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About this paper
Chuyuan Tao, Sheng Cheng, Yang Zhao, Fanxin Wang, Naira Hovakimyan
A B-spline Parameterized Optimization-Based Planner (BSPOP) generates continuous-time control inputs for autonomous robots, effectively bridging the frequency gap between high-level planning and low-level control. The method reduces optimization variables and enforces convex control constraints efficiently, resulting in smoother, more computationally efficient, and physically feasible trajectories compared to traditional discrete-time planners.
Key insights
- Parameterizing continuous control signals with B-spline control points drastically reduces the number of decision variables and inequality constraints in the optimization problem, enhancing computational efficiency.
- The inherent convex hull property of B-splines provides an efficient mechanism to guarantee that continuous control inputs satisfy convex physical limits by merely constraining the B-spline control points.
- Precomputing time-dependent B-spline coefficients and terms allows for the efficient online calculation of the control effort cost and dynamic constraints, streamlining the optimization process.
Summary by alphaXiv.
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