Jackknifing refers to the serious situation where a vehicle-trailer system enters a jackknife state and the vehicle and trailer eventually collide if trailer operation is not corrected. This paper considers low speed trailer maneuvering typical of trailer backing. Jackknife state limits can vary due to sideslip caused by physical interaction between the vehicle, trailer, and environment. Analysis of a kinematic model considers sideslip at the vehicle and trailer wheels. Results indicate that vehicle-trailer systems should be divided into three categories based on the ratio of hitch length and trailer tongue length, each with distinct behaviors. The Long Trailer category may have no jackknifing state while the other two categories always have states leading to jackknifing. It is found that jackknife limits, which are the boundaries between the jackknifing state and the recoverable regions, can be divided into safe and unsafe limits. The latter of which must be avoided. Simulations and physical experiments support these results and provide insight about the implications of vehicle and trailer states with slip that lead to jackknifing. Simulations also demonstrate the benefit of considering these new slip-based jackknife limits in trailer backing control.
Backing of a tractor-trailer system is a problem addressed in many literatures. It is usually solved using various nonlinear-based control methods, which are often not easy to implement or tune and do not consider the influence of side-slope. We propose a two-tier controller that is simple and intuitive, which directly controls the curvature of the trailer's trajectory. It allows the control input to be more directly related to path specification and handles path curvature discontinuity better. A side-slope compensator is designed upon the simple controller to prevent side-slope from deteriorating tracking performance. Experimental results are provided to illustrate the capability of this new algorithm applied to a full scale autonomous vehicle and trailer system in a real field environment using minimal sensing capability. Performance comparison between the compensated and uncompensated systems is also presented. Results demonstrate good performance on modest side-slope.
Rapid path planner plays an important role in autonomous ground vehicle (AGV) operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree (RRT) can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 m×170 m area with moderate obstacle complexity.
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