Motion-encoded particle swarm optimization for moving target search using UAVs

Advances in Sparrow Search Algorithm: A Comprehensive Survey

Mathematical programming and meta-heuristics are two types of optimization methods. Meta-heuristic algorithms can identify optimal/near-optimal solutions by mimicking natural behaviours or occurrences and provide benefits such as simplicity of execution, a few parameters, avoidance of local optimization, and flexibility. Many meta-heuristic algorithms have been introduced to solve optimization issues, each of which has advantages and disadvantages. Studies and research on presented meta-heuristic algorithms in prestigious journals showed they had good performance in solving hybrid, improved and mutated problems. This paper reviews the sparrow search algorithm (SSA), one of the new and robust algorithms for solving optimization problems. This paper covers all the SSA literature on variants, improvement, hybridization, and optimization. According to studies, the use of SSA in the mentioned areas has been equal to 32%, 36%, 4%, and 28%, respectively. The highest percentage belongs to Improved, which has been analyzed by three subsections: Meat-Heuristics, artificial neural networks, and Deep Learning.

A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain

Swarm Intelligence (SI) is one of the research fields that has continuously attracted researcher attention in these last two decades. The flexibility and a well-known decentralized collective behavior of its algorithm make SI a suitable candidate to be implemented in the swarm robotics domain for real-world optimization problems such as target search tasks. Since the introduction of Particle Swarm Optimization (PSO) as a representation of the SI algorithm, it has been widely accepted and utilized especially in local and global search strategies. Because of its simplicity, effectiveness, and low computational cost, PSO has retained popularity notably in the swarm robotics domain, and many improvements have been proposed. Target search problems are one of the areas that have been continuously solved by PSO. This article set out to analyze and give the inside view of the existing literature on PSO strategies towards target search problems. Based on the procedure of PRISMA Statement review method, a systematic review identified 51 related research studies. After further analysis of these total 51 selected articles and consideration on the PSO components, target search components, and research field components, resulting in nine main elements related to the discussed topic. The elements are PSO variant, application field, PSO inertial weight function, PSO efficiency improvement, PSO termination criteria, target available, target mobility status, experiment framework, and environment complexity. Several recommendations, opinions, and perfectives on the discussed topic are presented. Finally, recommendations for future research in this domain are represented to support future developments.

Multi-UAV Cooperative Anti-Submarine Search Based on a Rule-Driven MAC Scheme

In order to enhance the anti-submarine capability of multi-unmanned aerial vehicles (multi-UAVs) in the unknown sea environment and improve the search efficiency, in this paper, we propose a rule-inspired-multi-ant colony (RI-MAC)-based UAV cooperative search algorithm. First, a special sea area anti-submarine search model is established, including an association rule-driven target probability map (TPM) model, a UAV kinematics model, and a sensor model. The novel model has the characteristics of rule linkage, which effectively improves the accuracy of target detection probability in unknown environments. Secondly, according to the established search model, a multi-objective utility function based on association rules is derived. In order to solve the problem of multi-objective optimization, an RI-MAC algorithm based on association rules is proposed, and a pheromone update method using threat avoidance is designed to optimize the search path of multi-UAVs. Finally, a simulation experiment is conducted to verify the effectiveness and superiority of the proposed search algorithm.

Robotic autonomous systems for earthmoving equipment operating in volatile conditions and teaming capacity: a survey

There has been an increasing interest in the application of robotic autonomous systems (RASs) for construction and mining, particularly the use of RAS technologies to respond to the emergent issues for earthmoving equipment operating in volatile environments and for the need of multiplatform cooperation. Researchers and practitioners are in need of techniques and developments to deal with these challenges. To address this topic for earthmoving automation, this paper presents a comprehensive survey of significant contributions and recent advances, as reported in the literature, databases of professional societies, and technical documentation from the Original Equipment Manufacturers (OEM). In dealing with volatile environments, advances in sensing, communication and software, data analytics, as well as self-driving technologies can be made to work reliably and have drastically increased safety. It is envisaged that an automated earthmoving site within this decade will manifest the collaboration of bulldozers, graders, and excavators to undertake ground-based tasks without operators behind the cabin controls; in some cases, the machines will be without cabins. It is worth for relevant small- and medium-sized enterprises developing their products to meet the market demands in this area. The study also discusses on future directions for research and development to provide green solutions to earthmoving.

Motion-Encoded Electric Charged Particles Optimization for Moving Target Search Using Unmanned Aerial Vehicles

In this paper, a new optimization algorithm called motion-encoded electric charged particles optimization (ECPO-ME) is developed to find moving targets using unmanned aerial vehicles (UAV). The algorithm is based on the combination of the ECPO (i.e., the base algorithm) with the ME mechanism. This study is directly applicable to a real-world scenario, for instance the movement of a misplaced animal can be detected and subsequently its location can be transmitted to its caretaker. Using Bayesian theory, finding the location of a moving target is formulated as an optimization problem wherein the objective function is to maximize the probability of detecting the target. In the proposed ECPO-ME algorithm, the search trajectory is encoded as a series of UAV motion paths. These paths evolve in each iteration of the ECPO-ME algorithm. The performance of the algorithm is tested for six different scenarios with different characteristics. A statistical analysis is carried out to compare the results obtained from ECPO-ME with other well-known metaheuristics, widely used for benchmarking studies. The results found show that the ECPO-ME has great potential in finding moving targets, since it outperforms the base algorithm (i.e., ECPO) by as much as 2.16%, 5.26%, 7.17%, 14.72%, 0.79% and 3.38% for the investigated scenarios, respectively.

Drone Swarms as Networked Control Systems by Integration of Networking and Computing

The study of multi-agent systems such as drone swarms has been intensified due to their cooperative behavior. Nonetheless, automating the control of a swarm is challenging as each drone operates under fluctuating wireless, networking and environment constraints. To tackle these challenges, we consider drone swarms as Networked Control Systems (NCS), where the control of the overall system is done enclosed within a wireless communication network. This is based on a tight interconnection between the networking and computational systems, aiming to efficiently support the basic control functionality, namely data collection and exchanging, decision-making, and the distribution of actuation commands. Based on a literature analysis, we do not find revision papers about design of drone swarms as NCS. In this review, we introduce an overview of how to develop self-organized drone swarms as NCS via the integration of a networking system and a computational system. In this sense, we describe the properties of the proposed components of a drone swarm as an NCS in terms of networking and computational systems. We also analyze their integration to increase the performance of a drone swarm. Finally, we identify a potential design choice, and a set of open research challenges for the integration of network and computing in a drone swarm as an NCS.