Optimisation of Off-Road Transport Missions

Mines, construction sites, road construction and quarries are examples of applications where construction equipment are used. In a production chain consisting of several construction machines working together, the work needs to be optimised and coordinated to achieve an environmental friendly, energy efficient and productive production. Recent rapid development within positioning services, telematics and human machine interfaces (HMI) opens up for control of individual machines and optimisation of transport missions where several construction machines co-operate. The production chain on a work site can be split up in different sub-tasks of which some can be transport missions. Taking off in a transport mission where one wheel loader ("loader" hereinafter) and two articulated haulers ("haulers" hereinafter) co-operate to transport material at a set production rate [ton/h], a method for fuel optimal control is developed. On the mission level, optimal cycle times for individual sub-tasks such as wheel loader loading, hauler transport and hauler return, are established through the usage of Pareto fronts. The haulers Pareto fronts are built through the development of a Dynamic Programming (DP) algorithm that trades fuel consumption versus cycle time for a road stretch by means of a time penalty constant. Through varying the time penalty constant n number of times, discrete fuel consumption - cycle time values can be achieved, forming the Pareto front. At a later stage, the same DP algorithm is used to generate fuel optimal vehicle speed and gear trajectories that are used as control signals for the haulers. Input to the DP algorithm is the distance to be travelled, road inclination, rolling resistance coefficient and a max speed limit to avoid unrealistic optimisation results. Thus, a method to describe the road and detect the road related data is needed to enable the optimisation. A map module is built utilising an extended Kalman Filter, Rauch-Tung-Striebel smoother and sensor fusion to merge data and estimate parameters not observable by sensors. The map module uses a model of the vehicle, sensor signals from a GPS or GNSS sensor and machine sensors to establish a map of the road. The wheel loader Pareto front is based on data developed in previous research combined with Volvo in-house data. The developed optimisation algorithms are implemented on a PC and in an interactive computer tablet based system. A human machine interface is created for the tablet, guiding the operators to follow the optimal control signals, which is speed for the haulers and cycle time for the loader. To evaluate the performance of the system it is tested in real working conditions. The contributions develop algorithms, set up a demo mission control system and carry out experiments. Altogether rendering in a platform that can be used as a base for a future design of an off-road transport mission control system