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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]

In order to solve the unmanned underwater vehicle two-dimensional autonomous path planning problem in the environment affected by ocean current and obstacles, this paper applies the improved Fireworks-Ant Colony Hybrid Algorithm to solve it. Firstly, a two-dimensional Lamb vortex ocean current environment model with randomly distributed obstacles is established, and the circular obstacle is equivalent to a square grid. Then, the mathematical model of path planning is established considering the energy consumption cost, navigation time cost and navigation distance cost. Finally, the improved fireworks-ant colony hybrid algorithm is applied to solve the nonlinear optimization problem, and this algorithm is compared with the basic ant colony algorithm for simulation experiments in the four different marine environments. The experimental results show that this algorithm can quickly find the global optimal solution, and the more complex the environment, the more obvious its advantages. The algorithm proposed in this paper provides a new way for autonomous path planning of underwater vehicles. [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]