Robot motion planning is a crucial aspect of robotics, as it enables robots to navigate through complex environments while avoiding obstacles and reaching their goals. Two prominent approaches to tackle this challenge are sampling-based planners and search-based planners. This blog post will introduce you to these two approaches, discuss their key characteristics, compare their strengths and weaknesses, and explore their potential future developments in the field of robot motion planning.

Sampling-based Planners

Sampling-based planners are a class of motion planning algorithms that generate a roadmap of the robot’s configuration space by sampling random points and connecting them with feasible paths. These planners are particularly well-suited for high-dimensional spaces and complex environments, as they can efficiently explore the space without requiring an explicit representation of the environment. Some popular sampling-based planners include:

• Probabilistic Roadmap (PRM): A PRM planner constructs a graph by sampling random configurations and connecting them with local planners, creating a roadmap that represents the connectivity of the free space.
• Rapidly-exploring Random Trees (RRT): RRT planners incrementally build a tree by sampling random configurations and connecting them to the nearest node in the tree, allowing for rapid exploration of the space.

Search-based Planners:

Search-based planners, on the other hand, are a class of motion planning algorithms that rely on searching through a discretized representation of the configuration space to find a feasible path. These planners typically employ graph-based search algorithms, such as A* or Dijkstra’s algorithm, to find the shortest or most optimal path from the robot’s initial position to the goal. Some popular search-based planners include:

• Grid-based Planners: These planners discretize the environment into a grid and use search algorithms to navigate through the cells while avoiding obstacles.
• Visibility Graph Planners: Visibility graph planners construct a graph by connecting the robot’s initial position, goal, and obstacle vertices, and use search algorithms to find the shortest path along the visibility graph.

Comparison

Sampling-based and search-based planners each have their strengths and weaknesses, which make them suitable for different types of motion planning tasks.

• Complexity: Sampling-based planners are well-suited for high-dimensional spaces and complex environments, as they can efficiently explore the space without requiring an explicit representation of the environment. In contrast, search-based planners may struggle in high-dimensional spaces due to the “curse of dimensionality” and increased computational complexity.
• Optimality: Search-based planners generally provide more optimal paths compared to sampling-based planners, as they explicitly search for the shortest or most efficient path. However, this optimality often comes at the cost of increased computational time and complexity.
• Convergence: Sampling-based planners can quickly generate approximate solutions, while search-based planners may require more time to find an optimal solution, especially in high-dimensional spaces or complex environments.

Future

As research in robot motion planning continues, it is expected that both sampling-based and search-based planners will see further developments and improvements, leading to more efficient and versatile solutions for various motion planning tasks. Some potential future directions include:

• Hybrid Approaches: Combining the strengths of both sampling-based and search-based planners in a single algorithm could lead to more efficient and robust motion planning solutions.
• Adaptive Sampling: Developing sampling strategies that adapt to the environment’s complexity and the robot’s dynamics could improve the performance of sampling-based planners.
• Learning-based Planners: Integrating machine learning techniques, such as reinforcement learning or deep learning, could enable planners to learn from experience and improve their performance over time.

In conclusion, both sampling-based and search-based planners offer unique approaches to robot motion planning, with each being well-suited for different types of tasks and environments. Sampling-based planners excel in high-dimensional spaces and complex environments, efficiently exploring the configuration space without requiring an explicit representation of the environment. On the other hand, search-based planners focus on finding optimal paths by leveraging graph-based search algorithms in discretized representations of the configuration space.

As research progresses and new advancements emerge, it is anticipated that both types of planners will see further improvements and refinements. By exploring hybrid approaches, adaptive sampling strategies, and incorporating machine learning techniques, researchers can develop more efficient, robust, and versatile motion planning solutions. As the field of robotics continues to evolve, the development of advanced motion planning algorithms will play a crucial role in enabling robots to navigate and interact with their environments safely and effectively.