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Showing 1 to 9 of 9 for “"control barrier functions"”.
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Safe Navigation of Multi-Agent Quadrupedal Robots: A Hierarchical Control Framework Based on Distributed Predictive Control and Control Barrier Functions
… development of sophisticated distributed layered control algorithms focused on the navigation, planning, and control of multi-agent quadrupedal robots collaborating in uncertain environments. Quadrupedal robots are high-dimensional, complex systems that are inherently unstable, posing significant …
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Real-Time Self-Collision Avoidance for Dynamic Legged Robots
… as a constraint can conflict with other control objectives, such as stability or foot placement. Ensuring that these conflicts are resolved in real-time is critical for hardware deployment. This work presents a reactive collision avoidance framework that combines Control Barrier Functions …
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Safe Nonlinear Control Under Control Constraints via Reachability, Optimal Control and Reinforcement Learning
… complicates the task of designing stabilizing controllers that can guarantee safety, which we denote as the stabilize-avoid problem. Existing control-based techniques can provide safety and stability guarantees but under the assumption of unbounded control inputs. On the other hand, …
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SAFE REINFORCEMENT LEARNING-BASED GREEN LIGHT OPTIMAL SPEED ADVISORY FOR MIXED-TRAFFIC PLATOONS
… during implementation. Third, we integrate Control Barrier Functions (CBFs) into the RL-based policies to ensure car-following and red-light safety. Fourth, we address signal timing undertainty by leveraging CP to estimate a confidence interval of the signal timing prediction results. We …
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Training Safety Control Filters Using High-dimensional and Un-labeled Data
<p>Synthesizing control policies that preserve the safety of autonomous systems is a challenge that remains to be solved. Towards that goal, control barrier functions (CBFs) have been developed as mathematical constructs that can be used in real-time to correct safety-violating nominal actions to …
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Safe and adaptive reinforcement learning for robotics applications
In recent years, learning-based control methods, especially those leveraging the power of reinforcement learning (RL) and deep learning, have demonstrated impressive performance in complex robotics control tasks. However, they often suffer from the lack of safety and robustness guarantees, which …
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Control of Agentic Systems Using the Newton-Raphson Controller
The Newton-Raphson Controller is a tracking controller for dynamical systems. Existing results have shown the effectiveness of the controller for a number of nonlinear systems, for example, inverted pendulums, autonomous vehicles and quadrotors. Although this controller has reached a performance …
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Tailoring Complexity of Model-Based Controllers for Legged Robots
… but must manage complex and often conflicting control objectives. While model-based controllers can address these challenges using online optimization, they have high computational demands. Model predictive control (MPC) provides closed-loop stability with online trajectory optimization, but …
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Reinforcement Learning Control for Mobile Robot Parking with Safety Constraints
… that combines reinforcement learning (RL) with control barrier function (CBF)-based methods to achieve safe autonomous vehicle control, focusing on parking with obstacle avoidance. We apply Deep Deterministic Policy Gradient (DDPG) methods for continuous control and evaluate policies across …