conmoves

Conmoves are elementary actions used in constrained motion planning and control to move a system from one state to another while satisfying defined constraints. Each conmove specifies a start state, an end state, a feasible control profile over a time horizon, a dynamical model, and a constraint set that may include safety requirements, actuation limits, collision avoidance, energy budgets, and environmental restrictions. The collection of conmoves serves as a catalog or graph that planners use to assemble complete trajectories.

Origin and terminology: The term conmove is a portmanteau of constrained move, and it has been used in robotics and animation literature since the 2010s to distinguish constrained state transitions from unconstrained moves in classical planning.

Formal concept: A conmove is feasible if there exists a control input that drives the system from the start state to the end state within the specified horizon while respecting all constraints. Each conmove has an associated cost, which may reflect time, energy, smoothness (e.g., jerk), or risk. Conmoves can be composed: if the end state of one conmove matches the start state of another and the intermediate constraints are compatible, a sequence of conmoves yields a longer trajectory with a total cost equal to the sum of constituent costs.

Applications: In robotics, conmoves underpin path planning for manipulators and mobile robots. In autonomous vehicles, they describe safe waypoint transitions with altitude or speed constraints. In computer animation, conmoves help retarget motions while maintaining balance and contact constraints. In industrial automation, they model discrete steps in assembly sequences with tolerance requirements.

Challenges: Non-convex constraint satisfaction, high-dimensional state spaces, accurate dynamics modeling, and real-time computation remain difficult. Variants include soft-conmoves, which allow constraint violations with penalties, and robust conmoves designed for uncertainty. See also trajectory optimization, motion planning, and constrained optimization.