What are the best practices for planning tool paths in robotics?

In order to do an effective tool path planning one must have a clear understanding of the requirements, the range of motion available, the nature of operation to be executed by the robot and most importantly the constraints. Once you have identified the above parameters, you can select a coordinate system that allows you to perform your operations in the best possible way and with least possible hindrances. In most of the cases a base coordinate system of the robot and the coordinate system of the work piece is determined and a relationship is established between them. Motion of all the joints and links of the robot are then calculated precisely. Finally the desired outcome is achieved by executing the program handling the robotic system.

Selecting the right Geometric Path Planning technique. One of the path planning techniques is through the use of Geometric Path Planning technique. When implementing the Geometric Path Planning in robotics we consider the 2D context which will be considered as a polygonal representation of C-obstacles. This will provide a richer geometric information for high-dimensional problem. One of the ideal Geometric Path Planning is using the S-Nav which is a novel semantic-geometric path planner for mobile robots. There are other Geometric path planning that you can also consider.

In my experience with robotics, best practices for planning tool paths involve meticulously simulating and programming paths to ensure efficiency and safety, incorporating real-time feedback systems for adaptive path correction, and continually updating algorithms with the latest data on environmental variables and obstacle dynamics. I prioritize redundancy in the path planning algorithms to handle unexpected scenarios, maintaining a balance between optimal path precision and the flexibility needed for real-world applications. Furthermore, I regularly validate and refine these paths through rigorous testing with the actual robotic systems to account for any deviations from the simulated environment.

there are multiple factors to consider in path planning for robotics, starting from understanding task requirements, modeling the robot's dynamics and environment, and defining the target and possible constraints. It is also necessary to consider collision detection for the planned trajectory/path. Upon initial planning, path optimization and smoothing are necessary.

Task Understanding: Clearly define the task and its requirements. Workspace Definition: Identify and define the robot's workspace and constraints. Optimize Trajectories: Minimize unnecessary movements for efficiency. Collision Avoidance: Implement collision detection and avoidance strategies. Smooth Motion: Plan smooth and continuous paths to reduce wear and tear. Path Segmentation: Break complex tasks into simpler sub-tasks for better planning. Feedback Integration: Use sensor feedback for real-time adjustments. Energy Efficiency: Optimize paths to minimize energy consumption. Safety: Prioritize safety by avoiding abrupt movements and considering human-robot interaction.

Generally, there are basically two types of planning, optimization based planning and sampling based planning Optimization based can prove feasible and realistic results, since it allows the engineer to incorporate the robot constraint, it tends to fail when the robot is exposed to a dynamic environment. Sampling based on the other hand can be great when handling an unstructured environment but can lack optimality and completeness. The best path planning method is that which incorporates the two. Also, perhaps, in robotics, we need to redesign our algorithms to make robots "to not be too scared to interact with obstacles". As humans, we interact with obstacles and aren't scared of them. I think robots should be able to do this too.

In a robotic welding project, selecting the appropriate motion type was essential. We chose between joint motion, which moves each joint independently, and linear motion for smooth, straight-line tool movement. Joint motion was faster, suitable for longer distances, but for precision in intricate welding paths, linear motion was superior. It ensured consistent tool orientation and a smoother weld, vital for quality despite increased computation and time. This decision underscored the importance of matching motion type to the specific requirements of the task for optimal results.

OMPL (Open Motion Planning Library) is a very useful library to have for robotic path planning, as it is primarily designed for C++, but there are Python bindings available that allow you to use in Python. It allows you to path plan optimal trajectories given a set of start and end points, as well as obstacles or objects to avoid.

Dans le cas d'un mouvement de type multi-axis (organisation de plusieurs articulations pour réaliser un mouvement désiré de l'outil) . Il faut faire attention au temps réel des couches des logiciels et communication. Les latences pourra impacter largement la précision du mouvement de l'outil. L'exemple idéale est l'imprimante 3D et sa précision, liée à la précision du trajectoire de l'outil de l'injection de PLA, cette précision est impacté par la latence des boucles capteurs-moteurs (la reactivité par rapport au trajectoire commandé).

Tool motion during machining affects planning and execution. Linear motion is easy and fast, perfect for feature switching and tool changes. Circular motion for curves and arcs requires accurate feedrate control. Rotary motion works on cylindrical or spherical surfaces and requires path planning algorithms. Combine these basic motions to create more complicated and efficient pathways. Motion type also affects tool choices, material qualities, and machine capabilities. Path planning software uses different methods and calculations depending on motion type. Optimising machining operations and producing accurate, efficient, and high-quality outcomes requires understanding how motions affect toolpath planning.

For autonomous mobile robots another important factor is to consider the robot should execute in case something goes wrong. Should a collision or error happen, would is the robots next behavior? Should he sit down and wait for input? That might be the right behavior in open spaces, but on stairs or in narrow walkways the robot might become a hazardous obstacle!

In a robotics assembly project, addressing singularities and collision risks was critical. We faced issues with the robot arm's tendency towards singularities, restricting movement in certain directions. To overcome this, we used kinematic analysis and geometric models for planning tool paths, alongside sensors for collision detection. Real-time monitoring of the robot and its environment was essential, complemented by safety measures like emergency stops, speed limits, and warning signals. These steps not only ensured safety but also enhanced the efficiency and reliability of the assembly process, emphasizing the importance of careful planning and safety in robotic operations.

In modern systems there is a ready to use function called automatic path planning in which the software can generate a collision and singularity free path. These functions are becoming common in applications like bin picking and palletizing

Robot path planning relies on singularity and collision avoidance, influencing safety and performance. In singularity, the robot loses one or more degrees of freedom, decreasing its manoeuvrability and perhaps causing damage. Joint limitations, redundant robots, path optimisation, sensor feedback, and real-time adaptation minimise singularity. Planning a path to avoid collisions with barriers or the robot's body called collision avoidance. Environment mapping, sensor integration, planning algorithms, safety margins, and motion control are strategies. For dynamic situations and unexpected impediments, singularity and collision avoidance require a holistic strategy and real-time sensor feedback and adaptability.

Singularities occur when a robot reaches a configuration limiting its movement in certain directions, while collisions involve the robot or tool making unintended contact with objects or other robots. To avoid these, meticulous tool path planning is essential, involving kinematic analysis and geometric modeling to predict and circumvent potential issues. Integrating sensors provides real-time environmental feedback, enhancing operational safety. Continual monitoring of the robot's state and surroundings during operation is crucial, alongside implementing safety protocols like emergency stops, speed limits, and warning systems. This approach helps prevent errors, damage, and injuries, ensuring smoother and safer robotic operations.

Tool path testing and validation are essential for machining accuracy, efficiency, and safety. Simulation with CAM software, dry runs, and test parts are common. Validation evaluates accuracy, efficiency, safety, surface quality, and tool life. Cutting force, acoustic emission, and machine vision tests are also used. Starting with simulation, prioritising safety, dry runs, tool path refinement, performance monitoring, and meticulous records are best practices. Testing and validation methods depend on machining process, tool path complexity, and accuracy and quality. Tests and validation are crucial for successful machining, waste reduction, and error and machine damage prevention.

Robot simulation and offline programming software (OLP, ORP, or ORLP) streamline the iteration process across various mechanical designs, such as tools, fixtures, pedestals, and robot models, as well as work objects. This capability allows engineering teams to minimize time spent on the production line during integration. By testing extreme cases and validating both coarse and detailed implementations of the final product, teams achieve more efficient and effective integration outcomes.

Use offline simulation software to simulate and visualize the robot's movements before deploying the tool paths on the actual robot.Test tool paths in the virtual environment to identify potential problems Consider dynamic simulation to account for the effects of inertia, gravity, and other dynamic factors on the robot's movements.Implement emergency stop procedures and verify their effectiveness during testing.Use metrics to compare different iterations of tool paths and identify areas for improvement

Testar e validar caminhos de ferramentas antes de aplicá-los ao rob? real é crucial. Isso pode ser feito através de simula??o, programa??o offline e execu??o seca. Cada método tem suas vantagens e deve ser escolhido de acordo com os recursos e necessidades.

To test the project is important but it depends on the budget you may have assigned. Even tough I advise to always test in simulation. This will help you have an understanding of the process. If possible start testing from small to big problems your robot in real life. This will allow you to observe possible issues. Also you must always have an emergency stop. If something happens you don't want your robot to be damaged or to damage your environment. Always have a safe stop option.

Understand the difference between accuracy and repeatability, define your tolerances and manage expectations. If your application requires precise tool paths, consider robot calibration to address potential issues.

To plan tool paths in robotics, it's essential to understand the task and environment, choose the right coordinate system, use efficient motion types, prioritize singularity and collision avoidance, and employ effective testing and validation. This includes understanding the robot's objectives, accurately mapping the environment, considering material properties, cutting forces, and robot capabilities. Additionally, use simulations, dry runs, test pieces, and monitor cutting forces, acoustic emissions, and surface quality for process control. Adapting these practices to fit the specific robotic application, environment, and desired results is crucial for successful task completion and maximized outcomes.

One thing that has not been considered above is external aide in path planning, such as sensors and vision systems. Any additional sensors or cameras either mounted on the robot or elsewhere in the work cell not only help achieve precise motion and accuracy in the final path minimizing path error, but they also help with robot safety. This becomes more critical in the world of cobots, where there is no traditional guarding. The ability to move away from preprogrammed paths in certain applications to vision guided navigation makes the robots more flexible and scalable.

I believe that CAD to CAM, and adding many points for curves instead of arc/circular moces, makes it much easier to account for part to part variation, either by teaching manually for a batch, or also via vision guided point offset per part. This prevents crashes.

In most of my projects, robotic joint stiffness has probably been the single most impactful feature that positively or negatively affected the final result. High joint stiffness is crucial because it ensures greater accuracy even at high loads, speeds and significant disturbances. Conversely, low joint stiffness creates off-set type inaccuracies, or lagged responses. Low joint stiffness can be corrected with feedforward models and accurate plant identification.