Vision/Position Hybrid Control for a Hexa Robot Using Bacterial Foraging Optimization in Real-time Pose Adjustment

Abstract

This paper presents a novel architecture of the vision/position hybrid control for a Hexa parallel robot. The 3D vision system is combined with the Proportional-Integral-Derivative (PID) position controller to form a two-level closed-loop controller of the robot. The 3D vision system measures the pose of the end-effector after the PID control. The measurement of the 3D vision system is used as a feedback of the second closed-loop control. The 3D vision system has a simple structure using two fixed symmetric cameras at the top of the robot and four planar colored markers on the surface of the end-effector. The 3D vision system detects and reconstructs the 3D coordinates of colored markers. Based on the distance and coplanarity constraints of the colored markers, the optimization problem is modeled for the real-time adjustment, which is implemented during the operation of the robot to minimize the measurement error of the 3D vision system due to both the initial calibration of the stereo camera and the external noise affecting image processing. The bacterial foraging optimization is appropriately configured to solve the optimization problem. The experiment is performed on a specific Hexa parallel robot to assess the effectiveness and feasibility of the proposed real-time adjustment using the bacterial foraging optimization. The experimental result shows that it has high accuracy and fast computation time although the experiment is conducted on a laptop with an average hardware configuration. An experimental comparison of the performance between the proposed method and another control method is also implemented. The results show the superiority and application potential of the proposed method.

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

“…The functions 𝐽 (𝑖) and 𝐹 (𝑖) are defined to evaluate cost and fitness of 𝑖 𝑡ℎ bacterium respectively. Details of application flow diagram can be seen in Huynh et al (2020).…”

“…Monitoring a moving target along an unpredictable route by a mobile robot is another application of BFO for tuning PID controllers (Benbouabdallah and Zhu, 2013;Huynh et al, 2020), speed controller of a permanent magnet synchronous motor (Agarwal et al, 2017), position control of Direct Current (DC) servo motor (Jasim, 2018) and tuning PID parameters for Multi-variable Input and Multi-variable Output (MIMO) systems (Sivakumar et al, 2016). Kennedy and Eberhart (1995) proposed the PSO algorithm, which is a metaheuristic algorithm based on the principle of swarm intelligence and capable of solving complicated mathematics problems encountered in engineering (de Almeida and Leite, 2019).…”

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

“…The tumbling pattern allows the bacteria to move in a random direction. Bacteria use these two movement patterns in their foraging to increase the foraging area and to collaboratively find their food; many studies [33,34] have successfully used BFOA to solve complex problems. Further details about BFOA can be found in [27].…”

Vision-Based Hybrid Controller to Release a 4-DOF Parallel Robot from a Type II Singularity

“…One unambiguous solution to estimate the actual pose of the PR is by using a vision system [25]. Huynh et al [26] implemented a vision/position hybrid control for a Hexa PR by defining a two-level closed-loop controller. Amarasinghe et al [27] designed a vision-based hybrid control on a mobile robot.…”