Mario Tétreault

A multi-agent architecture for a driver model for autonomous road vehicles

A look at the current state of driver models and driver related activities (parking, avoiding collisions, path selection, etc.) shows that driving is broken up in a series of small tasks. These must be executed in a coordinated manner by the driver, Their different nature suggest that each could be implemented by an artificial intelligence paradigm (for example: fuzzy logic, neural nets and knowledge based systems). We think that a multi-agent system integrating autonomous agents and enabling them to cooperate to solve problems that are beyond their individual capabilities is the architecture for a more complete driver model. In the long run, the driver model must integrate all characteristics of driver and vehicle, but for the moment current driver models are dedicated to some specific tasks executed by the driver, lateral and longitudinal control, parking and collision avoidance are examples. In this paper we propose an architecture for a generic multi-agent system used to develop the driver model.

Lateral control of a skid-steering mining vehicle

This paper proposes a nonlinear feedback path controller for a skid-steering mining vehicle. The dynamic model of the vehicle is derived using the Boltzmann-Hamel formalism. The control design utilizes the computed torque type technique combined with an auxiliary continuous static state feedback, which involves error coordinates expressed in a moving reference frame partially linked to the vehicle. Assuming that the lateral friction forces caused by the skidding of the wheels are not exactly known, the last part of the paper is centered on the analysis of the effect of Coulomb friction on the behaviour of the system. Asymptotic stabilization of the equilibrium set is achieved for the closed-loop system with discontinuous right-hand side.

Hybrid feedback control for the parking problem of a load-haul-dump mine vehicle

This paper addresses the parking problem of a load-haul-dump (LHD) mine vehicle. The proposed controller stabilizes the configuration coordinates of the vehicle to an arbitrary small neighborhood of the origin. A specific feature of the control scheme is that the location of the reference point of the vehicle depends on the direction of motion. The control law is designed by Lyapunov techniques and relies on transforming a part of the system equations into chained form. The hybrid strategy combines a trajectory tracking control law until the lateral and orientation errors reach a sufficiently small prescribed magnitude with a time-invariant stabilizer to bring to zero the longitudinal error. Simulation results illustrate the effectiveness of the proposed controller.

Improving qualitative simulation with interval arithmetic and additional constraints

Conventional qualitative simulation methods supply good behavioral description for systems with qualitative description, but they also generate several behaviors and spurious solutions. In real‐world applications, a more precise description of the system is needed to reduce the number of behaviors. This article improves the qualitative simulation algorithms by using interval arithmetic and by adding constraints. In engineering applications, quantitative knowledge that can be represented in terms of interval numbers is generally available. Interval arithmetic can combine this quantitative knowledge with qualitative to reduce the number of behaviors. Our approach is compared to the segment interval algorithm, and we show that this algorithm is not appropriate in qualitative physics because it removes good solutions. We also add new global filters, known as nondeterministic cycles and inflection point constraints, to reduce the number of solutions since quantitative knowledge cannot remove all the ambiguities. Some examples show the improvement made by our approach.