path planning with two conflicting objectives : minimization of the consumed energy and maximization of the coverage rate. To solve the problem, we use two popular multi-objective evolutionary algorithms : Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Pareto Archived Evolution Strategy (PAES). First, we compare the efficiency of these two algorithms and show that PAES allows to find solutions allowing to save more energy as compared to those provided by NSGA-II. Then, we propose a new method which improves the performance of evolutionary algorithms when solving covering path planning problems by reducing the chromosome size. We have applied this method on the used algorithms and simulation results shows a significant performance enhancement both PAES and NSGA-II.
HEVC hardware vs software decoding : an objective energy consumption analysis and comparison
(Elsevier, 2021) Khernache, Mohammed Bey Ahmed; Benmoussa, Yahia; Boukhobza, Jalil; Menard, Daniel
Web data are experiencing a proliferation of video content for mobile platforms. This is accompanied by new advances in heterogeneous general purpose processor (GPP) cores embedded in mobile devices which offer a great opportunity to enhance both performance and energy efficiency of software (SW) video decoding. On the other hand, hardware (HW) video accelerators are more energy-efficient but are not flexible and their time-to-market is significant. In this context, this paper proposes a characterization methodology to investigate the performance and power consumption of two video decoding approaches on mobile platforms. The first one uses a HW decoder intellectual property (HDIP) in addition to a GPP (for the control). The second one is SW-based and uses only a heterogeneous multi-core GPP. The objective is to study the behavior of both video decoding approaches by comparing them and to understand why and in which case it is worth relying on the GPP rather than the HDIP. We also derive the optimal GPP configuration (number of cores and their frequency) that minimizes the energy consumption for a given video bit-stream on a given platform. The proposed methodology was applied on the HEVC video codec standard. In some state-of-the-art work figures, the SW video decoding consumes up to more energy than HDIPs. Our results show that, for video resolutions of 1080p and lower and at the operating system perspective point of view, the HEVC SW decoding consumes on average less than more energy (mJ/Frame) than the HW one. Then, the more we scale up the resolution, the more we get the advantage of using the HW video decoding. Furthermore, the HEVC HW and SW decoders consume effectively less than 30% and 50% of the global power consumption of the tested platforms, respectively
A methodology for performance/energy consumption characterization and modeling of video decoding on heterogeneous SoC and its applications
(Elsevier, 2015) Benmoussa, Yahia; Boukhobza, Jalil; Senn, Eric; Hadjadj-Aoul, Yassine; Benazzouz, Djamel
Mobile robot energy modelling integrated into ros and gazebo-based simulation environment
(2021) Touzout, Walid; Benazzouz, Djamel; Benmoussa, Yahia
Mobile robots' autonomy is limited by the capacity of their batteries; thus, their energy consumption estimation and management are important issues to deal with energy minimization techniques, such as path planning, tasks scheduling etc. These techniques need to be tested, evaluated, and approved for different scenarios; however, this cannot be feasible in case of huge scenarios and may require much hardware setup. In this paper, we introduce a numerical solution by enriching the Robot Operating System (ROS) infrastructure with modelling, monitoring, and energy management tools by integrating an energy consumption model of a differential drive mobile robot into ROS/Gazebo-based simulator. The obtained results involve realtime power consumption of the virtual robot for predefined scenarios, and the total energy consumption is monitored numerically at the end of each scenario without any hardware requirement.
Near-Optimal covering solution for USV coastal monitoring using PAES
(Springer, 2022) Ouelmokhtar, Hand; Benmoussa, Yahia; Diguet, Jean-Philippe; Benazzouz, Djamel; Lemarchand, Laurent
This paper addresses a multi-objective optimization problem for marine monitoring using USV. The objectives are to cover the maximum area with the lowest energy cost while avoiding collisions. The problem is solved using an exact and heuristic methods. First, a multi-objective Mixed Integer Programming formulation is proposed to model the USV monitoring problem. It consists of a combination of the Covering Salesman Problem (CSP) and Travelling Salesman Problem with Profit (TSPP). Then, we use CPLEX software to provide exact solutions. On the other hand, a customized chromosome-size algorithm is used to find heuristic solution. The latter is a multi-objective evolutionary algorithm known as Pareto Archived Evolution Strategy (PAES). The obtained results showed that the exact solving of the USV monitoring mission problem with mixed-integer programming (MIP) methods needs extensive computational costs. However, the customized PAES was able to provide Near-optimal solutions for large-size graphs in much faster time as compared to the exact one
Optimization of Energy Models and Behaviors in the Development of Mobile Robotics Applications
(2018) Touzout, Walid; Benmoussa, Yahia; Benazzouz, Djamel; Senn, Eric; Ouelmokhtar, Hand
Mobile robots are widely used in many applications, but their energy limitation is one of the most important challenges. The goal of our research work is to enrich an infrastructure of an embedded computer with tools of modeling, monitoring and energy management in mobile robotics applications. The proposed approach must guarantee the accomplishment of critical missions by maximizing the autonomy of devices, as well as their maintenance in operational state. In this contribution, we present the state of the art of some related works by showing the general concept of energy consumption and minimization of mobile robots and some recent related works concerning energy behaviors modeling and optimization.
Unmanned surface vehicle energy consumption modelling under various realistic disturbances integrated into simulation environment
(Elsevier, 2021) Touzout, Walid; Benmoussa, Yahia; Benazzouz, Djamel; Moreac, Erwan; Diguet, Jean-Philippe
Energy consumption estimation and management of the maritime Unmanned Surface Vehicles (USV) is an important issue to deal with energy minimization techniques such as path planning, tasks scheduling, etc. In this paper, we introduce the energy consumption parameter in USV simulation through three contributions: 1) An analytic USV's energy consumption model is developed based on the three-degrees-of-freedom dynamic model of surface vessels. 2) A reverse engineering approach is proposed to identify the previously used dynamic model parameters based on a set of scenarios executed within a recent simulation environment. 3) The simulator engine is enriched with the consumption modelling tools such that the power absorbed by the USV is instantaneously calculated and returned; thus, the required energy of any predefined scenario is available as a new simulation result