Study On Autonomous Navigation Of Intelligent Air-Cushion Vehicle For Soft Terrain

By combining the techniques in the fields of off-road vehicle and robotics, this work studies the autonomous navigation problem for intelligent air-cushion vehicles (ACVs) for soft terrain. ACVs are able to use the lifting system to adjust the pressure under the travelling mechanism and thus able to improve the crossing ability on soft terrain, such as swamp, beach and desert. By taking advantage of autonomous navigation and automatic control techniques, intelligent ACVs may substitute people in dangerous tasks like exploration, searching, cleaning and sampling. Consequently, this interdisciplinary study could have practical significance in many applications in industry, agriculture and military.The autonomous navigation system of the targeted intelligent ACV is described with three sub-systems, namely, the ACV, a real environment, and a virtual environment. Its workflow is briefly summarized as the real environment identification and the virtual environment construction based on sensor information, vehicle motion planning implemented in the virtual environment, as well as vehicle operating control for the realization of the planning objectives. The structure of the work, correspondingly, comprises the ACV modeling, soil parameter estimation, energy consumption optimization, motion planning and operating control.Firstly, based on a prototype ACV, the structure model and dynamic model of the targeted ACV are established. In the perspective of structure, the ACV consists of a lifting system and a propulsion system. The loss models of air pressure and flow are built for the former and the motion transfer model for the latter. In the perspective of dynamics, an 11 degree-of-freedom system is modeled by the Lagrangian method.As an important aspect of identification for soft terrain environment, eight key soil parameters are estimated step-by-step. In succession, the hybrid g-EKF algorithm is employed for the calculation of the soil parameters related to the tractive effort, the logarithm-mean algorithm for those related to the vertical forces, and the least squares method for those related to the bulldozing resistances. Experiment results show that the estimation multi-solution problem can be eliminated and estimation accuracy can be improved.Energy consumption optimization is studied next. By analyzing the relationships among the ACV's running parameters (e.g. load distribution, slip ratio and velocity), energy consumption is simplified as a function with only respect to slip ratio and load distribution ratio. Then the designed Adaptive Bees Algorithm (ABA) is applied to optimize this function. Its results, as the steady-state objectives of the running parameters, will be used in the succeeding motion planning and operating control. Experiment results show that the ABA can improve both optimization accuracy and optimization efficiency with the help of the features of functional partitioning, parallel operation and adaptive patch size adjustment.Motion planning is then made for the ACV by the Artificial Potential Field (APF) method, on the purpose of solving the instantaneous objectives of its linear velocity and angular velocity. For the APF modeling, multiple motion requirements are embodied in the contents of potential items, involving goal attractive potential, obstacle repulsive potential, dynamic safety potential and energy economy potential. In the APF application, the objectives of velocity direction and speed are determined respectively so as to improve calculation efficiency. Experiment results show that the potentials have different importance in applications and the major motion requirement could thus be satisfied.Finally, operating control is implemented to the ACV. A parallel double-loop control system is designed to control its linear/angular velocities and load distribution ratio. In the control system, fuzzy-PID controllers make up its core whose fuzzy reasoning rules present the idea of parameter coordinated control. Relatively, velocity control tracks an instantaneous objective, so that dynamic performance is paid more attention to. On the contrary, load distribution radio control tracks a steady-state objective and thus attaches more importance to static performance. Experiment results show that the ACV could, under control, reach the destination with soft-landing. The variations of path and velocity are consistent with the theoretical analysis in this process. Moreover, the velocity control presents a good dynamic performance, as required, to benefit the vehicle's safety, and the load distribution ratio control manifests a good static performance to decrease energy consumption.Although a soft-terrain-used intelligent ACV is taken as the object of study here, the proposed techniques and research results could have broader applications. For example, the designed soil parameter estimation algorithm is universally reasonable for ACVs. The ABA, as a global optimization metaheuristic, is suitable for general functional optimization problems. Relying on the improvements to potential function composition, motion requirement coordination and motion planning implementation, the improved APF is worth taking up a place in the area of motion planning for intelligent vehicles and robots. Consequently, the above three respects constitute the innovations of the work.