# Sampling-Based Path Planning Algorithm for a Plug & Produce Environment
## 4 Conclusions and Discussion  
This article investigated the suitable online path planning algorithm for a multi-agent-based manufacturing system. For this investigation, five different sampling-based path planning algorithms are considered and implemented successfully within the Plug & Produce environment. Here, the robot agent communicated with the path planner agent to generate a collision-free path. The path planner agent generated the collision-free optimal path using the adaptive RRT* algorithm even though there are several constraints present in the environment when the tool dimension can be accommodated between the obstacles. Two aspects of saving time during path generation could be identified: (1) the adaptive RRT and adaptive RRT* are faster than the other compared algorithms when a high number of nodes is required to avoid collisions due to narrow spaces between obstacles. However, the path generated from all the optimal algorithms are taking almost equal time to move along the path. (2) The optimal algorithms take more computational time to generate a path than non-optimal algorithms due to optimizing the path after generating the nodes. This higher computational time can be compensated for by reusing the already-generated paths from the saved path database in the agent system. The adaptive RRT* algorithm is preferred and best suited for online path generators in the agent-based platform in C-MAS-based applications for flexible manufacturing. The energy consumed by each path is measured for all the optimal algorithms via the Robotstudio add-in called signal analyzer online, and the measured energy is validated using the C.A 8335 Power Quality Analyzer. The energy consumption is considerably lower for the RRT* algorithm, with up to 1000 mm/sec of the robot. It is preferred to use the speed of the robot less in auto mode compared to the manual mode. All the generated paths are tested in the industrial test bed configured with the ABB IRB6700 robot within a Plug & Produce environment and validated whether the newly developed algorithms follow the characteristics of the existing traditional sampling-based algorithms and at the same time offered a path with constraints in the environment if a path exists. For further research based on path planning, to save the computational time of optimal path planning algorithms, we aim to split the generated path into different numbers of waypoints. Each time the robot agent requests for a path to be generated, the path planner  
Figure 5: The response between the speed and the energy of the optimal algorithms was tested. agent looks for the existing paths for each waypoint and the matching environments. Then, it tries to reuse the already-generated points for some of the waypoints. Only the new waypoint path will be generated and added along with the already-existing generated waypoints. From this approach, we can considerably reduce the computational time of the algorithm, which will facilitate the use of a faster online path planning agent service system. In addition to that, the authors are interested in testing other algorithms in the RRT family like RRT*-smart, potential-guided RRT*, real-time RRT*, and informed RRT* within the Plug & Produce environment. Conceptualization, S.R. and F.D. ; methodology, S.R. and K.M.E. ; software, S.R. and F.D. ; validation, S.R., K.M.E., F.D. and M.E. ; formal analysis, S.R., K.E, F.D. and M.E. ; investigation, S.R., K.M.E. and M.E; resources, S.R., K.M.E., F.D. and M.E; data curation, S.R., K.E., F.D. and M.E. ; writing--original draft preparation, S.R. ; writing--review and editing, S.R., K.M.E., F.D. and M.E. ; visualization, S.R. and K.M.E. ; supervision, F.D. ; project administration, M.E. ; funding acquisition, M.E. All authors have read and agreed to the published version of the manuscript. This research was funded by Miljo for Flexiblebl och Innovativ Automation, Project reference: 20201192, funded under Europeiska regionala utvecklingsfonden/VGR. Not applicable.