Internet of Drones (IoD) is an efficient technique that can be integrated with vehicular ad-hoc networks (VANETs) to provide terrestrial communications by acting as an aerial relay when terrestrial infrastructure is unreliable or unavailable. To fully exploit the drones' flexibility and superiority, we propose a novel dynamic IoD collaborative communication approach for urban VANETs. Unlike most of the existing approaches, the IoD nodes are dynamically deployed based on current locations of ground vehicles to effectively mitigate inevitable isolated cars in conventional VANETs. For efficiently coordinating IoD, we model IoD to optimize coverage based on the location of vehicles. The goal is to obtain an efficient IoD deployment to maximize the number of covered vehicles, i.e., minimize the number of isolated vehicles in the target area. More importantly, the proposed approach provides sufficient interconnections between IoD nodes. To do so, an improved version of succinct population-based meta-heuristic, namely Improved Particle Swarm Optimization (IPSO) inspired by food searching behavior of birds or fishes flock, is implemented for IoD assisted VANET (IoDAV). Moreover, the coverage, received signal quality, and IoD connectivity are achieved by IPSO's objective function for optimal IoD deployment at the same time. We carry out an extensive experiment based on the received signal at floating vehicles to examine the proposed IoDAV performance. We compare the results with the baseline VANET with no IoD (NIoD) and Fixed IoD assisted (FIoD). The comparisons are based on the coverage percentage of the ground vehicles and the quality of the received signal. The simulation results demonstrate that the proposed IoDAV approach allows finding the optimal IoD positions throughout the time based on the vehicle's movements and achieves better coverage and better quality of the received signal by finding the most appropriate IoD position compared with NIoD and FIoD schemes.

The increasing popularity of unmanned aerial vehicles (UAVs), commonly known as drones, in various fields is primarily due to their agility, quick deployment, flexibility, and excellent mobility. Particularly, the Internet of Drones (IoD)—a networked UAV system—has gained broad-spectrum attention for its potential applications. However, threat-prone environments, characterized by obstacles, pose a challenge to the safety of drones. One of the key challenges in IoD formation is path planning, which involves determining optimal paths for all UAVs while avoiding obstacles and other constraints. Limited battery life is another challenge that limits the operation time of UAVs. To address these issues, drones require efficient collision avoidance and energy-efficient strategies for effective path planning. This study focuses on using meta-heuristic algorithms, recognized for their robust global optimization capabilities, to solve the UAV path-planning problem. We model the path-planning problem as an optimization problem that aims to minimize energy consumption while considering the threats posed by obstacles. Through extensive simulations, this research compares the effectiveness of particle swarm optimization (PSO), improved PSO (IPSO), comprehensively improved PSO (CIPSO), the artificial bee colony (ABC), and the genetic algorithm (GA) in optimizing the IoD’s path planning in obstacle-dense environments. Different performance metrics have been considered, such as path optimality, energy consumption, straight line rate (SLR), and relative percentage deviation (RPD). Moreover, a nondeterministic test is applied, and a one-way ANOVA test is obtained to validate the results for different algorithms. Results indicate IPSO’s superior performance in terms of IoD formation stability, convergence speed, and path length efficiency, albeit with a longer run time compared to PSO and ABC.

This paper presents a comparative analysis of a fractional-order proportional–integral–derivative (FO-PID) controller against the standard proportional–integral–derivative (PID) controller, applied to a nonlinear robotic arm manipulator systems. The genetic algorithm (GA) optimization method was implemented to tune the gain parameters of the FO-PID and PID controllers. The performance of the FO-PID and PID controllers were evaluated though different cost functions, including integral of squared error (ISE), integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of time-weighted squared error (ITSE). The performance of these controllers was examined via extensive simulations by using MATLAB/SIMULINK for different operating scenarios of the robotic arm manipulator system. Based on the obtained results, a comparative performance matrix is proposed, wherein cost functions ISE, IAE, ITAE, and ITSE are represented as columns while characteristic parameters (overshoot, rising time, and settling time) are represented as rows. The proposed performance matrix facilitates the selection between the PID and FO-PID controllers.

Autonomous mobile robots (AMRs) are increasingly used in various applications, including transportation, logistics, and healthcare. One of the central challenges in AMR deployment is path planning, which involves finding an optimal and collision-free route from the robot’s initial position to its destination. This review, which builds upon a two-part study, presents a comprehensive overview of state-of-the-art techniques for AMR path planning. This paper focuses on classical and heuristic-based strategies, providing valuable insights into their foundational roles in autonomous mobile robot navigation. We categorize existing approaches based on their core principles, including graph-based, heuristic-based, metaheuristic-based, and learning/reasoning-based methods. For each category, we analyze the strengths and limitations of the representative techniques and evaluate their effectiveness in various operational scenarios. Furthermore, we identify emerging research directions and ongoing challenges in AMR path planning, such as multi-robot coordination, adaptation to dynamic environments, and integration with human–robot interaction frameworks.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in recent years thanks to their agility, mobility, and cost-effectiveness. However, UAV navigation presents several challenges, particularly in path planning, which requires determining an optimal route while avoiding obstacles and adhering to various constraints. Another critical challenge is the limited flight time imposed by the onboard battery. This paper introduces a novel approach for energy-efficient three-dimensional online path planning for UAV formations operating in complex environments. We formulate the path planning problem as a minimization optimization problem, and employ Mixed-Integer Linear Programming (MILP) to achieve optimal solutions. The cost function is designed to minimize energy consumption while considering the inter-collision and intra-collision avoidance constraints within a limited detection range. To achieve this, an optimization approach incorporating Receding Horizon Control (RHC) is applied. The entire path is divided into segments or sub-paths, with constraints used to avoid collisions with obstacles and other members of the fleet. The proposed optimization approach enables fast navigation through dense environments and ensures a collision-free path for all UAVs. A path-smoothing strategy is proposed to further reduce energy consumption caused by sharp turns. The results demonstrate the effectiveness and accuracy of the proposed approach in dense environments with high risk of collision. We compared our proposed approach against recent works, and the results illustrate that the proposed approach outperforms others in terms of UAV formation, number of collisions, and partial path generation time.