Abstract

In this chapter, safety methods in human-robot (HR) interaction/collaboration are presented. Ensuring the safety of humans, objects, or even the robot itself in the robot’s operating environment is one of the crucial aspects of collaborative robotics. Since there are limited ways of controlling the behavior of humans, e.g., by placing physical barriers, shaping the behavior of the robot is a feasible option. The chapter discusses current methods of placing barriers for human safety in an industrial setting and novel methods of placing virtual barriers by designing robot controllers using barrier transformation. The concepts of barrier functions (BFs), control barrier functions (CBFs), and barrier transformations are reviewed. The barrier transformation concept is used to design an adaptive trajectory tracking controller for the robot such that the robot does not cross the virtual barriers. The designed controller is tested in simulations. Future directions of safety technology in human-robot collaboration are presented.

Highlights

  • In many robotics and other engineering applications, maintaining system states within a prescribed bound is essential to satisfy the system safety property

  • In addition to the control design and its testing in simulation, the chapter presents a review of standard techniques of designing safe robot controllers using barrier functions (BFs) and control barrier functions (CBFs), followed by a review of Barrier transformations which is used to design adaptive robot controller of Euler– Lagrange (EL) robot system in this chapter

  • This chapter provides a perspective on problems wherein humans and robots work collaboratively with one another. Research in this field aims to relax the current workplace constraints, such as fences, virtual curtains often seen in manufacturing settings between humans and robots or velocity limits on collaborative robots

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Summary

Introduction

In many robotics and other engineering applications, maintaining system states within a prescribed bound is essential to satisfy the system safety property. Using a similar BLF, in [59], an adaptive neural network with full-state feedback control that uses a Moore-Penrose pseudoinverse term in the control law design is developed for an uncertain robot dynamics with output constraints, and the signals of the closed-loop systems are proven to be semi-global uniformly bounded (SGUUB). Barrier function transformation, presented in [67], is used to design a safe adaptive trajectory tracking controller for the robot using Euler– Lagrange (EL) system. In addition to the control design and its testing in simulation, the chapter presents a review of standard techniques of designing safe robot controllers using BFs and CBFs, followed by a review of Barrier transformations which is used to design adaptive robot controller of EL robot system in this chapter. Future directions of robot control design for safe human-robot collaboration are provided at the end

Barrier functions
Constructing the barrier functions
Control barrier functions
Constructing the control barrier functions
Review of barrier transformation
Adaptive control of a robot system with full-state constraints
Euler-Lagrange dynamics for robot arm Consider the Euler–Lagrange (EL) dynamics
State space system model and control design
Safe adaptive tracking control development
Lyapunov stability analysis
Simulations
Safe tracking control of an uncertain EL-dynamics with full-state constraints using BF
Conclusions and future directions
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