Off-road autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on sev- eral vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km. We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain mapping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories expressed in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in ...

The feasibility of applying neural network learning techniques in problems of system identification and control has been demonstrated through several empirical studies. These studies are based for the most part on gradient techniques for deriving parameter adjustment laws. While such schemes perform well in many cases, in general, problems arise in attempting to prove stability of the overall system, or convergence of the output error to zero. This paper presents a stability theory approach to synthesizing and analyzing identification and control schemes for nonlinear dynamical systems using neural network models. The nonlinearities of the dynamical system are assumed to be unknown and are modelled by neural network architectures. Multilayer networks with sigmoidal activation functions and radial basis function networks are the two types of neural network models that are considered. These static network architectures are combined with dynamical elements, in the form of stable filters, to construct a type of recurrent network configuration which is shown to be capable of approximating a large class of dynamical systems.

this paper, back-stepping control, has become a very popular and powerful tool in nonlinear adaptive control. A complete account for such methods can be found in [59, 73, 121]. An extension to non linearizable systems was proposed in [107]. The combination of adaptive control and feedback linearization applied to flight control can be found in [126]. In most of the classical adaptive control literature it is common to assume the unknown dynamics to have a known structure with unknown parameters entering linearly in the dynamics. The linear parameterization of unknown dynamics poses serious obstacles in adopting adaptive control algorithms in practical applications, because it is di#cult to fix the structure of the unknown nonlinearities. This fact has been the motivating factor behind the interest in on-line function approximators to estimate and learn the unknown function. The most common function approximators used in adaptive control are artificial neural network and fuzzy logic structures. On line control algorithms that do not require knowledge of the system dynamics (except its dimension and relative degree) have been made possible by employing artificial neural networks in the feedback loop [34]. The ability of neural networks to approximate uniformly continuous functions has been proven in several articles [21, 27, 38, 28, 40]. An important aspect of neural network control applications is the di#erence between approximation theory results and what is achievable in on-line adaptive schemes using such approximators. First and most importantly, in o#-line applications the neural network weights are updated based on input-output matching, 5 whereas in direct adaptive control situations the update of the network parameters is driven by a tracking error, which by it...