Embedded system for motion control of an omnidirectional mobile robot

In this paper, an embedded system for motion control of omnidirectional mobile robots is presented. An omnidirectional mobile robot is a type of holonomic robots. It can move simultaneously and independently in translation and rotation. The RoboCup small-size league, a robotic soccer competition, is chosen as the research platform in this paper. The first part of this research is to design and implement an embedded system that can communicate with a remote server using a wireless link, and execute received commands. Second, a fuzzy-Tuned proportional-integral (PI) path planner and a related low-level controller are proposed to attain optimal input for driving a linear discrete dynamic model of the omnidirectional mobile robot. To fit the planning requirements and avoid slippage, velocity, and acceleration filters are also employed. In particular, low-level optimal controllers, such as a linear quadratic regulator (LQR) for multiple-input-multiple-output acceleration and deceleration of velocity are investigated, where an LQR controller is running on the robot with feedback from motor encoders or sensors. Simultaneously, a fuzzy adaptive PI is used as a high-level controller for position monitoring, where an appropriate vision system is used as a source of position feedback. A key contribution presented in this research is an improvement in the combined fuzzy-PI LQR controller over a traditional PI controller. Moreover, the efficiency of the proposed approach and PI controller are also discussed. Simulation and experimental evaluations are conducted with and without external disturbance. An optimal result to decrease the variances between the target trajectory and the actual output is delivered by the onboard regulator controller in this paper. The modeling and experimental results confirm the claim that utilizing the new approach in trajectory-planning controllers results in more precise motion of four-wheeled omnidirectional mobile robots. 2018 IEEE.Scopu

130 nm low power CMOS analog multiplier

Processing analog signal often involves analog multiplier and the multiplier is part of system on chip (SoC). Designing such system with a low power consumption is crucial nowadays. It is very important to increase the system battery lifetime. The design also must be smaller in size. In order to reduce the power consumption of the multiplier, an architecture that require smaller current must be designed and the approach is to use a design that is able to function at a low voltage supply. This project has designed the analog multiplier with a low power consumption using Silterra 130 nm Complementary Metal Oxide Semiconductor (CMOS) technology. A four quadrant technique is applied in the design. The scaling of transistor will help in reducing the size of the analog multiplier, and the proposed circuit architecture has produced a compact multiplier. Cadence electronic design automation (EDA) Tools is used to design the circuit. The schematic, layout, physical verification and parasitic extraction with post layout simulation are done to verify the multiplier circuit is functioning. The analog multiplier is operated with 1.2 V voltage supply and the power consumption is 98 μW. At 1 V, the power consumption is 32 μW. The total area for the design is 99 μm²

Design of an adaptive neural predictive nonlinear controller for nonholonomic mobile robot system based on posture identifier in the presence of disturbance

This paper proposes an adaptive neural predictive nonlinear controller to guide a nonholonomic wheeled mobile robot during continuous and non-continuous gradients trajectory tracking. The structure of the controller consists of two models that describe the kinematics and dynamics of the mobile robot system and a feedforward neural controller. The models are modified Elman neural network and feedforward multi-layer perceptron respectively. The modified Elman neural network model is trained off-line and on-line stages to guarantee the outputs of the model accurately represent the actual outputs of the mobile robot system. The trained neural model acts as the position and orientation identifier. The feedforward neural controller is trained off-line and adaptive weights are adapted on-line to find the reference torques, which controls the steady-state outputs of the mobile robot system. The feedback neural controller is based on the posture neural identifier and quadratic performance index optimization algorithm to find the optimal torque action in the transient state for N-step-ahead prediction. General back propagation algorithm is used to learn the feedforward neural controller and the posture neural identifier. Simulation results show the effectiveness of the proposed adaptive neural predictive control algorithm; this is demonstrated by the minimised tracking error and the smoothness of the torque control signal obtained with bounded external disturbances

Nonlinear Model Predictive Control-based Collision Avoidance for Mobile Robot

This work proposes an efficient and safe single-layer Nonlinear Model Predictive Control (NMPC) system based on LiDAR to solve the problem of autonomous navigation in cluttered environments with previously unidentified static and dynamic obstacles of any shape. Initially, LiDAR sensor data is collected. Then, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, is used to cluster the (Lidar) points that belong to each obstacle together. Moreover, a Minimum Euclidean Distance (MED) between the robot and each obstacle with the aid of a safety margin is utilized to implement safety-critical obstacle avoidance rather than existing methods in the literature that depend on enclosing the obstacles with a circle or minimum bounding ellipse. After that, to impose avoidance constraints with feasibility guarantees and without compromising stability, an NMPC for set-point stabilization is taken into consideration with a design strategy based on terminal inequality and equality constraints. Consequently, numerous obstacles can be avoided at the same time efficiently and rapidly through unstructured environments with narrow corridors.  Finally, a case study with an omnidirectional wheeled mobile robot (OWMR) is presented to assess the proposed NMPC formulation for set-point stabilization. Furthermore, the efficacy of the proposed system is tested by experiments in simulated scenarios using a robot simulator named CoppeliaSim in combination with MATLAB which utilizes the CasADi Toolbox, and Statistics and Machine Learning Toolbox. Two simulation scenarios are considered to show the performance of the proposed framework. The first scenario considers only static obstacles while the second scenario is more challenging and contains static and dynamic obstacles. In both scenarios, the OWMR successfully reached the target pose (1.5m, 1.5m, 0°) with a small deviation. Four performance indices are utilized to evaluate the set-point stabilization performance of the proposed control framework including the steady-state error in the posture vector which is less than 0.02 meters for position and 0.012 for orientation, and the integral of norm squared actual control inputs which is 19.96 and 21.74 for the first and second scenarios respectively. The proposed control framework shows a positive performance in a narrow-cluttered environment with unknown obstacles

A fault-hiding approach for the switching quasi-LPV fault-tolerant control of a four-wheeled omnidirectional mobile robot

This paper proposes a reference model approach for the trajectory tracking of a four-wheeled omnidirectional mobile robot. In particular, the error model is brought to a quasi-linear-parameter-varying (LPV) form suitable for designing an error-feedback controller. It is shown that, if polytopic techniques are used to reduce the number of constraints from infinite to finite, a solution within the standard LPV framework could not exist due to a singularity that appears in the possible values of the input matrix. Adding a switching component to the controller allows solving this problem. Moreover, a switching LPV virtual actuator is added to the control loop in order to obtain fault tolerance within the fault-hiding paradigm, keeping the stability and some desired performances under the effect of actuator faults without the need of retuning the nominal controller. The effectiveness of the proposed approach is shown and proved through simulation and experimental results.This work has been funded by the Spanish MINECO through the project CYCYT SHERECS (ref. DPI2011-26243), by the European Commission through contract i-Sense (ref. FP7-ICT-2009-6-270428), by AGAUR through the contracts FI-DGR 2013 (ref. 2013FIB00218) and FI-DGR 2014 (ref. 2014FI B1 00172) and by the DGR of Generalitat de Catalunya (SAC group Ref. 2014/SGR/374).Peer Reviewe

Efficient Navigation and Motion Control for Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC Implementation

The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems

Unlimited-wokspace teleoperation

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2012Includes bibliographical references (leaves: 100-105)Text in English; Abstract: Turkish and Englishxiv, 109 leavesTeleoperation is, in its brief description, operating a vehicle or a manipulator from a distance. Teleoperation is used to reduce mission cost, protect humans from accidents that can be occurred during the mission, and perform complex missions for tasks that take place in areas which are difficult to reach or dangerous for humans. Teleoperation is divided into two main categories as unilateral and bilateral teleoperation according to information flow. This flow can be configured to be in either one direction (only from master to slave) or two directions (from master to slave and from slave to master). In unlimited-workspace teleoperation, one of the types of bilateral teleoperation, mobile robots are controlled by the operator and environmental information is transferred from the mobile robot to the operator. Teleoperated vehicles can be used in a variety of missions in air, on ground and in water. Therefore, different constructional types of robots can be designed for the different types of missions. This thesis aims to design and develop an unlimited-workspace teleoperation which includes an omnidirectional mobile robot as the slave system to be used in further researches. Initially, an omnidirectional mobile robot was manufactured and robot-operator interaction and efficient data transfer was provided with the established communication line. Wheel velocities were measured in real-time by Hall-effect sensors mounted on robot chassis to be integrated in controllers. A dynamic obstacle detection system, which is suitable for omnidirectional mobility, was developed and two obstacle avoidance algorithms (semi-autonomous and force reflecting) were created and tested. Distance information between the robot and the obstacles was collected by an array of sensors mounted on the robot. In the semi-autonomous teleoperation scenario, distance information is used to avoid obstacles autonomously and in the force-reflecting teleoperation scenario obstacles are informed to the user by sending back the artificially created forces acting on the slave robot. The test results indicate that obstacle avoidance performance of the developed vehicle with two algorithms is acceptable in all test scenarios. In addition, two control models were developed (kinematic and dynamic control) for the local controller of the slave robot. Also, kinematic controller was supported by gyroscope