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NODE’s view on Dynamic Path Planning for Mobile Robots

Unlocking Efficiency and Safety: Dynamic Path Planning for Mobile Robots

In the last post, we talked about the difference between AGV and AMR. There is one technology that plays a significant role in turning an AGV into an AMR, which we would like to introduce to you today: dynamic path planning.
Dynamic path planning is a crucial aspect of mobile robot applications, enabling robots to move safely and efficiently in complex and dynamic environments. The technology uses different approaches to determine the best path for the robot while avoiding obstacles and collisions.
Choosing the right approach depends on several factors, including the complexity of the environment, the degree of uncertainty and unpredictability, and the level of efficiency and safety required. Reactive control and potential field methods are suitable for simple and chaotic environments, while PRMs and MPC are better suited for complex environments. Machine learning approaches can be effective in situations where traditional approaches struggle, but they require a significant amount of data and computational resources.
Despite these challenges, advances in dynamic path planning algorithms and multi-robot coordination will continue to transform the industry and enable the use of mobile robots in flexible environments. We use dynamic path planning to enable our robots to adapt to unforeseen changes in warehouse environments while avoiding obstacles and collisions. This technology opens up new possibilities for automation and efficiency in various industries.
This technology is part of our NODE.move and NODE.execute products, which are part of our Robot Autonomy Skills. They enable mobile robots to perform tasks in a dynamic environment, avoid collisions and optimize autonomous path navigation, including A-to-B navigation. Which allows the robot to autonomously compute the global route to an any target position with an optional set of waypoints to pass. Therefore, the robot can execute the planned route and dynamically optimize the path locally, as well as avoid local obstacles.


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