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Decision-making for Aerial Construction with UAVs
Decision-making for Aerial Construction with UAVs
Autonomous aerial construction focuses on enabling teams of unmanned aerial vehicles (UAVs) to collaboratively build complex structures with minimal human intervention. Aerial construction introduces unique decision-making challenges that go beyond traditional multi-robot coordination, including strict structural dependencies, safety-critical spatiotemporal constraints, limited onboard resources, and process-driven deadlines such as material curing. To address these challenges, our work develops a hierarchical autonomy stack that tightly integrates optimal mission planning, safety-aware scheduling, and reactive execution.
Approaches
Approaches
"Optimal Safety-Aware Scheduling for Multi-Agent Aerial 3D Printing with Utility Maximization under Dependency Constraints. " by Marios-Nektarios Stamatopoulos, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
"Hierarchical Reactive Task Allocation with Dynamic Conflict Resolution Framework for Collaborative Aerial 3D Printing", by Marios-Nektarios Stamatopoulos, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
"Safety-Aware Optimal Scheduling for Autonomous Masonry Construction using Collaborative Heterogeneous Aerial Robots", by Marios-Nektarios Stamatopoulos, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
"Autonomous Reactive Masonry Construction using Collaborative Heterogeneous Aerial Robots with Experimental Demonstration", by Marios-Nektarios Stamatopoulos, Elias Small, Shridhar Velhal, Avijit Banerjee, George Nikolakopoulos
Perimeter Defense Problem
Perimeter Defense Problem
Rapidly evolving technologies in the autonomous operation of UAVs and associated developments in low-cost sensors have created significant interest among researchers in using them for various civil and military applications. With the autonomy and presence of various sensing equipment, onboard UAVs lead to problems in the privacy, safety, and security of many safety-critical infrastructures. A critical infrastructure that needs to be protected is approximated by the convex region and called territory. A team of UAVs that protects the territory is called the defenders and UAVs which try to enter the territory are called the intruders. A team of defenders operates inside and, on the perimeter, and protects the territory from intruders by capturing intruders on the perimeter is referred as the Perimeter Defense Problem (PDP).
Rapidly evolving technologies in the autonomous operation of UAVs and associated developments in low-cost sensors have created significant interest among researchers in using them for various civil and military applications. With the autonomy and presence of various sensing equipment, onboard UAVs lead to problems in the privacy, safety, and security of many safety-critical infrastructures. A critical infrastructure that needs to be protected is approximated by the convex region and called territory. A team of UAVs that protects the territory is called the defenders and UAVs which try to enter the territory are called the intruders. A team of defenders operates inside and, on the perimeter, and protects the territory from intruders by capturing intruders on the perimeter is referred as the Perimeter Defense Problem (PDP).
Spatio-Temporal Multi-Task Assignment Approaches for Perimeter Defense Problems
Spatio-Temporal Multi-Task Assignment Approaches for Perimeter Defense Problems
Spatio-Temporal formulation Multi-Task Assignment (STMTA)
Dynamic Resource allocation with Spatio-Temporal Multi-Task Assignments (DREAM)
Priority-based DREAM
Decentralized STMTA
Pick-up and Just-In-Time Delivery Problem
A typical warehouse has many objects that need picking and placing between various locations, which is currently done by autonomous robots. If items are delivered at an exact time, the subsequent processes can start immediately, improving the efficiency of operations. Also, it will reduce/eliminate the need for local storage space. The packaging of different items for an order is one example where all items must be at the packaging counter at the desired time. Local storage is not required if all items come to the packaging counter at desired times; it also helps improve efficacy by reducing redundant pick and place operations.
Just-in-time (JIT) management strategy implemented in manufacturing and automobile industries to align raw-material orders from suppliers directly with production schedules. A major concern in JIT approach is the potential disruptions in the supply chain. In this paper, we propose use of robots for pickup and just-in-time delivery tasks in warehouse operations and get the benefits of the JIT approach with a robust supply chain maintained with robots. This work proposes the modified dynamic resource allocation approach to compute the minimum resources (robots) required for PJITD tasks and their optimal trajectories to execute all the given PJITD tasks. The proposed approach is non-iterative and requires a maximum of two-step calculations of task assignments problems. The high fidelity simulations in ROS2-Gazebo and hardware experimental results show the real time compatibility and working of the proposed approach to solve the pick-up and just-in-time delivery problems.
Music Playing Robots
In music playing robots problem, an algorithm needs to compute the trajectories for a dynamically sized team of robots who will play the musical notes by traveling through the specific locations associated with musical notes at their respective specific times.
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