Anytime Motion Planning

• Also called: anytime planning, anytime planner
• Category: Planning & manipulation › Motion planning
• Tags: motion-planning, sampling-based-planning, optimal-planning, real-time, bounded-computation

Motion planning that returns a valid path quickly and then keeps improving it while time allows, so a usable answer is available whenever the planner is stopped.

Definition

An anytime motion planner produces a first feasible, collision-free solution as early as it can and then spends any remaining time improving it, typically by lowering path length, execution time or another cost. At every moment after the first solution it holds a best-so-far answer that can be returned if it is interrupted. The idea comes from the wider notion of anytime algorithms in artificial intelligence, introduced by Dean and Boddy in the late 1980s, where solution quality is traded against computation time and the relationship between the two is described by a performance profile.

In graph search, anytime behavior is usually obtained by running a weighted, inflated heuristic first and then tightening the weight while reusing earlier search effort, as in Anytime Repairing A (ARA). In sampling-based planning, asymptotically optimal planners such as RRT and the PRM variant of probabilistic roadmaps are naturally anytime: they keep sampling and rewiring after the first solution, and the cost of the best path tends to the optimum as the number of samples grows. Informed RRT and Batch Informed Trees (BIT) speed up the improvement phase by restricting samples to the region that could still contain a better path. A plain rapidly-exploring random tree** is not anytime in this sense, because it stops once the goal is connected, although restarts or post-processing can be wrapped around it.

Improvement over time is rarely smooth. Cost often falls in steps, especially when the planner finds a route through a different gap between obstacles, that is, a path in a new homotopy class, and long stretches can bring almost no gain. That makes the stopping decision a problem of its own: stopping too early wastes quality, stopping too late wastes time the robot could spend moving. Fixed time budgets are the usual practice, while meta-reasoning methods learn or estimate when further computation is no longer worth it.

Why it matters

Robots rarely have a fixed, known amount of time to plan. An anytime planner gives a safe fallback almost immediately and turns any spare time into better motion, which suits systems that must act on a deadline but still care about path quality, energy or cycle time.

Key applications

• Arm motion planning under per-query time budgets in manipulation pipelines
• Replanning for mobile robots and vehicles that must act on a fixed control cycle
• Path planning for aerial robots where shorter paths save battery
• Heuristic graph search over grids and lattices with ARA*-style weight reduction
• Meta-reasoning research on deciding when to stop planning and start acting

In practice

A mobile manipulator plans arm motions with an asymptotically optimal sampling-based planner from a library such as OMPL. Each query gets a time budget: the planner usually finds a first path early in that budget and then continues to shorten it. The team logs best cost against elapsed time over many recorded queries and sees that most of the gain arrives early, with occasional large drops when a shorter route around an obstacle is found late. Instead of always spending the full budget, they stop when the cost has not improved for a set interval, or use a learned stopping rule, then run shortcutting and time parameterization on the result before sending it to the controller.

Common misconceptions

• Anytime means the first solution is near-optimal — the first solution is often far from optimal, and quality depends on how long the planner runs
• Cost improves steadily with time — it typically drops in uneven steps, with long plateaus between them
• Asymptotic optimality guarantees a good path within the time budget — it only describes the limit as computation grows without bound and says nothing about a finite deadline
• Using the whole time budget is always best — time spent planning is time not spent moving, so stopping early is often the better trade

Who should know this

• Motion planning engineers configuring sampling-based or search-based planners
• Mobile manipulation and navigation developers working under real-time limits
• Robotics researchers studying planning under bounded computation
• Students learning optimal motion planning

Related technologies

• ARA (Anytime Repairing A)
• RRT*
• Informed RRT*
• BIT* (Batch Informed Trees)
• OMPL

Related terms

• RRT*
• Rapidly-exploring random tree
• Probabilistic Roadmap
• Homotopy Class
• Warm Start
• Motion Planning

Related projects

• GENESIS: World's fastest physics engine: 43M FPS for embodied AI learning
• Cartographer: Google's real-time 2D and 3D SLAM system for multi-platform robotics
• FAST-LIVO2: Fast, direct LiDAR-inertial-visual odometry from HKU-MARS, published in IEEE T-RO
• Dora AI: Dataflow middleware for AI robotics applications, built in Rust
• OpenMind OM1: AI-native operating system for robots with modular perception, cognition, and action
• Fast DDS: eProsima's high-performance DDS middleware — the default transport under ROS 2