Three-dimensional spatial energy-quality map construction for optimal robot placement in multi-robot additive manufacturing

The adoption of multiple robots for collaborative additive manufacturing is rapidly gaining attention in the industry and research community due to their numerous advantages, such as fast and efficient printing of largescale parts and their suitability for hazardous or extraterrestrial environments. However, to fully harness the potential of multi-robot additive manufacturing systems, several challenges must be addressed from a process planning perspective. These include part decomposition, part/robot placement, trajectory planning, and print scheduling considering various quality, energy efficiency, time, and reachability constraints as well as different robotic team compositions including mobile/stationary robots, aerial mobility/ground mobility, and heterogenous/homogenous teams. This work explores the optimal positioning of part with respect to the 3D printer robots and inversely the 3D printer robots with respect to the part in case of large structures (given that the structure is assumed to be grounded/fixed and not movable) in multi-robot additive manufacturing scenarios. A novel decision-making methodology for the robot placement problem, i.e., optimal positioning of multiple robots around a large-scale structure, based on the energy consumption of the robots during the additive manufacturing process as well as the final dimensional accuracy of the printed structure, is proposed. The decision making is guided by the construction of a 3D spatial energy-quality map around each of the robot's bases based on their kinematics as well as the geometry of the assigned part for additive manufacturing using the proposed energy and quality modules. Additionally, the simulated annealing algorithm is adopted to quickly identify the optimal robot positionings for the collaborative additive manufacturing task. Different case studies demonstrating the effectiveness of the proposed methodology in reducing energy consumption while maintaining the required print quality are presented. Finally, sensitivity analyses are performed to evaluate the impact of various parameters including the robot velocity and acceleration, number of robots, decomposition scenarios, and ratio of the printed geometry with respect to the robot's reach on the energy and quality metrics.