Multi-robot Task Planning Problem with Uncertainty in Game Theoretic Framework (original) (raw)
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Game Theoretic Approach to Multi-Robot Planning
In multi-agent (multi-robot) environment each agent tries to reach its own goal and it implies that in most cases the agent goals conflict. Under some assumptions such problems can be modelled as a STRIPS system (for instance Block World environment) with one initial state and disjunction of goal states. If STRIPS planning problem is invertible then it is possible to apply machinery for planning in the presence of incomplete information to solve the inverted problem and then to find a solution for the original problem. In the paper we propose the planning algorithm that solves problem described above and, basing on known results, we analyse its computational complexity. To make the plan complete we use non-cooperative strategies.
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This paper deals with a new simulation tool for the improvement of multi-robot pick & place applications performance combining behavioral simulation of multiple robots and products flows. A novelty of the proposed work is to take into account in the simulation not only the scheduling rules of each robot, but also the robots collaborative aspect to ensure the desired overall performance for a given task. The transition from simulation to implementation of pick & place strategies is also an issue tackled in this paper. By using a typical example consisting of comparing techniques to optimize the workflow, the utility of the simulation tool is proven. First experimental results validate the simulation results.
2016 IEEE International Conference on Industrial Technology (ICIT), 2016
This paper deals with a new simulation tool for the improvement of multi-robot pick & place applications performance combining behavioral simulation of multiple robots and product flows. A contribution of the proposed work is to take into account in the simulation not only the scheduling rules of each robot, but also the robots collaborative aspect to ensure the desired overall performance for a given task (high-level programming). The transition from simulation to implementation of pick & place strategies is also an important issue tackled in this paper. By using a typical example consisting of comparing techniques to optimize the workflow, the utility of the simulation tool is shown. Experimental results validate the simulation results and demonstrate the interest of the developed methodology.
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
Robotics: Science and Systems XVI, 2020
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of unsuccessful tasks at the end of the operation horizon. We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination and addresses them in a hierarchical manner. The lower layer computes policies for individual agents using dynamic programming with tree search, and the upper layer resolves conflicts in individual plans to obtain a valid multi-agent allocation. Our algorithm, Stochastic Conflict-Based Allocation (SCoBA), is optimal in expectation and complete under some reasonable assumptions. In practice, SCoBA is computationally efficient enough to interleave planning and execution online. On the metric of successful task completion, SCoBA consistently outperforms a number of baseline methods and shows strong competitive performance against an oracle with complete lookahead. It also scales well with the number of tasks and agents. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
Auction like Task Allocation and Motion Coordination Strategies for Multi-Robot Transport Tasks
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In this paper we present a task allocation method based on auction mechanisms that allows to find how many robots are needed to execute a task. This number is unknown and depends on several factors. There are also different types of tasks that must be executed using different skills of the robots. It is very difficult to find a correct allocation under this conditions and at present it is an open problem. We also propose two motion coordination methods to reduce the interference effect between robots. To test our system a modification of the well know foraging task has been used. This task introduces special characteristics, not directly studied in previous work, that our method try to solve.
Search for Multi-Robot Coordination under Uncertainty
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We introduce a principled method for multi-robot coordination based on a general model (termed a MacDec-POMDP) of multi-robot cooperative planning in the presence of stochasticity, uncertain sensing, and communication limitations. A new MacDec-POMDP planning algorithm is presented that searches over policies represented as finite-state controllers, rather than the previous policy tree representation. Finite-state controllers can be much more concise than trees, are much easier to interpret, and can operate over an infinite horizon. The resulting policy search algorithm requires a substantially simpler simulator that models only the outcomes of executing a given set of motor controllers, not the details of the executions themselves and can solve significantly larger problems than existing MacDecPOMDP planners. We demonstrate significant performance improvements over previous methods and show that our method can be used for actual multi-robot systems through experiments on a cooperati...
A Framework for Multi-Robot Coordination
In this thesis work, a complete framework for multi-robot coordination in which robots collectively execute interdependent tasks of an overall complex mission requiring diverse capabilities is proposed. Given a heterogeneous team of robots and task dependencies, the proposed framework provides a distributed, robust mechanism for assigning robots to tasks in an order that efficiently completes the mission. The approach is robust to unreliable communication and robot failures. The framework is based on the market-based approach, and therefore scalable. In order to obtain optimum allocations in noisy environments, a coalition maintenance scheme ensuring dynamic reconfiguration is introduced. Additional routines, called precautions are added in the framework for addressing different types of failures common in robot systems and solving conflicts in cases of these failures. The final solutions are close to optimal with the available resources at hand by using appropriate cost functions. The framework has been tested in simulations that include variable message loss rates and robot failures. The experiments illustrate the effectiveness of the proposed system in realistic scenarios.