Dynamic Target Tracking &amp; Collision Avoidance Behaviour of Person Following Robot Using Model Predictive Control

Ashe, Avijit Kumar; Krishna, K. Madhava

doi:10.1109/icstcc50638.2020.9259720

Cited by 3 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oleinikov et al (2021) used MPC for controlling a robot manipulator that avoids fixed and human obstacles. Ashe and Krishna (2020) used MPC for dynamic target tracking and obstacle avoidance for a personfollowing robot. Pankert and Hutter (2020) used MPC for continuous mobile manipulation and collision avoidance.…”

Section: Related Workmentioning

confidence: 99%

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Chappell

Kormushev

2022

Front. Robot. AI

View full text Add to dashboard Cite

Model predictive control is a widely used optimal control method for robot path planning and obstacle avoidance. This control method, however, requires a system model to optimize control over a finite time horizon and possible trajectories. Certain types of robots, such as soft robots, continuum robots, and transforming robots, can be challenging to model, especially in unstructured or unknown environments. Kinematic-model-free control can overcome these challenges by learning local linear models online. This paper presents a novel perception-based robot motion controller, the kinematic-model-free predictive controller, that is capable of controlling robot manipulators without any prior knowledge of the robot’s kinematic structure and dynamic parameters and is able to perform end-effector obstacle avoidance. Simulations and physical experiments were conducted to demonstrate the ability and adaptability of the controller to perform simultaneous target reaching and obstacle avoidance.

show abstract

Section: Related Workmentioning

confidence: 99%

Kinematic-Model-Free Predictive Control for Robotic Manipulator Target Reaching With Obstacle Avoidance

Chappell

Kormushev

2022

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…To obtain the kinematic behavior data of the differential robot, it is assumed that the robot moves on a flat surface without friction, that it moves only by the rotational motion of the wheels, that it is considered as a solid, rigid mechanism without flexible parts, but that the holonomic constraints of the system must be considered [13][14], that is, it cannot move to the sides, as shown in figure 2. The measurements of interest in the modeling process are the distance between the wheels, called L and the radius of the wheels, called R, as shown in figure 3.…”

Section: ) Direct Kinematics Of the Mobile Robotmentioning

confidence: 99%

Development of an Adaptive Trajectory Tracking Control of Wheeled Mobile Robot

2021

View full text Add to dashboard Cite

Classical modeling and control methods applied to differential locomotion mobile robots generate mathematical equations that approximate the dynamics of the system and work relatively well when the system is linear in a specific range. However, they may have low accuracy when there are many variations of the dynamics over time or disturbances occur. To solve this problem, we used a recursive least squares (RLS) method that uses a discrete-time structure first-order autoregressive model with exogenous variable (ARX). We design and modify PID adaptive self-adjusting controllers in phase margin and pole allocation. The main contribution of this methodology is that it allows the permanent and online update of the robot model and the parameters of the adaptive self-adjusting PID controllers. In addition, a Lyapunov stability analysis technique was implemented for path and trajectory tracking control, this makes the errors generated in the positioning and orientation of the robot when performing a given task tend asymptotically to zero. The performance of the PID adaptive self-adjusting controllers is measured through the implementation of the criteria of the integral of the error, which allows to determine the controller of best performance, being in this case, the PID adaptive self-adjusting type in pole assignment, allowing the mobile robot greater precision in tracking the trajectories and paths assigned, as well as less mechanical and energy wear, due to its smooth and precise movements.

show abstract