Integrating Planning and Execution for a Team of Heterogeneous Robots with Time and Communication Constraints (original) (raw)

Distributed Mission Planning of Complex Tasks for Heterogeneous Multi-Robot Teams

ArXiv, 2021

In this paper, we propose a distributed multi-stage optimization method for planning complex missions for heterogeneous multi-robot teams. This class of problems involves tasks that can be executed in different ways and are associated with cross-schedule dependencies that constrain the schedules of the different robots in the system. The proposed approach involves a multi-objective heuristic search of the mission, represented as a hierarchical tree that defines the mission goal. This procedure outputs several favorable ways to fulfil the mission, which directly feed into the next stage of the method. We propose a distributed metaheuristic based on evolutionary computation to allocate tasks and generate schedules for the set of chosen decompositions. The method is evaluated in a simulation setup of an automated greenhouse use case, where we demonstrate the method’s ability to adapt the planning strategy depending on the available robots and the given optimization criteria.

Spatially-Distributed Missions With Heterogeneous Multi-Robot Teams

IEEE Access, 2021

This work is about mission planning in teams of mobile autonomous agents. We consider tasks that are spatially distributed, non-atomic, and provide an utility for integral and also partial task completion. Agents are heterogeneous, therefore showing different efficiency when dealing with the tasks. The goal is to define a system-level plan that assigns tasks to agents to maximize mission performance. We define the mission planning problem through a model including multiple sub-problems that are addressed jointly: task selection and allocation, task scheduling, task routing, control of agent proximity over time. The problem is proven to be NP-hard and is formalized as a mixed integer linear program (MILP). Two solution approaches are proposed: one heuristic and one exact method. Both combine a generic MILP solver and a genetic algorithm, resulting in efficient anytime algorithms. To support performance scalability and to allow the effective use of the model when online continual replanning is required, a decentralized and fully distributed architecture is defined top-down from the MILP model. Decentralization drastically reduces computational requirements and shows good scalability at the expenses of only moderate losses in performance. Lastly, we illustrate the application of the mission planning framework in two demonstrators. These implementations show how the framework can be successfully integrated with different platforms, including mobile robots (ground and aerial), wearable computers, and smart-phone devices.

Adaptive teams of autonomous aerial and ground robots for situational awareness

Journal of Field Robotics, 2007

In this paper, we report on the integration challenges of the various component technologies developed towards the establishment of a framework for deploying an adaptive system of heterogeneous robots for urban surveillance. In our integrated experiment and demonstration, aerial robots generate maps that are used to design navigation controllers and plan missions for the team. A team of ground robots constructs a radio signal strength map that is used as an aid for planning missions. Multiple robots establish a mobile, ad-hoc communication network that is aware of the radio signal strength between nodes and can adapt to changing conditions to maintain connectivity. Finally, the team of aerial and ground robots is able to monitor a small village, and search for and localize human targets by the color of the uniform, while ensuring that the information from the team is available to a remotely located human operator. The key component technologies and contributions include (a) mission specification and planning software; (b) exploration and mapping of radio signal strengths in an urban environment; (c) programming abstractions and composition of controllers for multi-robot deployment; (d) cooperative control strategies for search, identification, and localization of targets; and (e) three-dimensional mapping in an urban setting.

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.

Decision Making in Multi-UAVs Systems: Architecture and Algorithms

This chapter depicts an architecture that aims at designing a multi-UAV framework enabling cooperative operations in a system in which some UAVs are directly controlled by an operator, others are only endowed with operational autonomy, and others have decisional autonomy capacities. The architecture provides with the possibility to configure dynamically the decisional scheme, depending on the available robots and on the operational context. A taxonomy of robots decisional autonomy is introduced, and used as a foundation to state the proposed architecture. The various functionalities on-board each robot are organized among a repartition that exhibits on-board functional components, and on-board or on-ground generic executive and decision making processes. A set of algorithms that fulfill the three main decision-making functionalities required in a multi-robot system are then presented: a contract-net protocol that can handle task allocation for complex multi-UAV missions, a planning scheme based on a Hierarchical Task Networks planner completed with plan-refiners that consider the actual domain models, and an executive system that handles the coordination and task execution issues.

A Framework for Information Distribution, Task Execution and Decision Making in Multi-Robot Systems

IEICE Transactions on Information and Systems, 2010

Robotic systems operating in the real-world have to cope with unforeseen events by determining appropriate decisions based on noisy or partial knowledge. In this respect high functional robots are equipped with many sensors and actuators and run multiple processing modules in parallel. The resulting complexity is even further increased in case of cooperative multi-robot systems, since mechanisms for joint operation are needed. In this paper a complete and modular framework that can handle this complexity in multi-robot systems is presented. It provides efficient exchange of generated data as well as a generic scheme for task execution and robot coordination.

Distributed Heterogeneous Robot-Human Teams

2019

We introduce a novel, scalable, distributed decision-making algorithm using factor graphs and the sum product algorithm to control the coordination of a heterogeneousmulti-robot team in exploration tasks. In addition, our algorithm supports seamless participation of human operators at arbitrary levels of interaction. We present experimental results performed using both simulated and actual teams of unmanned aerial systems (UAS). Our experiments demonstrate effective exploration while facilitating human participation with the team. At the same time, we show how robots with differing capabilities coordinate their behaviors effectively to leverage each other’s individual strengths, without having to explicitly account for every possible joint behavior during system design. We demonstrate our algorithm’s suitability for tasks such as weather data collection using a heterogeneous robot team consisting of fixedand rotary-wing UAS. In particular, during 60 flight hours of real-world experi...

Decentralized prioritized planning in large multirobot teams

2010

Abstract In this paper, we address the problem of distributed path planning for large teams of hundreds of robots in constrained environments. We introduce two distributed prioritized planning algorithms: an efficient, complete method which is shown to converge to the centralized prioritized planner solution, and a sparse method in which robots discover collisions probabilistically.

Multi-robot Cooperation : Architectures and Paradigms

This paper presents a generic architecture for the operation of a team of autonomous robots to achieve complex missions. Its interest stems from its ability to provide a framework for cooperative decisional processes at different levels : high level plan synthesis, task allocation and task achievement. It is based on a combination of local individual planning and coordinated decision for incremental plan adaptation to the multi-robot context. Indeed, we claim that it is often possible (and useful) to treat these three issues separately. As we will see, this levels deal with problems of different nature, leading to specific representations, algorithms and protocols.

A Distributed Multi-Robot Cooperation Framework for Real Time Task Achievement

In this paper, we propose a general framework, DEMiR-CF, for a multi-robot team to achieve a complex mission including inter-related tasks that require diverse capabilities and/or simultaneous executions. Our framework integrates a distributed task allocation scheme, cooperation mechanisms and precaution routines for multi-robot team execution. Its performance has been demonstrated in Naval Mine Countermeasures, Multi-robot Multi-Target Exploration and Object Construction domains. The framework not only ensures near-optimal solutions for task achievement but also efficiently responds to real time contingencies.