Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directions
Safe autonomy is important in many application domains, especially for applications involving interactions with humans. Existing safe control algorithms are similar to one another in the sense that: they all provide control inputs to maintain a low value of an energy function that measures safety. In different methods, the energy function is called a potential function, a safety index, or a barrier function. The connections and relative advantages among these methods remain unclear. This paper introduces a unified framework to derive safe control laws using energy functions. We demonstrate how to integrate existing controllers based on potential field method, safe set algorithm, barrier function method, and sliding mode algorithm into this unified framework. In addition to theoretical comparison, this paper also introduces a benchmark which implements and compares existing methods on a variety of problems with different system dynamics and interaction modes. Based on the comparison results, a new method, called the sublevel safe set algorithm, is derived under the unified framework by optimizing the hyperparameters. The proposed algorithm achieves the best performance in terms of safety and efficiency on the vast majority of benchmark tests.
Code (1)
Similar Papers 제목 키워드 기반
Safety-Guided Flow (SGF): A Unified Framework for Negative Guidance in Safe Generation
Safety mechanisms for diffusion and flow models have recently been developed along two distinct paths. In robot planning, control barrier functions are employed to guide generative trajectories away from obstacles at eve…
Image GenerationSafety Filtering for Reinforcement Learning-based Adaptive Cruise Control
Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy efficiency and traffic flow. However, the a…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
Safe reinforcement learning (RL) with hard constraint guarantees is a promising optimal control direction for multi-energy management systems. It only requires the environment-specific constraint functions itself a prior…
energy managementManagementReinforcement Learning (RL)Safe Reinforcement LearningThe effect of control barrier functions on energy transfers in controlled physical systems
Using a port-Hamiltonian formalism, we show the qualitative and quantitative effect of safety-critical control implemented with control barrier functions (CBFs) on the power balance of controlled physical systems. The pr…
Safety in Augmented Importance Sampling: Performance Bounds for Robust MPPI
This work explores the nature of augmented importance sampling in safety-constrained model predictive control problems. When operating in a constrained environment, sampling based model predictive control and motion plan…
Model Predictive ControlMotion Planning