Multi-objective Optimisation of Multi-robot Task Allocation with Precedence Constraints

Efficacy of the multi-robot systems depends on proper sequencing and optimal allocation of robots to the tasks. Focuses on deciding the optimal allocation of set-of-robots to a set-of-tasks with precedence constraints considering multiple objectives. Taguchi’s design of experiments based parameter tuned genetic algorithm (GA) is developed for generalised task allocation of single-task robots to multi-robot tasks. The developed methodology is tested for 16 scenarios by varying the number of robots and number of tasks. The scenarios were tested in a simulated environment with a maximum of 20 robots and 40 multi-robot foraging tasks. The tradeoff between performance measures for the allocations obtained through GA for different task levels was used to decide the optimal number of robots. It is evident that the tradeoffs occur at 20 per cent of performance measures and the optimal number of robot varies between 10 and 15 for almost all the task levels. This method shows good convergence and found that the precedence constraints affect the optimal number of robots required for a particular task level

The applicability of the 3C model for understanding the use of technology in emergency management scenarios

The nature of emergency is intensive, imposing challenges related to the way coagencies collaborate. The 3C model consists of the combination of three elements, namely communication, coordination, and cooperation, connecting in a cycle, illustrating the nature of collaborative work for accomplishing certain tasks. Very few studies considered the use of the 3C model for improving collaboration in domains other than emergency management. This paper presents a scoping review of the literature in the domain of emergency management, focusing on how the 3C model can help us understand the use of technology for improving collaboration. The paper identifies the commonalities between the elements of the 3C model for improving our understanding of collaboration in emergency management scenarios, and indicating the inter-relationships among the elements of the 3C model that are applicable for understanding the topology of technology use in emergency management

Coordination for dynamic weighted task allocation in disaster environments with time, space and communication constraints

Coordination for dynamic task allocation based on available resources is a very challenging research issue in disaster environments with time, space and communication constraints. In addition, the space and communication constraints and the dynamic features of disaster environments make an extra difficulty to achieve efficient coordination through centralised coordination approaches, which require the coordinators to have global knowledge of the environments. To this end, a coordination approach for dynamic weighted task allocation is proposed in this paper. The proposed approach considers time, space and communication constraints in disaster environments and urgent degrees of workloads of tasks without requiring the global knowledge of the environment. In particular, a dynamic group formation mechanism is developed to help agents to form groups and share information for task allocation under space and communication constraints in a decentralised manner, which can reflect real-life situations in disaster environments. The efficient coordination for task allocation is achieved through the utility calculation within each group. The experimental results show that the proposed approach outperforms most of other coordination approaches, such as the group formation approach proposed by Glinton et al. and the heuristics task allocation approach proposed by Ramchurn et al. in terms of group formation and weighted task allocation in disaster environments with time, space and communication constraints

Dynamic Task Allocation in Partially Defined Environments Using A* with Bounded Costs

The sector of maritime robotics has seen a boom in operations in areas such as surveying and mapping, clean-up, inspections, search and rescue, law enforcement, and national defense. As this sector has continued to grow, there has been an increased need for single unmanned systems to be able to undertake more complex and greater numbers of tasks. As the maritime domain can be particularly difficult for autonomous vehicles to operate in due to the partially defined nature of the environment, it is crucial that a method exists which is capable of dynamically accomplishing tasks within this operational domain. By considering the task allocation problem as a graph search problem, Minion Task, is not only capable of finding and executing tasks, but is also capable of optimizing costs across a range of parameters and of considering constraints on the order that tasks may be completed in. Minion task consists of four key phases that allow it to accomplish dynamic tasking in partially defined environments. These phases are a search space updater that is capable of evaluating the regions the vehicle has effectively perceived, a task evaluator that is capable of ascertaining which tasks in the mission set need to be searched for and which can be executed, a task allocation process that utilizes a modified version of the A* with Bounded Costs (ABC) algorithm to select the best ordering of task for execution based on an optimization routing, and, finally, a task executor that handles transiting to and executing tasks orders received from the task allocator. To evaluate Minion Task’s performance, the modified ABC algorithm used by the task allocator was compared to a greedy and a random allocation scheme. Additionally, to show the full capabilities of the system, a partial simulation of the 2018 Maritime RobotX competition was utilized to evaluate the performance of the Minion Task algorithm. Comparing the modified ABC algorithm to the greedy and random allocation algorithms, the ABC method was found to always achieve a score that was as good, if not better than the scores of the greedy and random allocation schemes. At best, ABC could achieve an up to 2 times improvement in the score achieved compared to the other two methods when the ranges for the score and execution times for each tasks in the task set as well as the space where these tasks could exists was sufficiently large. Finally, using two scenarios, it was shown that Minion Task was capable of completing missions in a dynamic environment. The first scenario showed that Minion Task was capable of handling dynamic switching between searching for and executing tasks. The second scenario showed the algorithm was capable of handling constraints on the ordering of the tasks despite the environment and arrangement of tasks not changing otherwise. This paper succeeded in proving a method, Minion Task, that is capable of performing missions in dynamic maritime environments