A Decentralized Strategy for Swarm Robots to Manage Spatially Distributed Tasks

Large-scale scenarios such as search-and-rescue operations, agriculture, warehouse, surveillance, and construction consist of multiple tasks to be performed at the same time. These tasks have non-trivial spatial distributions. Robot swarms are envisioned to be efficient, robust, and flexible for such applications. We model this system such that each robot can service a single task at a time; each task requires a specific number of robots, which we refer to as \u27quota\u27; task allocation is instantaneous; and tasks do not have inter- dependencies. This work focuses on distributing robots to spatially distributed tasks of known quotas in an efficient manner. Centralized solutions which guarantee optimality in terms of distance travelled by the swarm exist. Although potentially scalable, they require non-trivial coordination; could be computationally expensive; and may have poor response time when the number of robots, tasks and task quotas increase. For a swarm to efficiently complete tasks with a short response time, a decentralized approach provides better parallelism and scalability than a centralized one. In this work, we study the performance of a weight-based approach which is enhanced to include spatial aspects. In our approach, the robots share a common table that reports the task locations and quotas. Each robot, according to its relative position with respect to task locations, modifies weights for each task and randomly chooses a task to serve. Weights increase for tasks that are closer and have high quota as opposed to tasks which are far away and have low quota. Tasks with higher weights have a higher probability of being selected. This results in each robot having its own set of weights for all tasks. We introduce a distance- bias parameter, which determines how sensitive the system is to relative robot-task locations over task quotas. We focus on evaluating the distance covered by the swarm, number of inter- task switches, and time required to completely allocate all tasks and study the performance of our approach in several sets of simulated experiments

Multi-robot Task Allocation using Agglomerative Clustering

The main objective of this thesis is to solve the problem of balancing tasks in the Multi-robot Task Allocation problem domain. When allocating a large number of tasks to a multi-robot system, it is important to balance the load effectively across the robots in the system. In this thesis an algorithm is proposed in which tasks are allocated through clustering, investigating the effectiveness of agglomerative hierarchical clustering as compared to K-means clustering. Once the tasks are clustered, each agent claims a cluster through a greedy self-assignment. This thesis investigates the performance both when all tasks are known ahead of time as well as when new tasks are injected into the system periodically. To account for new tasks, both global re-clustering and greedy clustering methods are considered. Three metrics: 1) total travel cost, 2) maximum distance traveled per robot, and 3) balancing cost index are used to compare the performance of the overall system in environments both with and without obstacles. The results collected from the experiments show that agglomerative hierarchical clustering is deterministic and better at minimizing the total travel cost, especially for large numbers of agents, whereas K-means works better to balance costs. In addition to this, the greedy approach for clustering new tasks works better for frequently appearing tasks than infrequent ones

Distributed and Centralized Task Allocation: When and Where to Use Them

Self-organisation is frequently advocated as the solution for managing large, dynamic systems. Distributed algorithms are implicitly designed for infinitely large problems, while small systems are regarded as being controllable using traditional, centralised approaches. Many real-world systems, however, do not fit conveniently into these "small" or "large" categories, resulting in a range of cases where the optimal solution is ambiguous. This difficulty is exacerbated by enthusiasts of either approach constructing problems that suit their preferred control architecture. We address this ambiguity by building an abstract model of task allocation in a community of specialised agents. We are inspired by the problem of work distribution in distributed satellite systems, but the model is also relevant to the resource allocation problems in distributed robotics, autonomic computing and wireless sensor networks. We compare the behaviour of a self-organising, market-based task allocation strategy to a classical approach that uses a central controller with global knowledge. The objective is not to prove one mechanism inherently superior to the other; instead we are interested in the regions of problem space where each of them dominates. Simulation is used to explore the trade-off between energy consumption and robustness in a system of intermediate size, with fixed communication costs and varying rates of component failure. We identify boundaries between regions in the parameter space where one or the other architecture will be favoured. This allows us to derive guidelines for system designers, thus contributing to the development of a disciplined approach to controlling distributed systems using self-organising mechanisms

Decentralised Coordination in RoboCup Rescue

Emergency responders are faced with a number of significant challenges when managing major disasters. First, the number of rescue tasks posed is usually larger than the number of responders (or agents) and the resources available to them. Second, each task is likely to require a different level of effort in order to be completed by its deadline. Third, new tasks may continually appear or disappear from the environment, thus requiring the responders to quickly recompute their allocation of resources. Fourth, forming teams or coalitions of multiple agents from different agencies is vital since no single agency will have all the resources needed to save victims, unblock roads, and extinguish the ?res which might erupt in the disaster space. Given this, coalitions have to be efficiently selected and scheduled to work across the disaster space so as to maximise the number of lives and the portion of the infrastructure saved. In particular, it is important that the selection of such coalitions should be performed in a decentralised fashion in order to avoid a single point of failure in the system. Moreover, it is critical that responders communicate only locally given they are likely to have limited battery power or minimal access to long range communication devices. Against this background, we provide a novel decentralised solution to the coalition formation process that pervades disaster management. More specifically, we model the emergency management scenario defined in the RoboCup Rescue disaster simulation platform as a Coalition Formation with Spatial and Temporal constraints (CFST) problem where agents form coalitions in order to complete tasks, each with different demands. In order to design a decentralised algorithm for CFST we formulate it as a Distributed Constraint Optimisation problem and show how to solve it using the state-of-the-art Max-Sum algorithm that provides a completely decentralised message-passing solution. We then provide a novel algorithm (F-Max-Sum) that avoids sending redundant messages and efficiently adapts to changes in the environment. In empirical evaluations, our algorithm is shown to generate better solutions than other decentralised algorithms used for this problem

Multi-Agent Task Allocation for Robot Soccer

This is the published version. Copyright De GruyterThis paper models and analyzes task allocation methodologies for multiagent systems. The evaluation process was implemented as a collection of simulated soccer matches. A soccer-simulation software package was used as the test-bed as it provided the necessary features for implementing and testing the methodologies. The methodologies were tested through competitions with a number of available soccer strategies. Soccer game scores, communication, robustness, fault-tolerance, and replanning capabilities were the parameters used as the evaluation criteria for the mul1i-agent systems

Multi-Robot Task Allocation and Scheduling with Spatio-Temporal and Energy Constraints

Autonomy in multi-robot systems is bounded by coordination among its agents. Coordination implies simultaneous task decomposition, task allocation, team formation, task scheduling and routing; collectively termed as task planning. In many real-world applications of multi-robot systems such as commercial cleaning, delivery systems, warehousing and inventory management: spatial & temporal constraints, variable execution time, and energy limitations need to be integrated into the planning module. Spatial constraints comprise of the location of the tasks, their reachability, and the structure of the environment; temporal constraints express task completion deadlines. There has been significant research in multi-robot task allocation involving spatio-temporal constraints. However, limited attention has been paid to combine them with team formation and non- instantaneous task execution time. We achieve team formation by including quota constraints which ensure to schedule the number of robots required to perform the task. We introduce and integrate task activation (time) windows with the team effort of multiple robots in performing tasks for a given duration. Additionally, while visiting tasks in space, energy budget affects the robots operation time. We map energy depletion as a function of time to ensure long-term operation by periodically visiting recharging stations. Research on task planning approaches which combines all these conditions is still lacking. In this thesis, we propose two variants of Team Orienteering Problem with task activation windows and limited energy budget to formulate the simultaneous task allocation and scheduling as an optimization problem. A complete mixed integer linear programming (MILP) formulation for both variants is presented in this work, implemented using Gurobi Optimizer and analyzed for scalability. This work compares the different objectives of the formulation like maximizing the number of tasks visited, minimizing the total distance travelled, and/or maximizing the reward, to suit various applications. Finally, analysis of optimal solutions discover trends in task selection based on the travel cost, task completion rewards, robot\u27s energy level, and the time left to task inactivation

Scalable Control Strategies and a Customizable Swarm Robotic Platform for Boundary Coverage and Collective Transport Tasks

abstract: Swarms of low-cost, autonomous robots can potentially be used to collectively perform tasks over large domains and long time scales. The design of decentralized, scalable swarm control strategies will enable the development of robotic systems that can execute such tasks with a high degree of parallelism and redundancy, enabling effective operation even in the presence of unknown environmental factors and individual robot failures. Social insect colonies provide a rich source of inspiration for these types of control approaches, since they can perform complex collective tasks under a range of conditions. To validate swarm robotic control strategies, experimental testbeds with large numbers of robots are required; however, existing low-cost robots are specialized and can lack the necessary sensing, navigation, control, and manipulation capabilities. To address these challenges, this thesis presents a formal approach to designing biologically-inspired swarm control strategies for spatially-confined coverage and payload transport tasks, as well as a novel low-cost, customizable robotic platform for testing swarm control approaches. Stochastic control strategies are developed that provably allocate a swarm of robots around the boundaries of multiple regions of interest or payloads to be transported. These strategies account for spatially-dependent effects on the robots' physical distribution and are largely robust to environmental variations. In addition, a control approach based on reinforcement learning is presented for collective payload towing that accommodates robots with heterogeneous maximum speeds. For both types of collective transport tasks, rigorous approaches are developed to identify and translate observed group retrieval behaviors in Novomessor cockerelli ants to swarm robotic control strategies. These strategies can replicate features of ant transport and inherit its properties of robustness to different environments and to varying team compositions. The approaches incorporate dynamical models of the swarm that are amenable to analysis and control techniques, and therefore provide theoretical guarantees on the system's performance. Implementation of these strategies on robotic swarms offers a way for biologists to test hypotheses about the individual-level mechanisms that drive collective behaviors. Finally, this thesis describes Pheeno, a new swarm robotic platform with a three degree-of-freedom manipulator arm, and describes its use in validating a variety of swarm control strategies.Dissertation/ThesisDoctoral Dissertation Mechanical Engineering 201