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My robot has less then 3DOF. It is roughly humanoid and I am working with the left arm at the moment, which has certain joint limits which dictate a certain reachable space.

I missed this comment at first. Yes, it's going to be a problem, especially if you have to rely on cartesian planning. Increasing the cartesian tolerance will get you started, but likely will give imprecise results.

If you have to support "random" goals (goals you can't pre-plan for), to get fast, high-quality paths you may have to create a custom OMPL planner that understands the physical limitations of your robot.

My robot has less then 3DOF. It is roughly humanoid and I am working with the left arm at the moment, which has certain joint limits which dictate a certain reachable space.

I missed this comment at first. Yes, it's going to be a problem, especially if you have to rely on cartesian planning. Increasing the cartesian tolerance will get you started, but likely will give imprecise results.

If you have to support "random" goals (goals you can't pre-plan for), to get fast, rapidly-calculate high-quality paths you may have to create a custom OMPL planner that understands the physical limitations of your robot.

My robot has less then 3DOF. It is roughly humanoid and I am working with the left arm at the moment, which has certain joint limits which dictate a certain reachable space.

I missed this comment at first. Yes, it's going to be a problem, especially if you have to rely on cartesian planning. Increasing the cartesian tolerance will get you started, but likely will give imprecise results.results. If you are seeing IK failures during planning, try increasing the values of GoalPositionTolerance and GoalOrientationTolerance.

If you have to support "random" goals (goals you can't pre-plan for), to rapidly-calculate high-quality paths you may have to create a custom OMPL planner that understands the physical limitations of your robot.