Batch and Incremental Kinodynamic Motion Planning using Dynamic Factor Graphs
This provides a more efficient solution for robotics applications requiring real-time motion planning with dynamic constraints.
The paper tackles kinodynamic motion planning by using factor graphs and numerical optimization to generate energy-efficient trajectories that satisfy collision avoidance and dynamic constraints, achieving an order of magnitude faster replanning through incremental techniques.
This paper presents a kinodynamic motion planner that is able to produce energy efficient motions by taking the full robot dynamics into account, and making use of gravity, inertia, and momentum to reduce the effort. Given a specific goal state for the robot, we use factor graphs and numerical optimization to solve for an optimal trajectory, which meets not only the requirements of collision avoidance, but also all kinematic and dynamic constraints, such as velocity, acceleration and torque limits. By exploiting the sparsity in factor graphs, we can solve a kinodynamic motion planning problem efficiently, on par with existing optimal control methods, and use incremental elimination techniques to achieve an order of magnitude faster replanning.