In this paper, optimal sizing of a photovoltaic (PV) pumping system with a water storage tank (WST) is developed to meet the water demand to minimize the life cycle cost (LCC) and satisfy the probability of interrupted water (p_IW) constraint considering real region data. The component sizing, including the PV resources and the WST, is determined optimally based on LCC and p_IW using a new meta-heuristic method named enhanced artificial rabbits optimization (EARO) via a nonlinear inertia weight reduction strategy to overcome the premature convergence of its conventional algorithm. The WST is sized optimally regarding the lack of irradiation and inaccessibility of the pumping system so that it is able to improve the water supply reliability. The LCC for water extraction heights of 5 and 10 m is obtained at 0.2955 M$ and 0.2993 M$, respectively, and the p_IW in these two scenarios is calculated as zero, which means the complete and reliable supply of the water demand of the customers using the proposed methodology based on the EARO. Also, the results demonstrated the superior performance of EARO in comparison with artificial rabbits optimization (ARO) and particle swarm optimization (PSO); these methods have supplied customers’ water demands with higher costs and lower reliability than the proposed EARO method. Also, during the sensitivity analysis, the results showed that changes in the irradiance and height of the water extraction have a considerable effect on the cost and ability to meet customer demand.

Mars has long captivated the human imagination as a potential destination for settlement and scientific exploration. After deploying individual rovers, the next step in our journey to Mars is the autonomous exploration of the Red Planet using a collaborative swarm of rovers, drones, and satellites. This concept paper envisions a sustainable Mars exploration scenario featuring energy-aware, collaborative, and autonomous vehicles, including rovers, drones, and satellites, operating around Mars. The proposed framework is designed to address key challenges in energy management, edge intelligence, communication, sensing, resource-aware task scheduling, and radiation hardening. This work not only identifies these critical areas of research but also proposes novel technological solutions drawn from terrestrial advancements to extend their application to extraterrestrial exploration.

The design of robot dedicated to person with disabilities necessitate users implication in all steps of product development: design solution, prototyping the system, choice of users interfaces, and testing it with users in real conditions. However, before any design of any system, it is necessary to understand and meet the needs of the disabled users. In this paper, we describe our research activity on the integration of a robotic arm in the environment of disabled people who have lost the abilities to use their proper arms to perform daily living tasks and who are able to use an adapted robot to compensate, even partly, the problems of manipulation of objects in their environments. This paper presents our contribution for designing adaptable, configurable and personalized robot system based on the Manus robot. Some preliminary evaluation results are also presented.

Energy management in robotic systems has traditionally been driven by onboard energy limitations. In collaborative multi-robot systems, however, it concerns not only conserving energy but also improving how efficiently robots use their time, how reliably they operate, and how long they remain autonomous. This thesis investigates energy management strategies for collaborative multi-robot systems, emphasizing scenarios where both energy consumption and robots' time of use matter equally. It combines practical system development, realistic energy modeling, scheduling optimization, and energy-aware path planning, supported by both real robot deployment and simulation. The motivation arises from ambiently-powered systems that harvest renewable energy such as sunlight or other environmental sources. Since harvested energy rarely sustains robots fully, they must rely on charging infrastructure like docking stations or opportunistic wireless transfer. In such environments, efficiency means maintaining continuous task execution and maximizing productivity while minimizing interruptions caused by charging or idle states. These challenges are especially relevant in warehouse automation, environmental monitoring, agriculture, and surveillance. The first research question investigates managing idle energy consumption. A low-power Energy Management Module (EMM) was designed and deployed on a real TurtleBot3, enabling control of active, standby, off, and recovery modes. It reduced idle-mode power from roughly 7 W to 26.2 microwatts, showing hardware-level management can extend operational lifetime. The second question examines realistic energy modeling and embedding charging decisions into scheduling. Detailed consumption data from the TurtleBot3 built an accurate model integrated into ROS-Gazebo. Incorporating charging into scheduling reduced total mission makespan by up to 15%. The third question explores scaling scheduling to larger systems. An Adaptive Particle Swarm Optimization approach improved convergence, while a hybrid multiverse-based parallel framework exploited the swarm's own distributed processing, making scheduling five times faster. The fourth question extends scheduling to dynamic environments like UAVs. The EA-MAOSS framework integrated distributed reinforcement learning, adaptive charging, and digital twin-based modeling. It reduced makespan by up to 30%, increased cumulative rewards, and cut charging events by over 50% versus fixed-threshold approaches. The fifth question addresses exploiting energy-harvesting opportunities during navigation. The H-CMARL-DE framework combined hierarchically conditioned multi-agent reinforcement learning with meta-level differential evolution for decentralized coordinated path planning. It achieved up to 240% higher harvesting efficiency than classical ROS-OMPL planners and CTDE-based methods, while staying within 10% of shortest-path length. Collectively, these contributions integrate low-power hardware, realistic modeling, distributed optimization, reinforcement learning, and energy-aware navigation, improving the efficiency, scalability, and long-term autonomy of ambiently-powered robotic systems.