Multi-Robot Exploration of Unknown Space Using Combined Meta-Heuristic Salp Swarm Algorithm and Deterministic Coordinated Multi-Robot Exploration.

Multi-robot exploration means constructing a finite map using a group of robots in an obstacle chaotic space. Uncertainties are reduced by distributing search tasks to robots and computing the best action in real time. Many previous methods are based on deterministic or meta-heuristic algorithms, but limited work has combined both techniques to consolidate both classes' benefits and alleviate their drawbacks. This paper proposes a new hybrid method based on deterministic coordinated multi-robot exploration (CME) and the meta-heuristic salp swarm algorithm (SSA) to perform the search of a space. The precedence of adjacent cells around a robot is determined by deterministic CME using cost and utility. Then, the optimization process of the search space, improving the overall solution, is achieved utilizing the SSA. Three performance measures are considered to evaluate the performance of the proposed method: run time, percentage of the explored area, and the number of times when a method failed to continue a complete run. Experimental results compared four different methods, CME-GWO, CME-GWOSSA, CME-SCA, and CME, over seven maps with extra complexity varying from simple to complex. The results demonstrate how the proposed CME-SSA can outperform the four other methods. Moreover, the simulation results demonstrate that the proposed CME-SSA effectively distributes the robots over the search space to run successfully and obtain the highest exploration rate in less time.

Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms.

Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied. This method combines the robot grid map modeling with image processing, bringing in calibration objects. By converting non-standard actual environment maps into standard grid maps, this method was made suitable for robot motion path planning on non-standard maps of different types and sizes. After obtaining the planned path and pose, the robot motion path planning map under the non-standard map was obtained by combining the planned path and pose with the non-standard real environment map. The experimental results showed that this method has a high adaptability to robot non-standard map motion planning, can realize robot path planning under non-standard real environment maps, and can make the obtained robot motion path display more intuitive and convenient.

Improved Robot Path Planning Method Based on Deep Reinforcement Learning.

With the advancement of robotics, the field of path planning is currently experiencing a period of prosperity. Researchers strive to address this nonlinear problem and have achieved remarkable results through the implementation of the Deep Reinforcement Learning (DRL) algorithm DQN (Deep Q-Network). However, persistent challenges remain, including the curse of dimensionality, difficulties of model convergence and sparsity in rewards. To tackle these problems, this paper proposes an enhanced DDQN (Double DQN) path planning approach, in which the information after dimensionality reduction is fed into a two-branch network that incorporates expert knowledge and an optimized reward function to guide the training process. The data generated during the training phase are initially discretized into corresponding low-dimensional spaces. An "expert experience" module is introduced to facilitate the model's early-stage training acceleration in the Epsilon-Greedy algorithm. To tackle navigation and obstacle avoidance separately, a dual-branch network structure is presented. We further optimize the reward function enabling intelligent agents to receive prompt feedback from the environment after performing each action. Experiments conducted in both virtual and real-world environments have demonstrated that the enhanced algorithm can accelerate model convergence, improve training stability and generate a smooth, shorter and collision-free path.

A Flower Pollination Optimization Algorithm Based on Cosine Cross-Generation Differential Evolution.

The flower pollination algorithm (FPA) is a novel heuristic optimization algorithm inspired by the pollination behavior of flowers in nature. However, the global and local search processes of the FPA are sensitive to the search direction and parameters. To solve this issue, an improved flower pollination algorithm based on cosine cross-generation differential evolution (FPA-CCDE) is proposed. The algorithm uses cross-generation differential evolution to guide the local search process, so that the optimal solution is achieved and sets cosine inertia weights to increase the search convergence speed. At the same time, the external archiving mechanism and the adaptive adjustment of parameters realize the dynamic update of scaling factor and crossover probability to enhance the population richness as well as reduce the number of local solutions. Then, it combines the cross-generation roulette wheel selection mechanism to reduce the probability of falling into the local optimal solution. In comparing to the FPA-CCDE with five state-of-the-art optimization algorithms in benchmark functions, we can observe the superiority of the FPA-CCDE in terms of stability and optimization features. Additionally, we further apply the FPA-CCDE to solve the robot path planning issue. The simulation results demonstrate that the proposed algorithm has low cost, high efficiency, and attack resistance in path planning, and it can be applied to a variety of intelligent scenarios.

Rank-driven salp swarm algorithm with orthogonal opposition-based learning for global optimization.

Salp swarm algorithm (SSA) is a relatively new and straightforward swarm-based meta-heuristic optimization algorithm, which is inspired by the flocking behavior of salps when foraging and navigating in oceans. Although SSA is very competitive, it suffers from some limitations including unbalanced exploration and exploitation operation, slow convergence. Therefore, this study presents an improved version of SSA, called OOSSA, to enhance the comprehensive performance of the basic method. In preference, a new opposition-based learning strategy based on optical lens imaging principle is proposed, and combined with the orthogonal experimental design, an orthogonal lens opposition-based learning technique is designed to help the population jump out of a local optimum. Next, the scheme of adaptively adjusting the number of leaders is embraced to boost the global exploration capability and improve the convergence speed. Also, a dynamic learning strategy is applied to the canonical methodology to improve the exploitation capability. To confirm the efficacy of the proposed OOSSA, this paper uses 26 standard mathematical optimization functions with various features to test the method. Alongside, the performance of the proposed methodology is validated by Wilcoxon signed-rank and Friedman statistical tests. Additionally, three well-known engineering optimization problems and unknown parameters extraction issue of photovoltaic model are applied to check the ability of the OOSA algorithm to obtain solutions to intractable real-world problems. The experimental results reveal that the developed OOSSA is significantly superior to the standard SSA, currently popular SSA-based algorithms, and other state-of-the-artmeta-heuristic algorithms for solving numerical optimization, real-world engineering optimization, and photovoltaic model parameter extraction problems. Finally, an OOSSA-based path planning approach is developed for creating the shortest obstacle-free route for autonomous mobile robots. Our introduced method is compared with several successful swarm-based metaheuristic techniques in five maps, and the comparative results indicate that the suggested approach can generate the shortest collision-free trajectory as compared to other peers.

Improved RRT-Connect Algorithm Based on Triangular Inequality for Robot Path Planning.

This paper proposed a triangular inequality-based rewiring method for the rapidly exploring random tree (RRT)-Connect robot path-planning algorithm that guarantees the planning time compared to the RRT algorithm, to bring it closer to the optimum. To check the proposed algorithm's performance, this paper compared the RRT and RRT-Connect algorithms in various environments through simulation. From these experimental results, the proposed algorithm shows both quicker planning time and shorter path length than the RRT algorithm and shorter path length than the RRT-Connect algorithm with a similar number of samples and planning time.

Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing.

Large scale operational areas often require multiple service robots for coverage and task parallelism. In such scenarios, each robot keeps its individual map of the environment and serves specific areas of the map at different times. We propose a knowledge sharing mechanism for multiple robots in which one robot can inform other robots about the changes in map, like path blockage, or new static obstacles, encountered at specific areas of the map. This symbiotic information sharing allows the robots to update remote areas of the map without having to explicitly navigate those areas, and plan efficient paths. A node representation of paths is presented for seamless sharing of blocked path information. The transience of obstacles is modeled to track obstacles which might have been removed. A lazy information update scheme is presented in which only relevant information affecting the current task is updated for efficiency. The advantages of the proposed method for path planning are discussed against traditional method with experimental results in both simulation and real environments.

Hybrid Whale Optimization with a Firefly Algorithm for Function Optimization and Mobile Robot Path Planning.

With the wide application of mobile robots, mobile robot path planning (MRPP) has attracted the attention of scholars, and many metaheuristic algorithms have been used to solve MRPP. Swarm-based algorithms are suitable for solving MRPP due to their population-based computational approach. Hence, this paper utilizes the Whale Optimization Algorithm (WOA) to address the problem, aiming to improve the solution accuracy. Whale optimization algorithm (WOA) is an algorithm that imitates whale foraging behavior, and the firefly algorithm (FA) is an algorithm that imitates firefly behavior. This paper proposes a hybrid firefly-whale optimization algorithm (FWOA) based on multi-population and opposite-based learning using the above algorithms. This algorithm can quickly find the optimal path in the complex mobile robot working environment and can balance exploitation and exploration. In order to verify the FWOA's performance, 23 benchmark functions have been used to test the FWOA, and they are used to optimize the MRPP. The FWOA is compared with ten other classical metaheuristic algorithms. The results clearly highlight the remarkable performance of the Whale Optimization Algorithm (WOA) in terms of convergence speed and exploration capability, surpassing other algorithms. Consequently, when compared to the most advanced metaheuristic algorithm, FWOA proves to be a strong competitor.

Robot path planning based on artificial potential field with deterministic annealing.

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.

A chaos-based adaptive equilibrium optimizer algorithm for solving global optimization problems.

The equilibrium optimizer (EO) algorithm is a newly developed physics-based optimization algorithm, which inspired by a mixed dynamic mass balance equation on a controlled fixed volume. The EO algorithm has a number of strengths, such as simple structure, easy implementation, few parameters and its effectiveness has been demonstrated on numerical optimization problems. However, the canonical EO still presents some drawbacks, such as poor balance between exploration and exploitation operation, tendency to get stuck in local optima and low convergence accuracy. To tackle these limitations, this paper proposes a new EO-based approach with an adaptive gbest-guided search mechanism and a chaos mechanism (called a chaos-based adaptive equilibrium optimizer algorithm (ACEO)). Firstly, an adaptive gbest-guided mechanism is injected to enrich the population diversity and expand the search range. Next, the chaos mechanism is incorporated to enable the algorithm to escape from the local optima. The effectiveness of the developed ACEO is demonstrated on 23 classical benchmark functions, and compared with the canonical EO, EO variants and other frontier metaheuristic approaches. The experimental results reveal that the developed ACEO method remarkably outperforms the canonical EO and other competitors. In addition, ACEO is implemented to solve a mobile robot path planning (MRPP) task, and compared with other typical metaheuristic techniques. The comparison indicates that ACEO beats its competitors, and the ACEO algorithm can provide high-quality feasible solutions for MRPP.

Adaptive Guided Equilibrium Optimizer with Spiral Search Mechanism to Solve Global Optimization Problems.

The equilibrium optimizer (EO) is a recently developed physics-based optimization technique for complex optimization problems. Although the algorithm shows excellent exploitation capability, it still has some drawbacks, such as the tendency to fall into local optima and poor population diversity. To address these shortcomings, an enhanced EO algorithm is proposed in this paper. First, a spiral search mechanism is introduced to guide the particles to more promising search regions. Then, a new inertia weight factor is employed to mitigate the oscillation phenomena of particles. To evaluate the effectiveness of the proposed algorithm, it has been tested on the CEC2017 test suite and the mobile robot path planning (MRPP) problem and compared with some advanced metaheuristic techniques. The experimental results demonstrate that our improved EO algorithm outperforms the comparison methods in solving both numerical optimization problems and practical problems. Overall, the developed EO variant has good robustness and stability and can be considered as a promising optimization tool.

An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning.

This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves ß and δ with poor fitness in the position updating strategy; the second-best ß wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results.

Velocity clamping-assisted adaptive salp swarm algorithm: balance analysis and case studies.

Salp swarm algorithm (SSA) is a recently proposed, powerful swarm-intelligence based optimizer, which is inspired by the unique foraging style of salps in oceans. However, the original SSA suffers from some limitations including immature balance between exploitation and exploration operators, slow convergence and local optimal stagnation. To alleviate these deficiencies, a modified SSA (called VC-SSA) with velocity clamping strategy, reduction factor tactic, and adaptive weight mechanism is developed. Firstly, a novel velocity clamping mechanism is designed to boost the exploitation ability and the solution accuracy. Next, a reduction factor is arranged to bolster the exploration capability and accelerate the convergence speed. Finally, a novel position update equation is designed by injecting an inertia weight to catch a better balance between local and global search. 23 classical benchmark test problems, 30 complex optimization tasks from CEC 2017, and five engineering design problems are employed to authenticate the effectiveness of the developed VC-SSA. The experimental results of VC-SSA are compared with a series of cutting-edge metaheuristics. The comparisons reveal that VC-SSA provides better performance against the canonical SSA, SSA variants, and other well-established metaheuristic paradigms. In addition, VC-SSA is utilized to handle a mobile robot path planning task. The results show that VC-SSA can provide the best results compared to the competitors and it can serve as an auxiliary tool for mobile robot path planning.