We consider multi-agent transport task problems where, e.g. in a factory setting, items have to be delivered from a given start to a goal pose while the delivering robots need to avoid collisions with each other on the floor. We introduce a Task Conflict-Based Search (TCBS) Algorithm to solve the combined delivery task allocation and multi-agent path planning problem optimally. The problem is known to be NP-hard and the optimal solver cannot scale. However, we introduce it as a baseline to evaluate the sub-optimality of other approaches. We show experimental results that compare our solver with different sub-optimal ones in terms of regret.
The multi-agent path-finding (MAPF) problem has recently received a lot of attention. However, it does not capture important characteristics of many real-world domains, such as automated warehouses, where agents are constantly engaged with new tasks. In this paper, we therefore study a lifelong version of the MAPF problem, called the multi-agent pickup and delivery (MAPD) problem. In the MAPD problem, agents have to attend to a stream of delivery tasks in an online setting. One agent has to be assigned to each delivery task. This agent has to first move to a given pickup location and then to a given delivery location while avoiding collisions with other agents. We present two decoupled MAPD algorithms, Token Passing (TP) and Token Passing with Task Swaps (TPTS). Theoretically, we show that they solve all well-formed MAPD instances, a realistic subclass of MAPD instances. Experimentally, we compare them against a centralized strawman MAPD algorithm without this guarantee in a simulated warehouse system. TP can easily be extended to a fully distributed MAPD algorithm and is the best choice when real-time computation is of primary concern since it remains efficient for MAPD instances with hundreds of agents and tasks. TPTS requires limited communication among agents and balances well between TP and the centralized MAPD algorithm. Robotics technology has recently matured sufficiently to deploy autonomous robotic systems for daily use in several applications: from disaster response to environmental monitoring and logistics. In such applications, robots must establish collaborative interactions so to achieve their individual and collective goals and a key problem is for robots to make individual decisions so to optimize a system wide objective function. This problem is typically referred to as coordination. In this paper, we first describe modern optimization techniques for coordination in multiRobot systems. Specifically, we focus on approaches that are based on algorithms widely used to solve graphical models and constraint optimization problems, such as the max-sum algorithm. We then analyze the coordination problem faced by a set of robots operating in a warehouse logistic application. In this context robots must transport items from loading to unloading bays so to complete packages to be delivered to customers. Robots must cooperate to maximize the number of packages completed in the unit of time. To this end a crucial component is to avoid interferences when moving in the environment. We show how such problem can be formalized as a Distributed Constrained Optimization problem and we provide a solution based on the binary max-sum algorithm. Finally, we provide a quantitative evaluation of our approach in a simulated scenario using standard robotics tools (ROS and Gazebo). 