Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertainties

Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game

Distributed Control of a Limited Angular Field-of-View Multi-Robot System in Communication-Denied Scenarios: A Probabilistic Approach

Multi-robot systems are gaining popularity over single-agent systems for their advantages. Although they have been studied in agriculture, search and rescue, surveillance, and environmental exploration, real-world implementation is limited due to agent coordination complexities caused by communication and sensor limitations. In this work, we propose a probabilistic approach to allow coordination among robots in communication-denied scenarios, where agents can only rely on visual information from a camera with a limited angular field-of-view. Our solution utilizes a particle filter to analyze uncertainty in the location of neighbors, together with Control Barrier Functions to address the exploration-exploitation dilemma that arises when robots must balance the mission goal with seeking information on undetected neighbors. This technique was tested with virtual robots required to complete a coverage mission, analyzing how the number of deployed robots affects performances and making a comparison with the ideal case of isotropic sensors and communication. Despite an increase in the amount of time required to fulfill the task, results have shown to be comparable to the ideal scenario in terms of final configuration achieved by the system

Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertainties

Robots operating in real world settings must navigate and maintain safety while interacting with many heterogeneous agents and obstacles. Multi-Agent Control Barrier Functions (CBF) have emerged as a computationally efficient tool to guarantee safety in multi-agent environments, but they assume perfect knowledge of both the robot dynamics and other agents' dynamics. While knowledge of the robot's dynamics might be reasonably well known, the heterogeneity of agents in real-world environments means there will always be considerable uncertainty in our prediction of other agents' dynamics. This work aims to learn high-confidence bounds for these dynamic uncertainties using Matrix-Variate Gaussian Process models, and incorporates them into a robust multi-agent CBF framework. We transform the resulting min-max robust CBF into a quadratic program, which can be efficiently solved in real time. We verify via simulation results that the nominal multi-agent CBF is often violated during agent interactions, whereas our robust formulation maintains safety with a much higher probability and adapts to learned uncertaintie

Belief Control Barrier Functions for Risk-aware Control

Ensuring safety in real-world robotic systems is often challenging due to unmodeled disturbances and noisy sensor measurements. To account for such stochastic uncertainties, many robotic systems leverage probabilistic state estimators such as Kalman filters to obtain a robot's belief, i.e. a probability distribution over possible states. We propose belief control barrier functions (BCBFs) to enable risk-aware control synthesis, leveraging all information provided by state estimators. This allows robots to stay in predefined safety regions with desired confidence under these stochastic uncertainties. BCBFs are general and can be applied to a variety of robotic systems that use extended Kalman filters as state estimator. We demonstrate BCBFs on a quadrotor that is exposed to external disturbances and varying sensing conditions. Our results show improved safety compared to traditional state-based approaches while allowing control frequencies of up to 1kHz

Safety Under Uncertainty: Tight Bounds with Risk-Aware Control Barrier Functions

We propose a novel class of risk-aware control barrier functions (RA-CBFs) for the control of stochastic safety-critical systems. Leveraging a result from the stochastic level-crossing literature, we deviate from the martingale theory that is currently used in stochastic CBF techniques and prove that a RA-CBF based control synthesis confers a tighter upper bound on the probability of the system becoming unsafe within a finite time interval than existing approaches. We highlight the advantages of our proposed approach over the state-of-the-art via a comparative study on an mobile-robot example, and further demonstrate its viability on an autonomous vehicle highway merging problem in dense traffic.Comment: 7 pages, 4 figures, 5 tables, accepted at ICRA 202