Browsing by Author "Guruprasad, K. R."

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Multi-agent system Inspired Distributed Control of a Manipulator
(National Institute of Technology Karnataka, Surathkal, 2019) Soumya, S.; Guruprasad, K. R.
Robotic manipulators are used in a wide variety of applications. In all the applications, the end-effector or the tool of the manipulator needs to be moved along a desired trajectory in its workspace. In this thesis we present model-based control schemes for robotic manipulators using a distributed architecture. Inspired by multi-agent/robotic systems, first we perceive a manipulator, which is MIMO multi-body system, as a multi-agent system with the joints (or the joint-link pairs) as sub-systems or agents, which interact with each other in a distributed manner. Here, the interaction between the joint-link agents is in the form of interactive forces and moments that lead to dynamic coupling. As the adjacency graph formed by the joint-link agents as nodes and links between two joints as edges is connected, the direct interactions between the immediate neighbors result in interaction (in the form of dynamic coupling) between any two joint-link agents. We carry out an analysis of the computational cost associated with the model-based control law for planar serial-link manipulators with degrees-of-freedom varying from 2 to 6 using Maple. Using this analysis, we establish the fact that the total computational cost associated with the model-based control law increases with the degrees-of-freedom. Toward mitigating the computational overhead associated with the conventional model-based control scheme, we propose a distributed architecture for the motion control of manipulator exploiting its multi-agent nature. Here, each joint-link agent is controlled by a dedicated controller, and the joint-level controllers communicate and cooperate among themselves. Though one of the primary motivation for the proposed distributed control scheme is to reduce the computational overhead, in this thesis we rely on the natural distributed nature of the manipulator dynamics rather than the program optimization or operation optimization techniques that are used at the algorithmic level. We propose a simple distributed control scheme based on the conventional model-based control law and show that it can be implemented using thedistributed control architecture. Here, apart from the reduced computational lead time due to distributed computation of the control law at the joint-levels, unlike the decentralized or independent joint control schemes, the proposed control scheme fully utilizes the knowledge of the system dynamics, leading to a feedback linearized closed-loop error dynamics. Though the proposed distributed control scheme is suitable for a general serial-link manipulator, in this thesis, we focus on planar manipulators with revolute joints. We prove, that the proposed distributed control scheme makes the links of the manipulator, and hence the end-effector, follow the desired trajectory, asymptotically. We define a quantity called distribution effectiveness to quantify how the distributed control schemes share the computational load among the individual joint-level controllers. We also provide a discussion on implication of the discrete-time implementation of the proposed distributed control scheme in contrast to the conventional model-based control scheme. We design a distributed model-based controller for a planar 3R manipulator, to illustrate the proposed distributed control scheme and the distributed control architecture for a manipulator. For the case of planar manipulators with degrees-of-freedom 2 − 6, we provide a method to reduce the computational cost associated with dynamic equations used in the control law by identifying repetitive terms, which may be generalized for any other manipulator in principle. In an attempt to further improve the distribution effectiveness and reduce the computational lead time, we propose a cooperative control scheme for a manipulator using the distributed control architecture. While in the basic distributed control scheme proposed, joint-level controllers interact amongst themselves in terms of exchanging desired and measured states (and their derivatives), in the case of the cooperative control scheme the joint-level controller cooperate by exchanging certain computed terms between them. Even in this case, we provide a discussion on implication of the discrete-time implementation. We prove, that the proposed cooperative control law makes the links of the manipulator, and hence the end-effector, follow the desiredtrajectory, asymptotically. We design a cooperative distributed model-based controller for a planar 3R manipulator, to illustrate the proposed cooperative manipulator control scheme implemented in the distributed control architecture. We also provide a discussion on computational effectiveness of the proposed cooperative distributed control scheme along with a method to further reduce the computational lead time by identifying repetitive terms in the control law. We present a detailed analysis of computational cost associated with the dynamic equation of planar manipulators with degrees-of-freedom from 2 to 6, where we analyze the cost involved in the proposed distributed control schemes in contrast to that in the conventional centralized model-based control scheme, using Maple. We provide results which indicate that the distribution effectiveness of the proposed simple distributed control schemes improves with degrees-offreedom of the manipulator. We also provide a detailed discussion on reducing the computational cost by identifying repetitive terms in the dynamic equations at each joint-level, for planar manipulators with degrees-of-freedom from 3 to 6. We then present simulation results demonstrating the proposed control schemes. We present results which show how the trajectory tracking performance of the model-based control law degrades with increase in the sampling time. Then we present results which demonstrate that with the proposed distributed control schemes every joint tracks the desired trajectory satisfactorily, in comparison with the independent-joint PID control scheme. We present details of implementation of the proposed distributed manipulator control scheme using Simulink-ROS hybrid platform based on Matlab’s Robotics toolbox, which provides a more realistic simulation result and it is also amenable for hardware implementation. Finally, we present a discussion to compare decentralized control schemes presented in the literature with the distributed control schemes presented in this thesis.
Simultaneous Exploration and Coverage with Multiple Robots using Generalized Voronoi Partition
(National Institute of Technology Karnataka, Surathkal, 2019) Nair, Vishnu G.; Guruprasad, K. R.
The problem of area coverage by mobile robots is very useful in several applications such as vacuum cleaning, lawn mowing, landmine detection and de-mining, planetary exploration, etc. Using multiple robots to cover a specified region is expected to reduce the coverage time, apart from possible robustness to failure of a (or a few) robot(s). In this work we address a multi-robotic area coverage problem. When multiple robots need to cover a given area, the main concern is of avoiding repetitive coverage apart from complete coverage of the given area. Partitioning the area to be covered into cells and allotting one each cell to each of the robots for coverage solves the problem of duplicity, thus avoiding repetitive coverage, in a very simple and elegant manner. However, the spatial partitioning may lead to additional problems leading to either incomplete coverage or coverage overlap near the partition boundary, and possible non-contiguous partitioned cells in the presence of obstacles. Also, the coverage algorithms reported in the literature are either off-line, using complete prior knowledge about the arena, or online, using no a priori knowledge, but there is no provision for using any partial knowledge (of map) when available. In this thesis we address a problem of coverage path planning for multiple cooperative autonomous mobile robots. We consider a \partition and cover" approach to the multi-robotic coverage problem due to its inherent advantages of i) independent of the underlying single robot coverage algorithm, ii) reduced memory requirement due to spatial task partitioning, iii) minimal or no communication requirement during performance of the coverage task, and iv) no requirement of special collision avoidance again due the spatial task partitioning. Among the \partition and cover" approaches reported in the literature, we used Voronoi partition based coverage due to its main advantage of possible distributed implementation. One of the challenges associated with a multi-robot coverage problem is uniform load distribution among the robots. In the context of a \partition and cover" strategy employed in this thesis, this problem boils down to uniformpartitioning assuming that the coverage load is proportional to the are of the coverage. This is a classical problem of equatable partitioning that is addresses in locational optimization or sensor coverage problems. In this work, we provide a very simple solution to this problem by using the concept of the centroidal Voronoi configuration used in the locational optimization/sensor coverage literature. We introduce the concept of deploying \virtual nodes" rather than the robots and partitioning the space based on the \virtual nodes" locations. With this, we avoid unnecessary robot motion (in the sense that motion without performing coverage). We demonstrate with examples that with this approach, the areas of all the cells are approximately same, thus ensuring a uniform coverage load distribution among the individual robots. We propose Manhattan-VPC, a Manhattan distance based Voronoi Partition coverage algorithm that decomposes a 2D×2D gridded region completely avoiding partition boundary issues such as coverage gap and coverage overlap, that arise with the use of the standard Voronoi partition. Here, the robot footprint is assumed to be D × D square. We have established both by formal analysis and simulation and experiments with physical robots, that the proposed ManhattanVPC provides complete and non-overlapping coverage even in the presence of simple obstacles and completely avoids the partition boundary induced coverage gap and overlap. We also propose Geodesic-VPC, a Voronoi partition based coverage algorithm using the Geodesic distance in the place of the standard Euclidean distance. With this approach we ensure that the cells that individual robots have to cover are contiguous even in the presence of arbitrary obstacles. However, here, unlike in the case of Manhattan VPC (or the basic VPC), we assume that the map of the environment is available a priori to the planner. We then combine the Manhattan metric over the 2D × 2D grid and Geodesic metric and propose a GM-VPC algorithm. We establish both by formal analysis and simulation experiments that with the GM-VPC algorithm robots provide complete and non-overlapping coverage in the presence of arbitraryknown obstacles. Finally we combine exploration and coverage problems to address a novel SimExCoverage problem. Here, the primary task of the robots is coverage while it uses intermittent exploration to generate partial map that is used by coverage path planner. This approach combines the advantages of both the off-line and online coverage strategies. We first present a single robot SimExCoverage problem and then extend it to a multi-robotic scenario. While the Manhattan-VPC and SimExCoverage algorithms are suitable for scenarios when map of the area is not available, the Geodesic-VPC and GM-VPC strategies are useful when map of the region is available. We use a Boustrophedon-like coverage algorithm and the spanning tree based coverage algorithm which represent the approximate cellular decomposition based coverage algorithms and exact cellular decomposition based coverage algorithms reported in the literature as underlying single-robot coverage algorithms for demonstrating the proposed generalized Voronoi partition based coverage strategies and the SimExCoverage algorithms.