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Addressing the challenges of low convergence accuracy and stagnation at local optima in the application of the golden jackal optimizer (GJO) to mobile robot path planning, this paper proposes a hybrid strategy-based golden jackal optimizer (HGJO) algorithm. The improved algorithm employs a pre-decreasing slow nonlinear energy decay strategy to balance the global and local search capabilities. The roulette wheel selection algorithm and Lévy flight strategy are introduced into the position update of the GJO algorithm, so the proposed algorithm avoids stagnation at the local optimum. The HGJO algorithm is evaluated against some state-of-the-art optimizers on 23 benchmark functions and the CEC2021 benchmark function. It is also applied to ablation experiments for mobile robot path planning. The experimental results show that the HGJO algorithm improves the average path length in path planning by 0.21%, 82.4%, and 7.9% over the original algorithm in three different environments under 30 independent experiments. • A new energy decreasing method is proposed. • A roulette wheel selection strategy is introduced. • A new hybrid position updating strategy is proposed. [ABSTRACT FROM AUTHOR]

Mobile robot path planning refers to the design of the safely collision-free path with shortest distance and least time-consuming from the starting point to the end point by a mobile robot autonomously. In this paper, a systematic review of mobile robot path planning techniques is presented. Firstly, path planning is classified into global path planning and local path planning according to the mastery of environmental information. In the global path planning, environment modeling methods and path evaluation method are introduced. The methods of environment modeling include grid method, topology method, geometric feature method and mixed representation method. In the local path planning, we introduce the sensors commonly used in the detection environment, including laser radar and visual sensor. Next, according to the characteristics of algorithms, mobile robot path planning algorithms are divided into three categories: classical algorithms, bionic algorithms and artificial intelligence algorithms. Among the classical algorithms, we introduce the cell decomposition method, sampling based method, graph search algorithm, artificial potential field method and dynamic window method. In the algorithm based on bionics, we introduce genetic algorithm, ant colony algorithm, gray wolf algorithm, etc. in detail. In artificial intelligence algorithm, we introduce neural network algorithm and fuzzy logic. Finally, we compare the key technologies of mobile robot path planning in the form of graphs and charts based on the classification statistics of the collected literature to provide references for future research. [ABSTRACT FROM AUTHOR]

This paper focuses on the mobile robot path planning problem of optimizing the performance metrics of bidirectional A* algorithm in randomized two-dimensional map environments. An algorithm called direction constraints adaptive extended bidirectional A* (DCAE-BA*), which is an improvement of the traditional target dynamic bidirectional A* algorithm (TTD-BA*), is proposed to improve the performance metrics of the algorithm. Regarding the improvement, we propose the adaptive extension method and the direction-constrained optimal node extension method (DCONE). Simulation experiments were conducted for DCAE-BA*, TTD-BA* and traditional A* algorithm (A*) in a large number of random two-dimensional map environments. The simulation experimental scenarios consider four types of start and end point relative directions and three obstacle proportions to objectively and comprehensively evaluate the performance of the proposed algorithms. The results show that different scenarios have a significant impact on the algorithm performance metrics. Finally, the overall performance of the proposed algorithm is evaluated with a large number of experiments in "random" scenarios, and the results show that DCAE-BA* obtains significantly better search time for all three obstacle proportions, and better path length and number of expanded nodes for 10% and 25% obstacle proportions. The effectiveness of the proposed DCAE-BA* algorithm is demonstrated, which provides an essential reference for the path planning of mobile robots in a random 2D map environment. • DCAE-BA* is improved by adaptive extension and direction-constrained optimal node extension (DCONE). • The Start-End Dir and obstacle proportions have a significant impact on the algorithm metrics. • Overall, the DCAE-BA* has the highest computational efficiency for all three types of obstacle proportion. • DCAE-BA* optimizes the path length in the "diagonal" scenario most significantly. • As the proportion of obstacles increases, the metric optimization effect decreases. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic-Window Approach) to study the obstacle avoidance problem of robots in complex continuous state space. First, the obstacle avoidance problem is converted into an optimal learning incentive problem, and the self-learning of the obstacle avoidance policy is realized based on DDPG; second, the DWA obstacle avoidance trajectory evaluation function is optimized using the DDPG reward incentive mechanism, and the Experience Replay mechanism; finally, the algorithm model is simulated. The experiments show that the model can significantly circumvent the deficiency of the DWA algorithm in limiting to the optimal local solution in a complex environment and solve the action output problem in the continuous velocity and turning angle value interval of the robot; through the trial and error interaction with the environment and the trajectory evaluation incentive feedback, the obstacle avoidance passing ability of the robot in a complex environment is improved. [Display omitted] • We propose an adaptive obstacle avoidance algorithm based on DDPG (Deep Deterministic Policy Gradient) and DWA (Dynamic Window Approach), named the DDPG-DWA. • The DDPG-DWA algorithm can solve the problem of action output in the continuous velocity and turning angle value range of the robot. • The DDPG-DWA algorithm can avoid the problem of DWA algorithm easily being limited to local obstacle avoidance optimal solutions in complex environments. • The model is suitable for path obstacle avoidance of intelligent agents in complex, narrow and irregular environments. [ABSTRACT FROM AUTHOR]

This paper aims to give an optimal path planning of a mobile robot in a known indoor environment. An algorithm based on deep learning, ray tracing algorithm, waiting rule, and Rapidly-exploring Random Tree is proposed to solve the problem of obstacle avoidance and path planning. Firstly, GoogLeNet is used to classify obstacles. Here, it helps to distinguish between static and dynamic obstacles. Secondly, for the static obstacle avoidance, the ray tracing algorithm is proposed to avoid the obstacles which are identified by GoogLeNet. And for the dynamic obstacle avoidance, this paper proposed a waiting rule for dynamic obstacle avoidance. Thirdly, the RRT method plans a path from the start point to the goal point. The novelty of this paper is that the type of obstacles is distinguished by deep learning, and the two different kinds of obstacles used ray tracing algorithm and waiting rule to avoid obstacles collision, respectively. In experimental results, rapidly-exploring random tree is compared with genetic algorithm, and particle swarm optimization method in static environment, and it is compared with artificial potential field approach in dynamic environment. Experiments are carried out in the two-dimensional environment and successfully applied to the path planning of mobile robots in multi-obstacles environment, and they prove the feasibility of the algorithm. [ABSTRACT FROM AUTHOR]

• Propose a sampling-based asymptotically optimal path planning algorithm. • The proposed algorithm guarantees a fast convergence rate. • Theoretical proof of asymptotic optimality and fast convergence rate is given. During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Most of the existing path planning algorithms require a prior global map. Although there have been some algorithms proposed for unknown environments, they can only deal with those which just have several hidden obstacles in a roughly known global map. In order to improve the efficiency of robot's path planning without prior global maps, this paper proposes a Convolutionally Evaluated Gradient First Search (CE-GFS) path planning algorithm. It allows the robot to collect environmental information and complete path planning simultaneously. Firstly, the Gradient First Search (GFS) algorithm is proposed based on the gradient score parameter, with which the conventional cost function is replaced. The GFS can adapt to any moving direction through the environmental information surrounding the mobile robot and computing the gradient score parameter. Secondly, CE-GFS path planning algorithm is proposed based on GFS and convolutional evaluation method. The CE-GFS helps the robots to evaluate the efficiency of the global path and get global perception ability, so that they are prevented from going astray, which can significantly improve the efficiency of path planning. Finally, several simulation tests and field tests have been carried out. The test results show that convolutional evaluation improves the efficiency of CE-GFS by 86.94% on average compared with GFS at the price of 0.18% decrease in path planning success rate. Moreover, the time cost of the proposed CE-GFS algorithm is 42.18% less than that of FAR-Planner in some special cases. • Our CE-GFS gets rid of this dependence on an exploration algorithm, and allows the robot to combine the exploration and path planning stages. • Cost functions in conventional grid map algorithms are replaced with gradient score in our algorithm, to make the robot more flexible. • Convolutional evaluation method is proposed to evaluate the efficiency of the global path through local information, which can prevent the robot from going astray. [ABSTRACT FROM AUTHOR]

• Combines exploration, mapping and coverage. • Best utilization of onboard sensors such as long range distance sensors. • Combines the advantages of offline and online coverage path planning. In this paper, we present a novel problem of simultaneous exploration and area coverage by multiple cooperating mobile robots. As the robots cover an initially unknown region, they perform intermittent exploration of the region and build a map, which in turn is used to plan the coverage path. We use a Voronoi partition based multi-robot coverage strategy using the Manhattan distance metric to solve the coverage problem and a frontier based exploration strategy for exploration mapping. We provide results of simulation using Matlab/V-rep environments to demonstrate the proposed multi-robot simultaneous exploration and coverage (MR-SimExCoverage) problem using the spanning tree based coverage (STC) algorithm. [ABSTRACT FROM AUTHOR]