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What advanced motion planning techniques can you use with ROS?

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If you are a robotics enthusiast or researcher, you probably know about ROS, the Robot Operating System. ROS is a framework that provides tools, libraries, and conventions for developing and integrating robot applications. But did you know that ROS also offers advanced motion planning techniques that can help you solve complex and dynamic tasks? In this article, we will explore some of these techniques and how you can use them with ROS.

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Erencan Bulut

Robotics Research Assistant @Fraunhofer CML | M.Sc. @TU Hamburg
Atul Kant ??????????????

??Problem Solver ????Game Changer, Entrepreneur : 9K+ Family???????? Humanitarian ?? Diverse Experience solving all…

1 Sampling-based planners

One of the most popular motion planning techniques is sampling-based planning, which generates a roadmap or a tree of possible paths by randomly sampling the configuration space of the robot. Sampling-based planners can handle high-dimensional and non-holonomic problems, as well as deal with uncertainty and obstacles. ROS provides several packages that implement sampling-based planners, such as OMPL, MoveIt, and SBPL. You can use these packages to plan motions for different types of robots, such as manipulators, mobile robots, or humanoids.

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Atul Kant ??????????????

??Problem Solver ????Game Changer, Entrepreneur : 9K+ Family???????? Humanitarian ?? Diverse Experience solving all kinds of ?? problems. 5M+ Impressions, ?? Helping clients grow, scale Business rapidly??????
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As Leading Robotics Pro, A common way to plan how Robots move is by using sampling-based planning. This method creates a map of possible paths by randomly trying out different robot configurations. It's good for tricky problems, like when the robot can't move in all directions or when there's uncertainty or obstacles. ROS, robot software, has tools like OMPL, MoveIt, & SBPL that use this planning approach. With these tools, you can plan movements for various robots, like ones with arms, moving robots, or even human-like robots. Strategize with game theory, add context with semantic mapping, & address risks & self-awareness. Virtual reality aids, bio-inspired planning learns from nature, & uncertainty-aware planning handles unpredictability.

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2 Optimization-based planners

Another motion planning technique is optimization-based planning, which tries to find the optimal path that minimizes a cost function. Optimization-based planners can incorporate various criteria, such as smoothness, energy, collision avoidance, or task constraints. ROS supports optimization-based planners through packages like TrajOpt, CHOMP, or STOMP. You can use these packages to refine the paths generated by sampling-based planners or to directly plan motions that optimize your objectives.

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Erencan Bulut

Robotics Research Assistant @Fraunhofer CML | M.Sc. @TU Hamburg
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Imagine ROS-powered robots as dancers, and optimization-based planners as the choreographers fine-tuning their moves. These planners are like the creative minds behind a flawless dance routine, aiming to find the most elegant and efficient path for the robots. It's not just about moving from point A to point B; it's about the graceful flow, conserving energy, dodging obstacles like a seasoned dancer avoids missteps. ROS becomes the stage manager, coordinating the dance of robots with packages like TrajOpt, CHOMP, or STOMP, turning robotic motion into a beautifully orchestrated ballet.

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3 Learning-based planners

A more recent and emerging motion planning technique is learning-based planning, which uses machine learning methods to learn from data and improve the performance and adaptability of the planner. Learning-based planners can leverage prior knowledge, feedback, demonstrations, or reinforcement to learn motion policies or models that can cope with complex and dynamic environments. ROS enables learning-based planners through packages like DMP, GPT, or RRT*. You can use these packages to learn from human or expert demonstrations, to generalize to new situations, or to explore and exploit the environment.

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4 Multi-robot planners

A special case of motion planning is multi-robot planning, which involves coordinating the motions of multiple robots that share a common workspace or goal. Multi-robot planning poses additional challenges, such as communication, synchronization, collision avoidance, or task allocation. ROS facilitates multi-robot planning through packages like ROS-MPT, MRS, or MBF. You can use these packages to plan motions for cooperative or competitive scenarios, such as swarm robotics, formation control, or robot soccer.

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5 Reactive planners

A final motion planning technique that we will mention is reactive planning, which focuses on generating fast and robust responses to dynamic and unpredictable events. Reactive planners do not rely on a global model of the environment, but rather on local sensory information and feedback. Reactive planners can complement other planners by providing low-level control and recovery mechanisms. ROS supports reactive planners through packages like TEB, DWA, or VFH. You can use these packages to plan motions for navigation, obstacle avoidance, or emergency situations.

We hope that this article has given you an overview of some of the advanced motion planning techniques that you can use with ROS. Motion planning is a rich and active research field, and ROS is a powerful and versatile platform that can help you explore and implement new ideas and solutions. If you are interested in learning more about motion planning and ROS, you can check out the ROS wiki, the ROS tutorials, and the ROS community forums.

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6 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?

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Atul Kant ??????????????

??Problem Solver ????Game Changer, Entrepreneur : 9K+ Family???????? Humanitarian ?? Diverse Experience solving all kinds of ?? problems. 5M+ Impressions, ?? Helping clients grow, scale Business rapidly??????
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As a Leading Robotics professional, for future smart Robots, to move smartly using diverse methods, be mindful to testing paths, learning from experience, & adapting to Robot surroundings. They coordinate in swarms, consider risks, & use fuzzy logic. Techniques include sampling-based & optimization-based planning, learning from experience, & coordinating multiple robots. It involves strategic planning, semantic mapping, & understanding human intentions. This ensures effective movements, optimizing energy, staying responsive, & using explainable AI for understandable planning. Adapt with Reinforcement learning, manage diverse robots, refine decisions with Monte Carlo Tree Search, focus visually & stay responsive with real-time kinematics.

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What advanced motion planning techniques can you use with ROS?

1

2

3

4

5

6

1 Sampling-based planners

2 Optimization-based planners

3 Learning-based planners

4 Multi-robot planners

5 Reactive planners

6 Here’s what else to consider

Robotics

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What advanced motion planning techniques can you use with ROS?

1

2

3

4

5

6

1 Sampling-based planners

2 Optimization-based planners

3 Learning-based planners

4 Multi-robot planners

5 Reactive planners

6 Here’s what else to consider

Robotics

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