In many space exploration missions, where precise trajectory control is essential for successful operations, space robotics plays a critical role. This paper uses bond graph modeling to describe the dynamics of the system and to efficiently apply control schemes. This modeling method provides a strong framework for examining the behaviour and interactions of systems. Although bond graph modeling and Proportional-Integral-Derivative (PID) control are frequently combined, PID controllers frequently perform less than optimally in complicated or nonlinear systems. The controller parameters are optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to get over this restriction. GA simulates the process of natural selection and evolution while, PSO a population-based optimization method, imitates the behaviour of a swarm. The goal of both algorithms is to determine the PID parameters in order to reduce trajectory errors and improve efficiency. This study presents an enhanced capability for redundant space robots to manage their trajectory during contacts with free-floating objects by merging PID with PSO and GA respectively.

The trajectory of a space robot poses challenges due to its floating base, irregular surfaces, and unexpected circumstances. Combining redundant space robots with a control system offers a highly effective and efficient solution. The aim of this study is to present a trajectory control method for commanding two redundant space robots, capable of handling distinct floats. Controllers are employed to regulate the direction and location of robot tips within space robotic systems. These controllers continually analyse the variance between expected and actual set points, calculating control outputs for manipulating various floating objects using a pair of cooperative redundant space robots. The bond graph formulation is utilized for modeling and simulation of the two redundant space robots. The proposed method demonstrates significant accuracy, as per simulation findings.

The dynamics of inverted pendulum systems have an inherit property of instability, nonlinearity and underactuation. Therefore, the inverted pendulum systems have been recognized as a benchmark problem to validate emerging controllers. In addition, the dynamics of many real-world system resembles the inverted pendulum systems. To broaden the diversity of this classical system, this work focuses on a new type of inverted pendulum i.e., spatial inverted pendulum. In contrast to classical inverted pendulum systems, this system is highly complex to model and control due to its large degrees of freedom as well as underactuation. This paper proposes the bond graph model of this complex system to derive its dynamic equations. The bond graph technique only requires kinematics of the model and derives the complex dynamics by itself. Furthermore, the robust neuro-fuzzy controller based on linear quadratic regulator (LQR) controller is designed and simulated for the stabilization of the system. The proposed controller provides an advantage of improved robustness compared to classical LQR controller. The robustness of the proposed controller is verified by simulation results for various pendulum masses. The results demonstrate that when the mass variation is significant, proposed controller outperforms the LQR controller by achieving superior performance and robustness to parameter uncertainties.

Trajectory control of mobile manipulator under joint lock failure poses new challenges. In this work fault tolerant scheme is proposed for trajectory control under joint lock failure through reconfiguration while exploiting the existing redundancy of the mobile manipulator system. Closed loop inverse kinematics (CLIK) algorithm are employed to transform the tip trajectory control problem to joint space control problem. In this work, two kinds of reconfiguration schemes are proposed, one using the redundancy in the manipulator only and another using the redundancy available with complete mobile manipulator system. Both types of the reconfiguration schemes use CLIK algorithm to calculate mobile base position and manipulator joint configurations so that the tip follows the reference trajectory in spite of locked joint failure of manipulator. Simulation and experimental results are presented to validate the proposed fault tolerant schemes.

This paper presents the solution to inverse kinematics of a mobile manipulator operating in both obstacle free environment and cluttered workspace. This work conceives an optimization problem using bidirectional particle swarm optimization method to obtain the inverse kinematics solution of the mobile manipulator. The bidirectional search algorithm helps to search for the solution faster than conventional unidirectional search. The novel manipulator decoupling technique proposed in this paper enables the system to carry out bidirectional search to find inverse kinematics. Collision detection and obstacle avoidance tasks are undertaken using the algorithm proposed in this work to avoid line type obstacles. A multi-objective optimization problem is formulated with minimum total joint movement as another objective function and collision avoidance as constraint. Numerical are experiments performed on a four degree of freedom manipulator mounted over a mobile base to illustrate the efficacy of the proposed method.

A mobile manipulator is typically an assembly of a mobile robot base and an on-board manipulator arm. As the manipulator arm is mounted over the mobile robot base, the controller has the additional task of taking care of the disturbances of the mobile robot due to the dynamic interactions between the mobile robot base and manipulator arm. In the present work, dynamic models for the manipulator arm and an omni-wheeled mobile robot base were developed separately and then both were combined. Two control strategies, namely only manipulator arm control (OMAC) and simultaneous manipulator and base control (SMBC) were developed for the effective control of tip trajectory. In both strategies, an amnesia recovery coupled with classical proportional integral and derivative (PID) control was used. The bond graph methodology was used for the development of the dynamic model and control for the mobile manipulator. Simulation results are presented to illustrate the efficacy of the two control strategies.

This paper focuses on the problem of reducing via control actions the interaction between the mobile platform and the arm in a mobile robot equipped with a redundant planar manipulator. It is solved maintaining the mobile base as immobile as possible once it has been moved to a desired position, which serves to a double purpose. On the one hand, it helps keeping fixed the workspace of the manipulator, as predefined in the world coordinates, in order for the end-effector being able to reach the points where it has to perform its tasks. On the other hand, as this reduces the disturbances that the otherwise moving base would introduce on the arm movement, this serves to improve the precision in the execution of whatever task the end-effector has to perform. The problem is solved via a combination of operational space control to solve the arm tip trajectory tracking problem and energy-shaping and damping assignment to restrict the movement of the mobile base. The latter is achieved using a backstepping technique in the Bond Graph domain which emulates dissipation and stiffness at the base wheels coordinates through the control of the DC motors actuating them. Simulation results show the good performance of the control system.

Reconfiguration of a mobile manipulator is undertaken in this work. A three wheeled omnidirectional mobile robot mounted with a five degree of freedom manipulator has been considered. The bond graph model of the mobile manipulator is developed using the obtained kinematic relations. The end-effector of the manipulator has to track a given reference trajectory, assuming the trajectory is in the workspace of the manipulator. A conventional PID control is applied to ensure that the end-effector to follows the reference trajectory. Subsequently, the reconfiguration methodology of simultaneous actuation of mobile base and manipulator has been proposed when one of the joint actuator of the manipulator was failed.

The appearance of algebraic constraints among energy variables in models of physical systems leads to sets of (possibly nonlinear) implicit state equations, which usually complicate the treatment of the problems to be solved on the model. Building up on the Bond Graph model of a Planar Mobile Robotic Manipulator, this paper discusses some techniques to handle this kind of situations, determined here by the coupling of rigid bodies. Two alternatives to break the constraints are presented, consisting in the insertion between the coupled elements of: a) parasitic components –mostly spring-dampers, which is standard practice– or b) residual sinks – which is equivalent to the practice of adding constraint forces. Modifying the Bond Graph through the introduction of storage fields is the third method presented. Further, the extraction of constraint-free Euler-Lagrange and Hamiltonian descriptions from the Bond Graph are addressed. Finally, the suitability of all of these five alternatives for the purposes of simulation, analysis and control system design are discussed, and illustrated with simulation results.

The appearance of algebraic constraints among energy variables in models of physical systems leads to sets of (possibly nonlinear) implicit state equations, which usually complicate the treatment of the problems to be solved on the model. Building up on the Bond Graph model of a Planar Mobile Robotic Manipulator, this paper discusses some techniques to handle this kind of situations, determined here by the coupling of rigid bodies. Two alternatives to break the constraints are presented, consisting in the insertion between the coupled elements of: a) parasitic components-mostly springdampers, which is standard practice- or b) residual sinks - which is equivalent to the practice of adding constraint forces. Modifying the Bond Graph through the introduction of storage fields is the third method presented. Further, the extraction of constraint-free Euler- Lagrange and Hamiltonian descriptions from the Bond Graph are addressed. Finally, the suitability of all of these five alternatives for the purposes of simulation, analysis and control system design are discussed, and illustrated with simulation results.

Manipulators with mobile base are gaining momentum in the industry due to improved workspace and flexible adaptability to changes in the product designs. Although there has been research in this field for a considerable time, many issues are still to be resolved viz. obstacle avoidance, redundancy resolution for singularity avoidance, dynamic analysis of combined manipulator and a mobile robot, base disturbances and optimized motion planning for limited power sources. Meticulous motion planning is a very essential for a mobile robot, particularly when it is carrying an on board manipulator. In this work a three wheeled mobile robot (Robotino) with an on board redundant manipulator (MRBM) or simply mobile manipulator (MM) of eight degrees of freedom is considered for the analysis. Kinematic model of MM is developed first afterwards the Bond graph model of the mobile robot combined with manipulator is developed. Dynamic analysis is performed using the system equations generated from the bond graph model and simulation results are presented for different cases.