Motion planning comprises one of the main cornerstones in autonomous mobile robotics, where obstacle avoidance and path planning efficiency are quintessential for the success of maneuverability applications. However, real-time implementation of path planning is limited by adaptive scenarios, high dimensional maps, and time constraints. This paper proposes a Double Deep Q-Network approach for path planning and obstacle avoidance of skid-steer mobile robots due to its ability to explore an extended navigation workspace, and to reduce over estimation bias produced by sparse rewards. The proposed DDPG approach was compared to Q-learning and Deep Q-Network (DQN) algorithms to examine path planning performance under changing simulation environments, intended to be similar to those found in mining. Results from several exploration trails show that DDQN enhances the path length and significantly outperforms QL and DQN regarding path following time, reducing it about 26% and 17%, respectively. Ongoing research is expected to have an impact on the energy resources of the robot in mining scenarios.