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Safe Control for Soft-Rigid Robots with Self-Contact using Control Barrier Functions


Core Concepts
Incorporating Control Barrier Functions for safe self-contact in soft-rigid hybrid robots is an effective strategy.
Abstract
The content explores the challenges and solutions for controlling soft-rigid hybrid robots, focusing on self-contact issues. It introduces Control Barrier Functions (CBFs) and High Order CBFs to manage self-contact scenarios. The methodology is evaluated in simulation environments and physical hardware systems, demonstrating effective regulation of self-contact in soft-rigid hybrid robotic systems. The content is structured into sections covering Introduction, Preliminaries and System Formulation, Control Approach, Simulation Results, Hardware Results, Discussion and Conclusion, and References. Introduction Soft-rigid hybrid robots offer compliance and strength. Challenges in controlling self-contact in soft-rigid robots. Preliminaries and System Formulation Introduction to High Order CBFs for control systems. Kinematics and dynamics of soft-rigid robots. Control Approach Nominal control input using a PD+ controller. Implementation of CBFs for safe self-contact. Simulation Results Implementation of safety constraints in simulation. Parameters, setup, and results of the simulation. Hardware Results Validation of the approach on hardware. Parameters, setup, and results of the hardware experiment. Discussion and Conclusion Effectiveness of CBFs in regulating self-contact. Potential issues and future directions.
Stats
"Parameters are set to L0 = 0.1m, d = 0.04m, r = 0.05m, bending stiffness κθ = 10 Nm rad, axial stiffness κL = 10 N m, bending damping βθ = 5 Nms rad, axial damping βL = 5 Ns m, and module mass mj = 0.15kg." "PD gains are set to KP = 5 and KD = 1." "For our barrier functions, ϵj = 0.005m for all safety constraints."
Quotes
"CBFs provide a natural mechanism to design controllers that can gracefully regulate behavior near contact points." "The CBF prevents the safety constraints from dropping below zero, counteracting the safety constraint."

Deeper Inquiries

Can CBFs be used to regulate robot-environment interactions effectively?

Control Barrier Functions (CBFs) can indeed be utilized to regulate robot-environment interactions effectively. By defining safety constraints as functions of the robot's state and its derivatives, CBFs can enforce these constraints to ensure that the robot operates within safe boundaries. These constraints can include maintaining a safe distance from fragile objects or ensuring that the robot does not collide with its environment. By incorporating CBFs into the control strategy, the robot can navigate its surroundings while adhering to specified safety constraints, thus enhancing safety during interactions with the environment.

How can the conflict between stability properties of the nominal controller and CBFs be addressed?

The conflict between the stability properties of the nominal controller and CBFs can be addressed by incorporating a Control Lyapunov Function (CLF) into the control framework. By combining CBFs with a CLF, it is possible to achieve both safety guarantees provided by the CBFs and stability properties ensured by the nominal controller. The CLF can help maintain stability while the CBFs enforce safety constraints, ensuring that the system operates within safe limits without compromising its stability. Additionally, a passivity constraint, as proposed in certain studies, can also help reconcile the competing constraints of stability and safety, providing a feasible solution to the conflict.

What are the potential applications of CBFs beyond self-contact regulation in soft robots?

Beyond self-contact regulation in soft robots, Control Barrier Functions (CBFs) have a wide range of potential applications in robotics and control systems. Some of these applications include: Obstacle Avoidance: CBFs can be used to ensure that robots navigate around obstacles in their environment, maintaining a safe distance to prevent collisions. Trajectory Tracking: CBFs can help robots track desired trajectories while avoiding deviations that could lead to unsafe conditions. Collision Avoidance: CBFs can be employed to prevent collisions between multiple robots operating in the same workspace, enhancing overall safety. Task Execution in Human-Robot Collaboration: CBFs can ensure that robots perform tasks safely in collaboration with humans, preventing accidental contact or collisions. Autonomous Vehicles: CBFs can contribute to the safe operation of autonomous vehicles by enforcing constraints related to speed, distance from other vehicles, and adherence to traffic rules. Aerospace Applications: CBFs can be used in spacecraft control to maintain safe distances from other objects in space or to regulate maneuvers during docking procedures. These applications demonstrate the versatility and effectiveness of CBFs in ensuring safe and reliable operation of various robotic systems beyond self-contact regulation in soft robots.
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