Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting time constraints. The novel idea is to enable an online reconfiguration of task allocation among distributed and networked vehicles. The proposed strategy reallocates tasks among vehicles to create feasible spaces for unallocated tasks, thereby optimizing the total number of allocated tasks. The algorithm is shown to be efficient with respect to previous methods because changes are made to a task list only once a suitable space in a schedule has been identified. Furthermore, the proposed TSA can be employed as an extension for other distributed task allocation algorithms with similar constraints to improve performance by escaping local optima and by reacting to dynamic environments

Increasing allocated tasks with a time minimization algorithm for a search and rescue scenario

Rescue missions require both speed to meet strict time constraints and maximum use of resources. This study presents a Task Swap Allocation (TSA) algorithm that increases vehicle allocation with respect to the state-of-the-art consensus-based bundle algorithm and one of its extensions, while meeting time constraints. The novel idea is to enable an online reconfiguration of task allocation among distributed and networked vehicles. The proposed strategy reallocates tasks among vehicles to create feasible spaces for unallocated tasks, thereby optimizing the total number of allocated tasks. The algorithm is shown to be efficient with respect to previous methods because changes are made to a task list only once a suitable space in a schedule has been identified. Furthermore, the proposed TSA can be employed as an extension for other distributed task allocation algorithms with similar constraints to improve performance by escaping local optima and by reacting to dynamic environments

A robust, distributed task allocation algorithm for time-critical, multi agent systems operating in uncertain environments

The aim of this work is to produce and test a robust, distributed, multi-agent task allocation algorithm, as these are scarce and not well-documented in the literature. The vehicle used to create the robust system is the Performance Impact algorithm (PI), as it has previously shown good performance. Three different variants of PI are designed to improve its robustness, each using Monte Carlo sampling to approximate Gaussian distributions. Variant A uses the expected value of the task completion times, variant B uses the worst-case scenario metric and variant C is a hybrid that implements a combination of these. The paper shows that, in simulated trials, baseline PI does not han-dle uncertainty well; the task-allocation success rate tends to decrease linear-ly as degree of uncertainty increases. Variant B demonstrates a worse per-formance and variant A improves the failure rate only slightly. However, in comparison, the hybrid variant C exhibits a very low failure rate, even under high uncertainty. Furthermore, it demonstrates a significantly better mean ob-jective function value than the baseline.EPSR

Distributed autonomous GIS to form teams for public safety

Public safety requires emergency response that is timely and effi-cient. This paper describes how to distribute the emergency call among those in the area so as to optimize their locations when called, and their expertise. The call will route to the next most qualified if those in the immediate vicinity cannot come. We de-scribe the system architecture and provide the algorithmic rules, as well as sketch an interface and propose how the completed system could be evaluated

Fast consensus for fully distributed multi-agent task allocation

In distributed multi-agent task allocation problems, the time to find a solution and a guarantee of reaching a solution, i.e. an execution plan, is critical to ensure a fast response. The problem is made more difficult by time constraints on tasks and on agents, which may prevent some tasks from being executed. This paper proposes a new distributed consensus-based task allocation algorithm that reduces convergence time with respect to previous methods, i.e. the time required for the network of agents to agree on a task allocation, while maximising the number of allocated tasks. The novel idea is to reduce the time to reach consensus among agents by using a hierarchy or rank-based conflict resolution among agents. Unlike other existing algorithms, this method enables different agents to construct their task schedules using any insertion heuristic, and still guarantee convergence. Simulation results demonstrate that the proposed approach can allocate a greater number of tasks in a shorter time than an established baseline method. Additionally, the analysis delineates dependencies between optimal insertion strategies and number of tasks per agent, providing insights for further optimisation strategies

Distributed task allocation optimisation techniques in multi-agent systems

A multi-agent system consists of a number of agents, which may include software agents, robots, or even humans, in some application environment. Multi-robot systems are increasingly being employed to complete jobs and missions in various fields including search and rescue, space and underwater exploration, support in healthcare facilities, surveillance and target tracking, product manufacturing, pick-up and delivery, and logistics. Multi-agent task allocation is a complex problem compounded by various constraints such as deadlines, agent capabilities, and communication delays. In high-stake real-time environments, such as rescue missions, it is difficult to predict in advance what the requirements of the mission will be, what resources will be available, and how to optimally employ such resources. Yet, a fast response and speedy execution are critical to the outcome. This thesis proposes distributed optimisation techniques to tackle the following questions: how to maximise the number of assigned tasks in time restricted environments with limited resources; how to reach consensus on an execution plan across many agents, within a reasonable time-frame; and how to maintain robustness and optimality when factors change, e.g. the number of agents changes. Three novel approaches are proposed to address each of these questions. A novel algorithm is proposed to reassign tasks and free resources that allow the completion of more tasks. The introduction of a rank-based system for conflict resolution is shown to reduce the time for the agents to reach consensus while maintaining equal number of allocations. Finally, this thesis proposes an adaptive data-driven algorithm to learn optimal strategies from experience in different scenarios, and to enable individual agents to adapt their strategy during execution. A simulated rescue scenario is used to demonstrate the performance of the proposed methods compared with existing baseline methods