What are the most effective robot path planning algorithms?

3 Potential field methods

Potential field methods model the environment as a potential field that exerts attractive and repulsive forces on the robot. The robot then follows the gradient of the potential field to reach the goal while avoiding obstacles. Potential field methods are easy to implement and can handle dynamic environments, but they have some problems, such as local minima, oscillations, and sensitivity to parameters.

5 Learning-based methods

Learning-based methods use machine learning techniques, such as neural networks, reinforcement learning, or imitation learning, to learn a policy or a model that guides the robot's path planning. The robot then uses the learned policy or model to generate paths that adapt to new situations and feedback. Learning-based methods can achieve human-like or superhuman performance, but they have some issues, such as data dependency, generalization, and explainability.

6 Hybrid methods

Hybrid methods combine two or more of the above methods to leverage their strengths and overcome their weaknesses. For example, a hybrid method might use a grid-based method for global planning and a potential field method for local planning, or a sampling-based method for exploration and a learning-based method for exploitation. Hybrid methods can offer more robustness and efficiency than single methods, but they have some trade-offs, such as increased complexity and compatibility.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?