The motors or engines of an autonomous ground vehicles (AGV) have torque and power limitations, which limit their abilities to climb steep hills, which are defined to be hills that have high grade sections in which the vehicle is forced to decelerate. Traversal of a steep hill requires the vehicle to have sufficient momentum before entering the hill. This problem is part of a larger class of momentum-based motion planning problems such as the problem of lifting heavy objects with manipulators. Hence, solutions to the steep hill climbing problem have much wider applicability. The motion planning here is accomplished using a dynamic model of the skid-steered AGV used in the experiments along with Sampling Based Model Predictive Control (SBMPC), a recently developed input sampling planning algorithm that may be viewed as a generalization of LPA* to the direct use of kinodynamic models. The motion planning is demonstrated experimentally using two scenarios, one in which the robot starts at rest at the bottom of a hill and one in which the robot starts at rest a distance from the hill. The first scenario requires the AGV to first reverse direction so that the vehicle can gather enough momentum before reaching the hill. This corresponds to having the vehicle begin at a local minimum, which results in a problem that many traditional model predictive control methods cannot solve. It is seen that, whereas open loop trajectories can lead to vehicle immobilization, SBMPC successfully uses the information provided by the dynamic model to ensure that the AGV has the requisite momentum.

Autonomous navigation systems for mobile robots have been successfully deployed for a wide range of planar ground-based tasks. However, very few counterparts of previous planar navigation systems were developed for 3-D motion, which is needed for both unmanned aerial and underwater vehicles. A novel fuzzy behavioral scheme for navigating an unmanned helicopter in cluttered 3-D spaces is developed. The 3-D navigation problem is decomposed into several identical 2-D navigation subproblems, each of which is solved by using preference-based fuzzy behaviors. Due to the shortcomings of vector summation during the fusion of the 2-D subproblems, instead of directly outputting steering subdirections by their own defuzzification processes, the intermediate preferences of the subproblems are fused to create a 3-D solution region, representing degrees of preference for the robot movement. A new defuzzification algorithm that steers the robot by finding the centroid of a 3-D convex region of maximum volume in the 3-D solution region is developed. A fuzzy speed-control system is also developed to ensure efficient and safe navigation. Substantial simulations have been carried out to demonstrate that the proposed algorithm can smoothly and effectively guide an unmanned helicopter through unknown and cluttered urban and forest environments.

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, fuzzy behavior systems have been proposed. The most difficult problem in applying these systems is that of arbitrating or fusing the reactions of the individual behaviors. This paper presents an architectural design of a fuzzy behavior based system for navigation control of robotic vehicles using multivalued reactive fuzzy behaviors. This design allows the robot to thoroughly use the available sensor information when choosing the control action to be taken. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.