BibTex format

author = {AlAttar, A and Chappell, D and Kormushev, P},
doi = {10.3389/frobt.2022.809114},
journal = {Frontiers in Robotics and AI},
pages = {1--9},
title = {Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance},
url = {},
volume = {9},
year = {2022}

RIS format (EndNote, RefMan)

AB - Model predictive control is a widely used optimal control method for robot path planning andobstacle avoidance. This control method, however, requires a system model to optimize controlover a finite time horizon and possible trajectories. Certain types of robots, such as softrobots, continuum robots, and transforming robots, can be challenging to model, especiallyin unstructured or unknown environments. Kinematic-model-free control can overcome thesechallenges by learning local linear models online. This paper presents a novel perception-basedrobot motion controller, the kinematic-model-free predictive controller, that is capable of controllingrobot manipulators without any prior knowledge of the robot’s kinematic structure and dynamicparameters and is able to perform end-effector obstacle avoidance. Simulations and physicalexperiments were conducted to demonstrate the ability and adaptability of the controller toperform simultaneous target reaching and obstacle avoidance.
AU - AlAttar,A
AU - Chappell,D
AU - Kormushev,P
DO - 10.3389/frobt.2022.809114
EP - 9
PY - 2022///
SN - 2296-9144
SP - 1
TI - Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance
T2 - Frontiers in Robotics and AI
UR -
UR -
UR -
VL - 9
ER -