A Closed-Form Dual-Barrier CBF Safety Filter for Holonomic Robots on Incrementally Built Occupancy Grid Maps
2026-05-06 • Robotics
Robotics
AI summaryⓘ
The authors developed a safety system that helps robots move safely by controlling their speed based on what the robot has already mapped. It uses two rules to keep the robot away from known obstacles and from areas it hasn't explored yet, which might be risky. This method is efficient enough to run on simple computers without slowing down other important robot tasks. The system can adjust its caution based on how well the area is mapped, allowing the robot to explore better while staying safe. They tested this on a quadrotor drone indoors and it avoided all collisions during the experiments.
control barrier functionoccupancy grid mapsigned distance fieldholonomic robotvelocity controlSLAMexploration frontieradaptive gainPX4 quadrotor
Authors
Himanshu Paudel, Basanta Joshi, Dhirendra Raj Madai, Alina Bartaula, Biman Rimal, Sanjay Neupane
Abstract
We present a dual-barrier control barrier function (CBF) safety filter for real-time, safety-critical velocity control of holonomic robots operating in incrementally built occupancy grid maps. As a robot explores an unknown environment, unmapped regions introduce irreducible uncertainty, since obstacle geometry beyond the explored frontier is unknown, making entry into such regions a source of collision risk, especially with front-facing sensors. To address this, we enforce two constraints: avoidance of mapped obstacles and restriction from unexplored regions. Both constraints are derived analytically from the occupancy grid's signed distance field, yielding a closed-form safety filter that requires only a small linear system solve per cycle. On resource-constrained platforms such as the Raspberry Pi, where SLAM and planning already consume significant compute, the low overhead of the proposed filter preserves resources. An adaptive gain schedule relaxes the frontier constraint in information-rich regions and tightens it in well-mapped areas, improving exploration efficiency while maintaining safety. The filter operates in velocity space as a minimally invasive correction and composes with arbitrary nominal controllers, including learning-based methods. Hardware flight experiments on a PX4-controlled quadrotor demonstrate zero collisions across multiple indoor runs.