How do you use motion planning algorithms for robots?

1 What is motion planning?

Motion planning is the process of computing a sequence of actions or configurations that moves a robot from a given initial state to a desired final state, while satisfying some criteria of optimality, safety, and robustness. For example, a robot arm may need to plan how to reach a target object without hitting any other objects, or a mobile robot may need to plan how to drive from one room to another while avoiding walls and furniture. Motion planning algorithms can be classified into two main categories: discrete and continuous. Discrete motion planning algorithms deal with finite sets of states and actions, such as grid-based maps and graph search methods. Continuous motion planning algorithms deal with infinite sets of states and actions, such as geometric models and sampling-based methods.

3 How to solve a motion planning problem?

The second step to use motion planning algorithms for robots is to apply a suitable algorithm that can find a solution to the motion planning problem, or report that no solution exists. Depending on the characteristics of the problem and the requirements of the solution, there are many different algorithms that can solve motion planning problems. Graph search algorithms are discrete and efficient, but may not be able to handle high-dimensional or continuous state spaces. Alternatively, sampling-based algorithms are continuous and flexible, but may not guarantee optimality or completeness. Finally, optimization-based algorithms can handle complex cost functions and constraints, however they may be sensitive to initial conditions and local minima.

4 How to evaluate a motion planning solution?

In the third step of using motion planning algorithms for robots, it is necessary to evaluate the solution produced by the algorithm and assess whether it meets the expectations and specifications of the problem. This evaluation involves measuring the feasibility, optimality, and robustness of the solution. Feasibility can be verified by testing it on a simulator or a real robot and looking for any collisions or violations. Optimality can be measured by comparing the solution with the lower and upper bounds of the cost function, or with other solutions from different algorithms or methods. To assess robustness, variations or perturbations should be introduced in problem parameters such as initial state, goal state, obstacle space, or cost function to see how well the solution adapts or degrades.

5 How to improve a motion planning solution?

The fourth step to use motion planning algorithms for robots is to improve the solution that the algorithm produces, making it more suitable for real-world applications and scenarios. This can be done by applying post-processing techniques such as smoothing, simplifying, or refining the solution to reduce its length, complexity, or roughness. Feedback control can also be used to adjust the solution during its execution to correct any errors or deviations due to uncertainties or disturbances. This may involve tracking, stabilizing, or replanning the solution to maintain its feasibility, optimality, or robustness. Additionally, learning can be applied to update the algorithm or problem based on previous or current solutions to increase its efficiency or effectiveness. This may include tuning, adapting, or generalizing the algorithm or problem to reduce computation time, memory usage, or error rate.

6 How to choose a motion planning algorithm?

When it comes to selecting a motion planning algorithm for robots, there are several factors to consider, such as complexity, completeness, optimality, and scalability. Complexity is determined by the computational resources needed, such as time, space, or power, and is based on the size, dimension, and structure of the state space, action space, and obstacle space. On the other hand, completeness is based on the coverage, connectivity, and diversity of the algorithm. Optimality is determined by the consistency, monotonicity, and convexity of the algorithm in relation to the cost function. Finally, scalability depends on the flexibility, adaptability, and modularity of the algorithm in terms of representing and abstracting problems.

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?