Task allocation and motion coordination of multiple autonomous vehicles - with application in automated container terminals

University of Technology, Sydney. Faculty of Engineering and Information Technology.This thesis focuses on developing an approach to solve the complex problem of task allocation and motion coordination simultaneously for a large fleet of autonomous vehicles in highly constrained operational environments. The multi-vehicle task allocation and motion coordination problem consists of allocating different tasks to different autonomous vehicles and intelligently coordinating motions of the vehicles without human interaction. The motion coordination itself comprises two sub-problems: path planning and collision / deadlock avoidance. Although a number of research studies have attempted to solve one or two aspects of this problem, it is rare to note that many have attempted to solve the task allocation, path planning and collision avoidance simultaneously. Therefore, it cannot be conclusively said that, optimal or near-optimal solutions generated based on one aspect of the problem will be optimal or near optimal results for the whole problem. It is advisable to solve the problem as one complete problem rather than decomposing it. This thesis intends to solve the complex task allocation, path planning and collision avoidance problem simultaneously. A Simultaneous Task Allocation and Motion Coordination (STAMC) approach is developed to solve the multi-vehicle task allocation and motion coordination problem in a concurrent manner. Further, a novel algorithm called Simultaneous Path and Motion Planning (SiPaMoP) is proposed for collision free motion coordination. The main objective of this algorithm is to generate collision free paths for autonomous vehicles, once they are assigned with tasks in a conventional path topology of a material handling environment. The Dijkstra and A * shortest path search algorithms are utilised in the proposed Simultaneous Path and Motion Planning algorithm. The multi-vehicle task allocation and motion coordination problem is first studied in a static environment where all the tasks, vehicles and operating environment information are assumed to be known. The multi-vehicle task allocation and motion coordination problem in a dynamic environment, where tasks, vehicles and operating environment change with time is then investigated. Furthermore, issues like vehicle breakdowns, which are common in real world situations, are considered. The computational cost of solving the multi-vehicle STAMC problem is also addressed by proposing a distributed computational architecture and implementing that architecture in a cluster computing system. Finally, the proposed algorithms are tested in a case study in an automated container terminal environment with a large fleet of autonomous straddle carriers. Since the multi-vehicle task allocation and motion coordination is an NP-hard problem, it is almost impossible to find out the optimal solutions within a reasonable time frame. Therefore, this research focuses on investigating the appropriateness of heuristic and evolutionary algorithms for solving the STAMC problem. The Simulated Annealing algorithm, Ant Colony and Auction algorithms have been investigated. Commonly used dispatching rules such as first come first served, and closest task first have also been applied for comparison. Simulation tests of the proposed approach is conducted based on information from the Fishermen Island's container terminal of Patrick Corporation (Pty.) Ltd in Queensland, Australia where a large fleet of autonomous straddle carriers operate. The results shows that the proposed meta-heuristic techniques based simultaneous task allocation and motion coordination approach can effectively solve the complex multi-vehicle task allocation and motion coordination problem and it is capable of generating near optimal results within an acceptable time frame

Sequential Single-Cluster Auctions for Multi-Robot Task Allocation

This thesis studies task allocation in multi-robot teams operating in dynamic environments. The multi-robot task allocation problem is a complex NP-Complete optimisation problem with globally optimal solutions often difficult to find. Because of this, the rapid generation of near optimal solutions to the problem that minimise task execution time and/or energy used by robots is highly desired. Our approach seeks to cluster together closely related tasks and then builds on existing distributed market-based auction architectures for distributing these sets of tasks among several autonomous robots. Dynamic environments introduce many challenges that are not found in closed systems. For instance, it is common for additional tasks to be inserted into a system after an initial solution to the task allocation problem is determined. Additionally, it is highly likely in long-term autonomous systems that individual robots may suffer some form of failure. The ability to alter plans to react to these types of challenges in a dynamic environment is required for the completion of all tasks. In our approach we allow the repeated formation and auctioning of task clusters with varying tasks. This allows us to react to and change the task allocation among robots during execution. Throughout this thesis we use empirical evaluation to study different approaches for forming clusters of tasks and the application of task clustering to distributed auctions for multi-robot task allocation problems. Our results show that allocating clusters of tasks to robots in solving these types of problems is a fast and effective method and produces near optimal solutions

Power Load Management as a Computational Market

Power load management enables energy utilities to reduce peak loads and thereby save money. Due to the large number of different loads, power load management is a complicated optimization problem. We present a new decentralized approach to this problem by modeling direct load management as a computational market. Our simulation results demonstrate that our approach is very efficient with a superlinear rate of convergence to equilibrium and an excellent scalability, requiring few iterations even when the number of agents is in the order of one thousand. Aframework for analysis of this and similar problems is given which shows how nonlinear optimization and numerical mathematics can be exploited to characterize, compare, and tailor problem-solving strategies in market-oriented programming

Decentralized dynamic task allocation for UAVs with limited communication range

We present the Limited-range Online Routing Problem (LORP), which involves a team of Unmanned Aerial Vehicles (UAVs) with limited communication range that must autonomously coordinate to service task requests. We first show a general approach to cast this dynamic problem as a sequence of decentralized task allocation problems. Then we present two solutions both based on modeling the allocation task as a Markov Random Field to subsequently assess decisions by means of the decentralized Max-Sum algorithm. Our first solution assumes independence between requests, whereas our second solution also considers the UAVs' workloads. A thorough empirical evaluation shows that our workload-based solution consistently outperforms current state-of-the-art methods in a wide range of scenarios, lowering the average service time up to 16%. In the best-case scenario there is no gap between our decentralized solution and centralized techniques. In the worst-case scenario we manage to reduce by 25% the gap between current decentralized and centralized techniques. Thus, our solution becomes the method of choice for our problem

Market-Based Task Allocation Mechanisms for Limited Capacity Suppliers

This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers' uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns

Multi-Robot Coordination : Applications in Orchard Bin Management and Informative Path Planning

Efficient coordination is desired for multi-robot systems in many scenarios. In this research, we first provide a multi-robot system to help human workers during tree fruit harvest. We present an auction-based method to coordinate a team of self-propelled bin carriers to retrieve fruit bins. Second, we propose a more general information gathering problem in a dynamic environment. In this problem, locations of points of interest change over time. Further, the amount of meaningful information or reward that can be obtained from each point is limited. We propose to use a distributed sampling algorithm for task allocation, and a receding horizon strategy for path planning in this problem. To evaluate its performance, the proposed algorithm is compared to a baseline algorithm that implements sequential auction for task allocation with greedy path planning. Experimental results suggest that the proposed algorithm is more suitable for solving the aforementioned information gathering problem. Finally we present an effective approach to coordinating a team of UAVs (unmanned aerial vehicles) to simultaneous explore, map, and search in unknown environments. The UAVs can perform a weighted trade off between the three sub-tasks. Moreover, human operators can limit the time allowed for each UAV to remain without a valid communication link to the control base station. We compare results to a market-based baseline algorithm. Results suggest that our relay system improves the efficiency of exploring, mapping, and searching tasks

Distributed Task Allocation and Task Sequencing for Robots with Motion Constraints

This thesis considers two routing and scheduling problems. The first problem is task allocation and sequencing for multiple robots with differential motion constraints. Each task is defined as visiting a point in a subset of the robot configuration space -- this definition captures a variety of tasks including inspection and servicing, as well as one-in-a-set tasks. Our approach is to transform the problem into a multi-vehicle generalized traveling salesman problem (GTSP). We analyze the GTSP insertion methods presented in literature and we provide bounds on the performance of the three insertion mechanisms. We then develop a combinatorial-auction-based distributed implementation of the allocation and sequencing algorithm. The number of the bids in a combinatorial auction, a crucial factor in the runtime, is shown to be linear in the size of the tasks. Finally, we present extensive benchmarking results to demonstrate the improvement over existing distributed task allocation methods. In the second part of this thesis, we address the problem of computing optimal paths through three consecutive points for the curvature-constrained forward moving Dubins vehicle. Given initial and final configurations of the Dubins vehicle and a midpoint with an unconstrained heading, the objective is to compute the midpoint heading that minimizes the total Dubins path length. We provide a novel geometrical analysis of the optimal path and establish new properties of the optimal Dubins' path through three points. We then show how our method can be used to quickly refine Dubins TSP tours produced using state-of-the-art techniques. We also provide extensive simulation results showing the improvement of the proposed approach in both runtime and solution quality over the conventional method of uniform discretization of the heading at the mid-point, followed by solving the minimum Dubins path for each discrete heading