Robot Path Planning Using LSTM+Adaptive Greedy Weight and A*+Adaptive Greedy Weight Strategies

The 'lstmmov' video showcases Robot 1's dynamic obstacle avoidance capabilities, utilizing an advanced approach that combines the （Long Short-Term Memory) LSTM model with the adaptive greedy weight method. In this demonstration, Robot 1 effectively leverages the predictive power of LSTM to anticipate the positions and movements of other robots within the environment. By integrating these predictions with a safety threshold, the robot is able to make more informed and intelligent decisions, allowing it to proactively adjust its path and avoid potential collisions. This approach not only enhances the robot's ability to navigate through a complex and dynamic environment but also improves overall efficiency and safety. The video highlights how the combination of LSTM's predictive accuracy with the flexibility of adaptive greedy weight strategies enables Robot 1 to achieve a higher success rate in obstacle avoidance, demonstrating a significant advancement over traditional methods. This method showcases the potential for more intelligent and responsive robot navigation in real-world applications, where dynamic interactions with other agents are a critical factor.

The 'Amov' video illustrates how Robot 1 performs collision avoidance using the A* algorithm combined with the adaptive greedy weight strategy. While this approach allows the robot to navigate around obstacles effectively, it has certain limitations. Notably, the robot lacks the ability to predict the movements of other robots, which increases the risk of collisions, especially in the absence of a safety threshold. This limitation highlights the challenges of relying solely on reactive strategies in dynamic environments, where proactive collision avoidance is essential for ensuring safety and efficiency.