Motionforward

Motionforward, often written in lowercase as motionforward, is a term used in discussions of motion planning and control to denote methods that seek steady, progressive advancement toward a target state while respecting dynamic and safety constraints. The idea is to structure planning problems so that each decision increases the agent's forward progress, either in continuous time or discrete steps.

In practice, motionforward can be implemented as a class of objective functions or algorithmic guidance that combines an explicit forward-progress term with standard cost terms for distance, energy, and collision avoidance. It is compatible with various planning paradigms, including sampling-based planners and trajectory optimization, and can be applied to autonomous vehicles, robotic manipulators, and animated characters.

The approach is studied for its promise of robust performance in dynamic environments, but it raises questions about balance between forward progress and risk of constraint violations, potential biases toward fast trajectories, and computational overhead. Researchers emphasize tuning of forward-progress weights and validation in realistic simulations.

Motionforward is not a widely standardized framework and is used descriptively rather than as a formal, single algorithm. It serves as a lens for evaluating motion planning methods that prioritize progress along a path, rather than only endpoint optimality.

Related topics include motion planning, trajectory optimization, model predictive control, and velocity obstacles.