Control Barrier Functions: Theory and Applications
About
This paper provides an introduction and overview of recent work on control barrier functions and their use to verify and enforce safety properties in the context of (optimization based) safety-critical controllers. We survey the main technical results and discuss applications to several domains including robotic systems.
Aaron D. Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, Paulo Tabuada• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Obstacle Avoidance | LASA | Line Error0.00e+0 | 11 | |
| Push-T | Push T | Success Rate78 | 8 | |
| Robot navigation | Hospital Scenario 2 | Safety Compliance Count10 | 7 | |
| Robot navigation | Hospital Scenario 1 | Safety Incidents10 | 7 | |
| Robot navigation | Hospital Scenario 3 | Safety Incidents10 | 7 | |
| Robot navigation | Social Navigation Scenario 4 | Safety Count2 | 7 | |
| Multi-agent human-robot navigation | Crowd interaction trajectory data Multi-agent scenario real-world pedestrian (test) | Collision Rate38.8 | 4 | |
| Single-agent human-robot navigation | Head-on simulation instances Single-agent scenario Corridor situation (test) | Collision Rate16 | 3 | |
| Safety-critical control | Stewart platform Region 1 (hardware) | Average Computation Time (s)0.0055 | 2 | |
| Safety-critical control | Stewart platform Region 2 hardware | Avg Computation Time (s)0.0059 | 2 |
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