Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the motion planning problem of a jumping point-robot. Each jump consists in a ballistic motion linking two positions in contact with obstacle surfaces. A solution path is thus a sequence of parabola arcs. The originality of the approach is to consider non-sliding constraints at contact points: slipping avoidance is handled by constraining takeoff and landing velocity vectors to 3D friction cones. Furthermore the magnitude of these velocities is bounded. The ballistic motion lying in a vertical plane,  transform the 3D problem into a 2D one.  then solve the motion equations. The solution gives rise to an exact steering method computing a jump path beten two contact points while respecting all constraints. The method is integrated into a standard probabilistic roadmap planner. Probabilistic completeness is proven. Simulations illustrate the performance of the approach.
['Mylène Campana', 'Jean-Paul Laumond']
I am writing a research paper on a idea Ballistic motion planning
[{"from": "human", "value": "I am writing a research paper on a idea Ballistic motion planning"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  addresses the motion planning problem of a jumping point-robot. Each jump consists in a ballistic motion linking two positions in contact with obstacle surfaces. A solution path is thus a sequence of parabola arcs. The originality of the approach is to consider non-sliding constraints at contact points: slipping avoidance is handled by constraining takeoff and landing velocity vectors to 3D friction cones. Furthermore the magnitude of these velocities is bounded. The ballistic motion lying in a vertical plane,  transform the 3D problem into a 2D one.  then solve the motion equations. The solution gives rise to an exact steering method computing a jump path beten two contact points while respecting all constraints. The method is integrated into a standard probabilistic roadmap planner. Probabilistic completeness is proven. Simulations illustrate the performance of the approach."}]