![]() ![]() ![]() In general, both the techniques are offline or static in their working nature, wherein an optimal or sub-optimal policy ( π*) can be obtained from the computational environment which will be utilized for the real-time problems. ![]() The problems modeled as MDPs are mostly solved using model-based or model-free reinforcement learning (RL) algorithms. Earlier researchers have also made the MDP to address the deterministic environments. The Markov decision process (MDP) addresses the sequential decision problems in fully observable stochastic environments with transition and reward functions. In addition, several constraints can be imposed for achieving joint angles within the required range, so that an abrupt change in joint angles between two adjacent target points could be avoided. As multiple solutions are possible, optimization-based inverse kinematics model is generally preferred for the computation of joint angles. The inverse kinematics model computes the angles of all joints for a corresponding end-effector target pose (Fig. Given the coordinates of the target position and direction of tool, the end-effector target pose of the serial manipulator can be determined. In this work, a serial robot possessing higher work volume is deployed in coordination with SwarmItFIX robots. The stiffness of joints will be heterogeneous throughout the work envelope of the serial manipulator hence, the machining has to be performed within its possible range of best stiffness. This error is in the inner areas of the work and high in the areas when the robot attains its maximum reach. The repeatability error present in the trajectory of a serial manipulator is directly proportional to its reach. Different feed rate segments in a single machining trajectory increase the joints’ jerk, which ultimately leads to deviation in the machining trajectory. In robot-assisted machining, for maintaining the proper balance between machining efficiency and quality, constant feed rate is generally preferred. 1b) assist CNC machines during machining which would enhance throughput. A software framework called MRROC++ has been utilized for the path planning of robot systems including ASEA IRB 6 and SwarmItFIX. The details of the SwarmItFIX setup and the agent coordination have been described in detail in the previous works of the authors. The workspace of these SwarmItFIX platform can be modified by varying the size of the workbenches or by increasing the number of docking pins. These robots perform a novel Swing and Dock (SaD) locomotion on a fixed modular platform, called as workbench. For a single SwarmItFIX robot, all the three agents have to work in a coordinated manner to accomplish any particular task. SwarmItFIX is a multiagent platform consisting of three different agents: head, parallel kinematic machine (PKM), and Base (Fig. However, with certain modifications, this RFA platform can contribute to other applications, including material handling and material transfer. 1a) is a novel robotic setup that has been developed as a robot fixtureless assembly (RFA) platform under the European Union project mainly for sheet metal manufacturing applications. Finally, trajectories of SwarmItFIX robots are found to be completely in-line with the trajectory of tool center point (TCP) of the serial manipulator. The multi-robot path planning module of the planner computes the optimal collision-free paths of SwarmItFIX robots for all goal positions. The results obtained from the proposed planner is found to be efficient as the task planning module has computed the precise support locations and support duration for the SwarmItFIX robots. The tool velocity is assumed to be constant as it improves the quality of machining. A hexagonal segment that fits inside the boundaries of the workspace is considered as the machining trajectory. Mathematical formulation of all the three sub-problems is developed and presented in this paper. Motion of the serial manipulator and SwarmItFIX robots’ coordinated locomotion are divided into three sub-problems, viz, trajectory planning of serial manipulator, task planning of SwarmItFIX robots, and homogenous prioritized multi-robot path planning of SwarmItFIX robots. ![]() A novel offline coordination planner which follows the hierarchical based hybrid type decentralized planning strategy has been proposed. The former holds the machining tool as an end effector, and the other two robots act as swarm robotic fixtures in a sheet metal milling process. This work investigates on the coordinated locomotion between a ceiling-mounted serial manipulator and two SwarmItFIX robots. ![]()
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