Hierarchical task allocation in robotic exploration

Autonomous robotic exploration has long been a topic of interest in robotics research. Robotic exploration promises the ability to explore otherwise unreachable or hostile environments. Autonomous exploration is particularly useful in distant or hostile environments in which real-time communication with a human controller may not be practical, such as deep sea or planetary exploration. In order to more effectively explore a large unknown area, multiple robots may be employed to work cooperatively. While cooperation among multiple robots allows for increased exploration potential, it also entails significantly more complex planning. This complex planning involves allocation of exploration tasks to the robots participating in the exploration. Task allocation for multi-agent systems has applications in a wide variety of fields, but specifically in robotics, it makes a level of autonomy possible that is difficult to achieve otherwise. Task allocation has been approached in a variety of ways, depending largely on the nature of the tasks considered. Some problems present very specific tasks, allowing task allocation algorithms for them to be very domain-specific. This thesis presents an analysis of various task allocation approaches that have been taken specifically for autonomous robotic exploration, and will present a new hierarchical market based approach. This new approach provides agents with a mechanism to form coalitions and to divide a coalition into smaller coalitions. The formation of new coalitions from larger coalitions to pursue multiple avenues of exploration forms an implicit hierarchy of goals as they are discovered

A Distributed Control Architecture for Collaborative Multi-Robot Task Allocation

This thesis addresses the problem of task allocation for multi-robot systems that perform tasks with complex, hierarchical representations which contain different types of ordering constraints and multiple paths of execution. We propose a distributed multi-robot control architecture that addresses the above challenges and makes the following contributions: i) it allows for online, dynamic allocation of robots to various steps of the task, ii) it ensures that the collaborative robot system will obey all of the task constraints and iii) it allows for opportunistic, flexible task execution given different environmental conditions. This architecture uses a distributed messaging system to allow the robots to communicate. Each robot uses its own state and team member states to keep track of the progress on a given task and identify which sub-tasks to perform next using an activation spreading mechanism. We demonstrate the proposed architecture on a team of two humanoid robots (a Baxter and a PR2) performing hierarchical tasks

Hedonic Coalition Formation for Task Allocation with Heterogeneous Robots

Tasks in the real world are complex in nature and often require multiple robots to collaborate in order to be accomplished. However, multiple robots with the same set of sensors working together might not be the optimal solution. In many cases a task might require different sensory inputs and outputs. However, allocating a large variety of sensors on each robot is not a cost-effective solution. As such, robots with different attributes must be considered. In this thesis we study the coalition formation problem for task allocation with multiple heterogeneous (equipped with a different set of sensors) robots. The proposed solution is implemented utilizing a Hedonic Coalition Formation strategy, rooted in game theory, coupled with bipartite graph matching. Our proposed algorithm aims to minimize the total cost of the formed coalitions and to maximize the matching between the required and the allocated types of robots to the tasks. Simulation results show that it produces near-optimal solutions (up to 94%) in a negligible amount of time (0:19 ms. with 100 robots and 10 tasks)

Initial Task Allocation for Multi-Human Multi-Robot Teams with Attention-based Deep Reinforcement Learning

Multi-human multi-robot teams have great potential for complex and large-scale tasks through the collaboration of humans and robots with diverse capabilities and expertise. To efficiently operate such highly heterogeneous teams and maximize team performance timely, sophisticated initial task allocation strategies that consider individual differences across team members and tasks are required. While existing works have shown promising results in reallocating tasks based on agent state and performance, the neglect of the inherent heterogeneity of the team hinders their effectiveness in realistic scenarios. In this paper, we present a novel formulation of the initial task allocation problem in multi-human multi-robot teams as contextual multi-attribute decision-make process and propose an attention-based deep reinforcement learning approach. We introduce a cross-attribute attention module to encode the latent and complex dependencies of multiple attributes in the state representation. We conduct a case study in a massive threat surveillance scenario and demonstrate the strengths of our model.Comment: Accepted to IROS202

Human-Robot Trust Integrated Task Allocation and Symbolic Motion planning for Heterogeneous Multi-robot Systems

This paper presents a human-robot trust integrated task allocation and motion planning framework for multi-robot systems (MRS) in performing a set of tasks concurrently. A set of task specifications in parallel are conjuncted with MRS to synthesize a task allocation automaton. Each transition of the task allocation automaton is associated with the total trust value of human in corresponding robots. Here, the human-robot trust model is constructed with a dynamic Bayesian network (DBN) by considering individual robot performance, safety coefficient, human cognitive workload and overall evaluation of task allocation. Hence, a task allocation path with maximum encoded human-robot trust can be searched based on the current trust value of each robot in the task allocation automaton. Symbolic motion planning (SMP) is implemented for each robot after they obtain the sequence of actions. The task allocation path can be intermittently updated with this DBN based trust model. The overall strategy is demonstrated by a simulation with 5 robots and 3 parallel subtask automata

Task Allocation and Dead-Lock-Free Trajectory Planning for Collaborative Multi-Robot System

With advances in technology, robots have become an integral part of industries across the board. They are being employed in all sorts of applications from simple pick and place operations to the more complex operations involving AI with computer vision. In the manufacturing sector, robots are mostly used to perform tasks in isolation. There lies a big opportunity for efficiency improvement by having robots collaborate on tasks. This brings along with it challenges of obstacle avoidance, task allocation, and deadlocks. These challenges are easier to tackle for non-varying tasks such as a multi-robot system (MRS) used for spray painting a specific part. But when the same MRS has to be used for a number of tasks such as spray-painting a wide variety of parts, each with its own requirements, the challenges become harder to solve. The goal of this research is to advance task allocation and trajectory planning for multiple robot agents working collaboratively to perform manufacturing tasks. These industrial robots can vary from simple gantry robots to industrial robot arms mounted on mobile bases. Their applications will involve low-volume, high-mix manufacturing tasks such as spray painting, pressure washing, 3D printing, media blasting, and sanding. Apart from dealing with the generation of an offline collision-free path, manufacturing constraints must be considered as well. These involve achieving a constant speed of end-effector throughout a trajectory to avoid any undesirable effects. This research focuses on developing a technique for several robots with 3 or more revolute and/or prismatic joints with partially shared workspaces that enables them to allocate and perform manufacturing tasks in a time-effective and computationally efficient manner.https://ecommons.udayton.edu/stander_posters/3890/thumbnail.jp

Analysis of Dynamic Task Allocation in Multi-Robot Systems

Dynamic task allocation is an essential requirement for multi-robot systems operating in unknown dynamic environments. It allows robots to change their behavior in response to environmental changes or actions of other robots in order to improve overall system performance. Emergent coordination algorithms for task allocation that use only local sensing and no direct communication between robots are attractive because they are robust and scalable. However, a lack of formal analysis tools makes emergent coordination algorithms difficult to design. In this paper we present a mathematical model of a general dynamic task allocation mechanism. Robots using this mechanism have to choose between two types of task, and the goal is to achieve a desired task division in the absence of explicit communication and global knowledge. Robots estimate the state of the environment from repeated local observations and decide which task to choose based on these observations. We model the robots and observations as stochastic processes and study the dynamics of the collective behavior. Specifically, we analyze the effect that the number of observations and the choice of the decision function have on the performance of the system. The mathematical models are validated in a multi-robot multi-foraging scenario. The model's predictions agree very closely with experimental results from sensor-based simulations.Comment: Preprint version of the paper published in International Journal of Robotics, March 2006, Volume 25, pp. 225-24