14,869 research outputs found
Hybrid stabilizing control on a real mobile robot
To establish empirical verification of a stabilizing controller for nonholonomic systems, the authors implement a hybrid control concept on a 2-DOF mobile robot. Practical issues of velocity control are also addressed through a velocity controller which transforms the mobile robot to a new system with linear and angular velocity inputs. Experiments in the physical meaning of different controller components provide insights which result in significant improvements in controller performanc
A Simple Mono-Dimensional Approach for Lap Time Optimisation
Lap time minimisation methods have great relevance in the analysis of race tracks, and in the design and optimisation of race vehicles. Several lap time minimisation approaches have been proposed in the literature, which are computationally demanding because they need to either solve differential equations or to implement a forward−backward integration based on an apex-finding method. This paper proposes an alternative method, based on a mono-dimensional quasi-steady-state numerical approach. The proposed approach uses a simplified vehicle model accounting for combined tyre−road interactions, aerodynamic effects, and power limitations. The method exploits the knowledge of the curvature of the trajectory, which is worked out through a rigorous approach that allows for the use trajectories defined with respect to ageneric curve parameter and not necessarily the arc length. An iterative routine is implemented that exploits the vehicle dynamics, without solving differential equations or performing forward−backward integrations from the trajectory apexes. Simulations are carried out on three different tracks and are shown to be computationally efficient. Despite being intentionally simple, the proposed method allows to grasp key aspects of the problem, such as the effect of the combined tyre−road interactions on the acceleration profiles, and the effect of aerodynamic drag and downforce on the position of the braking point on the track and on the speed profile
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
Simulation of Electric Vehicles Combining Structural and Functional Approaches
In this paper the construction of a model that represents the behavior of an Electric Vehicle is described. Both the mechanical and the electric traction systems are represented using Multi-Bond Graph structural approach suited to model large scale physical systems. Then the model of the controllers, represented with a functional approach, is included giving rise to an integrated model which exploits the advantages of both approaches. Simulation and experimental results are aimed to illustrate the electromechanical interaction and to validate the proposal.Fil: Silva, Luis Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Magallán, Guillermo Andrés. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de la Barrera, Pablo Martin. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: de Angelo, Cristian Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; ArgentinaFil: Garcia, Guillermo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Rio Cuarto. Facultad de Ingeniería. Grupo de Electronica Aplicada; Argentin
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving
Behavior and motion planning play an important role in automated driving.
Traditionally, behavior planners instruct local motion planners with predefined
behaviors. Due to the high scene complexity in urban environments,
unpredictable situations may occur in which behavior planners fail to match
predefined behavior templates. Recently, general-purpose planners have been
introduced, combining behavior and local motion planning. These general-purpose
planners allow behavior-aware motion planning given a single reward function.
However, two challenges arise: First, this function has to map a complex
feature space into rewards. Second, the reward function has to be manually
tuned by an expert. Manually tuning this reward function becomes a tedious
task. In this paper, we propose an approach that relies on human driving
demonstrations to automatically tune reward functions. This study offers
important insights into the driving style optimization of general-purpose
planners with maximum entropy inverse reinforcement learning. We evaluate our
approach based on the expected value difference between learned and
demonstrated policies. Furthermore, we compare the similarity of human driven
trajectories with optimal policies of our planner under learned and
expert-tuned reward functions. Our experiments show that we are able to learn
reward functions exceeding the level of manual expert tuning without prior
domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote,
minor correction in preliminarie
Multi-objective optimisation for battery electric vehicle powertrain topologies
Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars
I Am The Passenger: How Visual Motion Cues Can Influence Sickness For In-Car VR
This paper explores the use of VR Head Mounted Displays
(HMDs) in-car and in-motion for the first time. Immersive
HMDs are becoming everyday consumer items and, as they
offer new possibilities for entertainment and productivity, people
will want to use them during travel in, for example, autonomous
cars. However, their use is confounded by motion
sickness caused in-part by the restricted visual perception
of motion conflicting with physically perceived vehicle motion
(accelerations/rotations detected by the vestibular system).
Whilst VR HMDs restrict visual perception of motion, they
could also render it virtually, potentially alleviating sensory
conflict. To study this problem, we conducted the first on-road
and in motion study to systematically investigate the effects
of various visual presentations of the real-world motion of
a car on the sickness and immersion of VR HMD wearing
passengers. We established new baselines for VR in-car motion
sickness, and found that there is no one best presentation
with respect to balancing sickness and immersion. Instead,
user preferences suggest different solutions are required for
differently susceptible users to provide usable VR in-car. This
work provides formative insights for VR designers and an entry
point for further research into enabling use of VR HMDs,
and the rich experiences they offer, when travelling
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