CONCLUSION & FUTURE SCOPE

This thesis work investigation has been carried out to generate flexible operational space using the robotic arm by integrating mobility feature to it. This feature facilitates the mobile manipulator with the ability to perform its tasks in a large workspace. The mobile manipulator considered in this study consists of a robotic manipulator mounted on a mobile platform. The development of the hybrid manipulator system covers mechanics of systems design, system dynamic modeling and simulations, design optimization, computer architecture, and control system design.

9.1. Contributions

This section summarizes the main contributions of the current research work as follows:

 Kinematic modeling of manipulator: The motion of the structure is analyzed in two ways, forward and inverse kinematics. Forward kinematics is the determination of the every link configuration, specially the end-effector, when the joint variables are given. Inverse kinematics deals with determining the joint variables of a robot manipulator for the given position and orientation of the end-effector. The standard Denavit-Hartenberg convention is implemented for determining coordinate frames attached to each robot’s link. Later, arm equation is developed for the considered 4-axis manipulator using forward kinematic models. From the developed arm equation, inverse kinematic models have been developed to find out the joint parameters.

 Kinematic modeling of mobile platform: The behavior of various wheeled mobile platforms has been analyzed. The work aimed towards to investigate the complete description of the control theory of wheeled robots and its maneuverability. Equations are modeled to describe the rigid body motions that arise from rolling trajectories based on the geometrical constraints of these wheels. Moreover, this study explains about the differential drive wheeled platform and its motion equations in terms of wheel velocities.

 Coordination of manipulation & locomotion: The prime objective of the work is to develop a hybridised robot system by integrating the kinematic models of a 4-axis manipulator and a differential mobile platform. The developed WMM is controlled by five parameters in which three parameters (joint velocities) gives the velocity information of the manipulator and the remaining two (left & right wheel velocities) corresponds the differential mobile platform. The final velocity Jacobean has been developed for a mobile

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manipulator which controls the entire robot system and makes the robot to follow the desired trajectories by the manipulator and platform within its workspace.

 Swarm based motion control paradigm: A new computational methodology has been proposed for solving path planning problem of an intelligent mobile platform, based on Particle Swarm Optimization. The developed algorithm is effective in avoiding obstacles and generating optimal paths within its unknown environments. The trajectories generated by robot are based on the selection of global best position in each iteration. Among the swarm, the particle which has the minimum fitness is considering as the global best position. There by, the robot moves towards the global best position and this process is continued for several iterations until the robot reaches its target position. Moreover, from the developed two fitness functions, type-1fitness is giving efficient results as compared to type-2 fitness function.

 Immune based motion control paradigm: Two efficient immune based motion planners have been introduced for solving mobile robot path planning problem in unknown environments. The first motion planner called innate immune based motion planner, comprises the parameter ‘Learning Rate’ and is not getting any global information from the system. For the second motion planner, an efficient adaptive learning mechanism has been presented and integrated to the developed innate immune based motion planner. Path analysis results showed that, both motion planners are generating collision free paths in their robotic search space. Moreover, from the developed swarm based & immune based motion planners, AIMP gives better results as compared to PSO based motion planner.

 Experiments: Two experimental setups have been developed independently, one is the task performed by the manipulator and another is for the mobile platform path generation within its environments. The manipulator in this study has 4-DOF and is used for pick & place task. Online monitoring of the entire robot system is performed while the WMM is in operation, by interfacing various electronic elements with microcontroller. In the same way the mobile platform structure is made and developed motion planners have been implemented to the designed differential mobile platform. Experimental results are in good agreement with the theoretical results. Path analysis results showed that, from the developed swarm based & immune based motion planners, AIMP gives better results as compared to PSO based motion planner.

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9.2. Conclusions

The conclusions drawn from the current investigation are depicted below:

1. Kinematic models of the manipulator have been done to analyze the position of the arm during pick and place task in various situations.

2. Kinematic analysis for the wheeled mobile platform has been done to measure its maneuverability while subjected to wheel geometric constraints.

3. Co-ordination of manipulator and mobile platform has been analyzed to reach the various coordinates in cluttered work space efficiently and effectively.

4. Swarm system architecture has been introduced to generate the optimal trajectories by the mobile manipulator. With the help of sensory information, two fitness functions namely type-1 fitness and type -2 fitness have been developed to solve path planning problem. Results showed that, type-1 fitness based motion planner giving efficient results as compared to type-2 fitness function type-1 fitness based motion planner by 7%.

5. Immune based system architecture has been introduced to generate the optimal trajectories by the mobile manipulator. With the help of sensory information, two motion planners namely IIMP and AIMP have been developed to solve path planning problem. Results showed that, AIMP gives efficient results as compared to IIMP by 4%.

6. Comparison evaluations have been made for the developed type-1 fitness based motion planner (efficient swarm based architecture) and AIMP (efficient immune based architecture). . Path analysis results showed that, AIMP gives better results as compared to type-1 fitness based PSO motion planner by 6.5%.

7. Since the developed AIMP solves the path planning problem efficiently, AIMP has been validated with ER-400 mobile platform in simulation and lab environments. Experimental results are giving good agreement with theoretical results (error <4%).

9.3. Future Scope

The focus of this thesis is on modeling, control, and coordination of a single mobile manipulator which consists of a 4-axis manipulator mounted on differential mobile platform. The following is a list of interesting directions to pursue as future work by improving the state of the art immediately related to this work.

 Multiple mobile manipulators: One of the techniques for coordinating multiple mobile manipulators is the leader/follower mothodology, in which one WMM is chosen as the leader and the other WMMs are designated as followers. These types of systems are used to perform simple tasks such as jointly transporting a large object. The algorithms

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developed in Chapter 6 & 7 can be implemented to the followers that keep in contact with and support the object to be transported. The coordination of multiple WMMs when performing complex tasks such as mechanical parts assembly is a future study where coordination strategies other than the leader/follower has to be considered.

 Two manipulators on a mobile platform: In this investigation a mobile manipulator is considered which has only one manipulator equipped on top of the mobile platform. In general, one may have two or more manipulators on the same mobile platform for performing coordination works. In that case, the motion of one manipulator will affect the motion of the mobile platform as well as the motion of the other manipulators aboard.

 The system is to be redesigned with the effects of external forces under certain circumstances. This feature renders more applicability of the proposed coordination algorithm, while the system is in interaction with ubiquitous environments.

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