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

Distributed simultaneous task allocation and motion coordination of autonomous vehicles using a parallel computing cluster

Task allocation and motion coordination are the main factors that should be consi-dered in the coordination of multiple autonomous vehicles in material handling systems. Presently, these factors are handled in different stages, leading to a reduction in optimality and efficiency of the overall coordination. However, if these issues are solved simultaneously we can gain near optimal results. But, the simultaneous approach contains additional algorithmic complexities which increase computation time in the simulation environment. This work aims to reduce the computation time by adopting a parallel and distributed computation strategy for Simultaneous Task Allocation and Motion Coordination (STAMC). In the simulation experiments, each cluster node executes the motion coordination algorithm for each autonomous vehicle. This arrangement enables parallel computation of the expensive STAMC algorithm. Parallel and distributed computation is performed directly within the interpretive MATLAB environment. Results show the parallel and distributed approach provides sub-linear speedup compared to a single centralised computing node. © 2007 Springer-Verlag Berlin Heidelberg

Motion coordination of multiple autonomous vehicles in dynamic and strictly constrained environments

With the increasing applications of fully autonomous vehicles, efficient motion coordination of multi-autonomous vehicles becomes a very important problem as it significantly affects the productivity. This problem is even harder to solve with the increases of the number of autonomous vehicles employed in a dynamic changing environment and constraints to vehicle movement. This paper presents a simultaneous path and motion planning (SiPaMoP) approach to coordinate motions of multi-autonomous vehicles in dynamic and strictly constrained environments. This approach integrates the path planning, collision avoidance and motion planning into a comprehensive model, which has so far not attracted a lot of attention in the academic literature, and optimizes vehicles' path and speed to minimize the completion time of a set of tasks. Simulation results demonstrated that this approach can effectively coordinate the motion of a team of vehicles, and solve the problems of traffic congestion and collision under various traffic conditions. © 2006 IEEE

Ant colony optimization based simultaneous task allocation and path planning of autonomous vehicles

This paper applies a meta-heuristic based Ant Colony Optimization (ACO) technique for simultaneous task allocation and path planning of Automated Guided Vehicles (AGV) in material handling. ACO algorithm allocates tasks to AGVs based on collision free path obtained by a proposed path and motion planning algorithm. The validity of this approach is investigated by applying it to different task and AGV combinations which have different initial settings. For small combinations, i.e. small number of tasks and vehicles, the quality of the ACO solution is compared against the optimal results obtained from exhaustive search mechanism. This approach has shown near optimal results. For larger combinations, ACO solutions are compared with Simulated Annealing algorithm which is another commonly used meta-heuristic approach. The results show that ACO solutions have slightly better performance than that of Simulated Annealing algorithm. © 2006 IEEE

Railway based container transportation to greening supply chains: a case study in Sri Lanka

The scale of export and importation has been rapidly increased in Sri Lanka recent past. Large percentage of this fall into local manufacturing industry which are scattered around number of export processing zones around the island. Due to road based container transportation, most of the highways experiencing traffic congestions especially around main cities and in Colombo city where the only container handling port is located. Not only the traffic congestion contribute CO2 emissions but also the large number of trucks which transport containers are the major contributory factor of transportation related emissions. However, there is dearth of research on these issues in local context especially on the alternative mode of container transportation. Conversely, local railway network is connecting commercial hub with eastern, southern, northern and central regions of the island. Further, Sri Lanka Railways currently upgrades the track conditions to run high speed trains and rehabilitant northern line after the war. Therefore, this research focuses on to investigate the feasibility of adapting railways as a mode of container transportation in order to reduce CO2 emissions during the container transportation. The inward and outward bound containers to the export processing zones from/to Colombo harbour are considered for this research. The in and out bound containers to the 11 export processing zones over the last year were analyzed to investigate the feasibility of converting transportation to railways since there should be reasonable number of containers necessary to run a freight train in economical manner. IN addition to the emission comparisons cost benefit analysis also carried out in this research. The results revealed that 4 out of 11 export processing zones have necessary quantities of containers which can be transported by railway mode. The level of direct emission reduction out of the transportation is significant and based on indirect factors such as traffic congestions, this value increases further. The baseline information and comparison were carried out according to IPCC guidelines