Keeping a robot inside a safe set (no collisions, no stalls over water, no battery below the return threshold) while it does its job. The usual tool is a filter between the planner and the motors that changes a command only when it would leave the safe set.
Barrier functions
- CBF-QP (Ames and others) solves a small quadratic program at every step: stay as close as possible to the commanded input, subject to a barrier constraint that keeps the safe set invariant. When safety and progress conflict, progress gives way.
- Safety barrier certificates turn this into a multi-robot collision filter, with each robot taking its share of the avoidance. The authors admit deadlock and infeasible programs in dense crowds.
- Qin and others learn decentralised barrier certificates with a neural network, and Bousias and others use an equivariant graph network for quadrotor swarms. Like most learned barrier work, both assume every robot runs the same policy.
Switching and shielding
- Simplex runs a high-performance controller and switches to a verified safe one when needed. SOTER also switches back.
- Shield synthesis builds a component that corrects unsafe outputs of a system against a temporal-logic specification, and shielded RL puts one between a learning agent and the world.
Assumptions
The guarantee is about the state the controller believes. A wrong estimate (spoofed GPS, a drifting IMU) means the filter keeps the estimate safe, which may not be where the robot is. In a team, the distributed filters assume every neighbour runs the same filter and does its share: a neighbour that ignores its share, through a bug or on purpose, pushes the honest robots to do more than theirs. And a safety filter that only ever slows down can trap a robot in a state that is safe and useless, and getting it out is a failure recovery problem.
Questions
- How should a barrier function treat a neighbour whose reported position it doesn’t trust?
- Can a safety filter prove progress as well as safety, at least for a while?
Related: runtime monitoring, aerial swarms, robust robotics, resilient consensus.