ESP for Suppression of Jackknifing in an Articulated Bus

The Electronic Stability Program (ESP) is becoming increasingly popular in vehicles as a means to prevent spin-out and lane departure accidents, and is nowadays a standard feature in most cars. From 2012 the ESP will also be a standard feature in buses. As a first step, this thesis is dedicated to implementing an ESP for an articulated bus in simulation using Matlab/Simulink. The core of the ESP is a yaw rate controller that calculates a moment about the center of gravity of the front part of the bus to stabilize it. Integrated in the ESP are also an Anti-lock Braking System (ABS) and an Anti-Spin Regulation (ASR) system, which are model-based controllers that produce the brake pressures needed to achieve the desired moment. For the articulated bus it is clear that the fact that the bus consists of two parts makes the problem of stabilizing the bus more difficult. Furthermore, for articulated vehicles it is known that the risk of having the vehicle folding, known as jackknifing, during cornering is a major problem. However, the ESP is found to stabilize the bus for a large number of maneuvers and loading configurations, as well as suppressing jackknifing

Derivation of a Six Degrees-of-Freedom Ground-Vehicle Model for Automotive Applications

This report contains derivations of a rigid double-track ground vehicle model including roll and pitch dynamics, using a Newton-Euler modeling approach. Suspension is incorporated in the model. The suspension system is modeled as a rotational spring and damper system, where the spring and damper constants for each wheel have been lumped to two constants, one for each degree of freedom. The resulting chassis model is of fifth order. In addition, a first-order approach to take load transfer into account is discussed, which gives an additional degree of freedom. The model is derived with the aim of accurate simulation, but should also be possible to utilize for nonlinear control design

Extending the Occupancy Grid Concept for Low-Cost Sensor Based SLAM

The simultaneous localization and mapping problem is approached by using an ultrasound sensor and wheel encoders. To be able to account for the low precision inherent in ultrasound sensors, the occupancy grid notion is extended. The extension takes into consideration with which angle the sensor is pointing, to compensate for the issue that an object is not necessarily detectable from all position due to deficiencies in how ultrasonic range sensors work. Also, a mixed linear/nonlinear model is derived for future use in Rao-Blackwellized particle smoothing

Rao-Blackwellized Out-of-Sequence Processing for Mixed Linear/Nonlinear State-Space Models

We investigate the out-of-sequence measurements particle filtering problem for a set of conditionally linear Gaussian state-space models, known as mixed linear/nonlinear state-space models. Two different algorithms are proposed, which both exploit the conditionally linear substructure. The first approach is based on storing only a subset of the particles and their weights, which implies low memory and computation requirements. The second approach is based on a recently reported Rao-Blackwellized forward filter/backward simulator, adapted to the out-of-sequence filtering task with computational considerations for enabling online implementations. Simulation studies on two examples show that both approaches outperform recently reported particle filters, with the second approach being superior in terms of tracking performance

Rao-Blackwellized Particle Filters with Out-of-Sequence Measurement Processing

This paper addresses the out-of-sequence measurement (OOSM) problem for mixed linear/nonlinear state-space models, which is a class of nonlinear models with a tractable, conditionally linear substructure. We develop two novel algorithms that utilize the linear substructure. The first algorithm effectively employs the Rao-Blackwellized particle filtering framework for updating with the OOSMs, and is based on storing only a subset of the particles and their weights over an arbitrary, predefined interval. The second algorithm adapts a backward simulation approach to update with the delayed (out-of-sequence) measurements, resulting in superior tracking performance. Extensive simulation studies show the efficacy of our approaches in terms of computation time and tracking performance. Both algorithms yield estimation improvements when compared with recent particle filter algorithms for OOSM processing; in the considered examples they achieve up to 10% enhancements in estimation accuracy. In some cases the proposed algorithms even deliver accuracy that is similar to the lower performance bounds. Because the considered setup is common in various estimation scenarios, the developed algorithms enable improvements in different types of filtering applications

Mobile Manipulation with a Kinematically Redundant Manipulator for a Pick-and-Place Scenario

Mobile robots and robotic manipulators have traditionally been used separately performing different types of tasks. For example, industrial robots have typically been programmed to follow trajectories using position sensors. If combining the two types of robots and adding sensors new possibilities emerge. This enables new applications, but it also raises the question of how to combine the sensors and the added kinematic complexity. An omni-directional mobile robot together with a new type of kinematically redundant manipulator for future use as a service robot for grocery stores is proposed. The scenario is that of distributing groceries on refilling shelves, and a constraint- based task specification methodology to incorporate sensors and geometric uncertainties into the task is employed. Sensor fusion is used to estimate the pose of the mobile base online. Force sensors are utilized to resolve remaining uncertainties. The approach is verified with experiments

A Convex Approach to Path Tracking with Obstacle Avoidance for Pseudo-Omnidirectional Vehicles

This report addresses the related problems of trajectory generation and time-optimal path tracking with online obstacle avoidance. We consider the class of four-wheeled vehicles with independent steering and driving on each wheel, also referred to as pseudo-omnidirectional vehicles. Appropriate approximations of the dynamic model enable a convex reformulation of the path-tracking problem. Using the precomputed trajectories together with model predictive control that utilizes feedback from the estimated global pose, provides robustness to model uncertainty and disturbances. The considered approach also incorporates avoidance of a priori unknown moving obstacles by local online replanning. We verify the approach by successful execution on a pseudo-omnidirectional mobile robot, and compare it to an existing algorithm. The result is a significant decrease in the time for completing the desired path. In addition, the method allows a smooth velocity trajectory while avoiding intermittent stops in the path execution