Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Planning motion for humanoid robots requires obeying simultaneous constraints on balance, collision-avoidance, and end-effector pose, among others. Several algorithms are able to generate configurations that satisfy these constraints given a good initial guess, i.e. a configuration which is already close to satisfying the constraints. Hover, when selecting goals for a planner a close initial guess is rarely available. Methods that attempt to satisfy all constraints through direct projection from a distant initial guess often fail due to opposing gradients for the various constraints, joint-limits, or singularities.  approach the problem of generating a constrained goal by searching for a configuration in the intersection of all constraint manifolds in configuration space (C-space). Starting with an initial guess, our algorithm, Constellation, builds a graph in C-space whose nodes are configurations that satisfy one or more constraints and whose cycles determine where the algorithm explores next.  compare the performance of our approach to direct projection and a previously-proposed cyclic projection method on reaching tasks for a humanoid robot with 33 DOF.  find that Constellation performs the best in terms of the number of solved queries across a wide range of problem difficulty. Hover, this success comes at higher computational cost.
['Peter Kaiser', 'Dmitry Berenson', 'Nikolaus Vahrenkamp', 'Tamim Asfour', 'Rüdiger Dillmann', 'Siddhartha S. Srinivasa']
I am writing a research paper on a idea Constellation - An algorithm for finding robot configurations that satisfy multiple constraints
[{"from": "human", "value": "I am writing a research paper on a idea Constellation - An algorithm for finding robot configurations that satisfy multiple constraints"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Planning motion for humanoid robots requires obeying simultaneous constraints on balance, collision-avoidance, and end-effector pose, among others. Several algorithms are able to generate configurations that satisfy these constraints given a good initial guess, i.e. a configuration which is already close to satisfying the constraints. Hover, when selecting goals for a planner a close initial guess is rarely available. Methods that attempt to satisfy all constraints through direct projection from a distant initial guess often fail due to opposing gradients for the various constraints, joint-limits, or singularities.  approach the problem of generating a constrained goal by searching for a configuration in the intersection of all constraint manifolds in configuration space (C-space). Starting with an initial guess, our algorithm, Constellation, builds a graph in C-space whose nodes are configurations that satisfy one or more constraints and whose cycles determine where the algorithm explores next.  compare the performance of our approach to direct projection and a previously-proposed cyclic projection method on reaching tasks for a humanoid robot with 33 DOF.  find that Constellation performs the best in terms of the number of solved queries across a wide range of problem difficulty. Hover, this success comes at higher computational cost."}]