Motion Dynamics of a Rover With Slip-Based Traction Model

Proceedings of the 2002 IEEE International Conference on Robotics & Automation, Washington, DC, May 200

OBTAINING OPTIMAL CONTROL PARAMETERS-ACCELERATION BASED WHEEL TRACTION CONTROL

This project deals with a detailed dynamic model of a two independent wheel drives and a traction control system. By applying external force it is possible to have a path plan in each wheel drive that enables the implementation of a traction control algorithm. This control level improves the stability and the safety of the vehicle. Analysis, design and simulation results of this system will be presented. The wheel traction control method for path tracking of the two independent wheel drives is the function of velocity and acceleration of the mobile robots. The traction control algorithm which can be independently implemented to each wheel without extra sensors and devices compared with standard speed control. Simulations are performed to verify the validity of the algorithm. The proposed traction control algorithm to improve the tracking control efficiency. This project work aims to analyze the fixed acceleration path for two independent wheel drives system by using analyzing software (Adams 12.0) and also this project work is mainly focused on the increase stability and the safety of the two independent wheel drives by planning the fixed acceleration path

Investigation on electric motor braking control system for electric powered wheelchair

In recent years, research on Electric Powered Wheelchair (EPW) has been widely studied due to its high importance of mobility for disabled people. During descent on a slope, the manual braking system is commonly used to control the speed by gripping the brake lever. However, the task becomes difficult if the user is an elderly or paralyzed due to their body’s deficiencies. As a result, the possibilities of collision and injuries to occur are high. In this study, the automatic electric motor braking control that is known as Hill Descent Control (HDC) is proposed to increase the safety of EPW during descending on slopes. Since the electric motor has an advantage which can generate the torque during braking, the plugging braking is integrated with the HDC system to control the speed of the EPW according to the desired speed from the user. The analysis of this study is divided into three phases; investigation of braking performance using electrical braking, development of active braking control system in the embedded system as well as the simulation environment and analysis on active braking control system in experimental and simulation work. From the experimental results, the plugging brake is most suitable to integrate with the active brake control system compared to the regenerative and dynamic brake. In the plugging brake, by changing the plugging voltage from 0.5 V to 4.5 V, a variety of dynamic behaviour effects such as braking distance, tire speed and slip ratio can be achieved. Meanwhile, from the analysis of active braking control system that was integrated with plugging braking, both of the experimental and simulation analysis results show the speed of EPW can be maintained at the desired speed o

Path-Following Control of Wheeled Planetary Exploration Robots Moving on Deformable Rough Terrain

The control of planetary rovers, which are high performance mobile robots that move on deformable rough terrain, is a challenging problem. Taking lateral skid into account, this paper presents a rough terrain model and nonholonomic kinematics model for planetary rovers. An approach is proposed in which the reference path is generated according to the planned path by combining look-ahead distance and path updating distance on the basis of the carrot following method. A path-following strategy for wheeled planetary exploration robots incorporating slip compensation is designed. Simulation results of a four-wheeled robot on deformable rough terrain verify that it can be controlled to follow a planned path with good precision, despite the fact that the wheels will obviously skid and slip

Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control

Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence of variations in the operating conditions, the model should be continuously refined to compensate for dynamics changes. In this paper, we present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. We combine offline learning from past experience and online learning from current robot interaction with the unknown environment. These two ingredients enable a highly sample-efficient and adaptive learning process, capable of accurately inferring model dynamics in real-time even in operating regimes that greatly differ from the training distribution. Moreover, we design an uncertainty-aware model predictive controller that is heuristically conditioned to the aleatoric (data) uncertainty of the learned dynamics. This controller actively chooses the optimal control actions that (i) optimize the control performance and (ii) improve the efficiency of online learning sample collection. We demonstrate the effectiveness of our method through a series of challenging real-world experiments using a quadrotor system. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions, while it significantly outperforms classical and adaptive control baselines

Planetary Rover Simulation for Lunar Exploration Missions

When planning planetary rover missions it is useful to develop intuition and skills driving in, quite literally, alien environments before incurring the cost of reaching said locales. Simulators make it possible to operate in environments that have the physical characteristics of target locations without the expense and overhead of extensive physical tests. To that end, NASA Ames and Open Robotics collaborated on a Lunar rover driving simulator based on the open source Gazebo simulation platform and leveraging ROS (Robotic Operating System) components. The simulator was integrated with research and mission software for rover driving, system monitoring, and science instrument simulation to constitute an end-to-end Lunar mission simulation capability. Although we expect our simulator to be applicable to arbitrary Lunar regions, we designed to a reference mission of prospecting in polar regions. The harsh lighting and low illumination angles at the Lunar poles combine with the unique reflectance properties of Lunar regolith to present a challenging visual environment for both human and computer perception. Our simulator placed an emphasis on high fidelity visual simulation in order to produce synthetic imagery suitable for evaluating human rover drivers with navigation tasks, as well as providing test data for computer vision software development.In this paper, we describe the software used to construct the simulated Lunar environment and the components of the driving simulation. Our synthetic terrain generation software artificially increases the resolution of Lunar digital elevation maps by fractal synthesis and inserts craters and rocks based on Lunar size-frequency distribution models. We describe the necessary enhancements to import large scale, high resolution terrains into Gazebo, as well as our approach to modeling the visual environment of the Lunar surface. An overview of the mission software system is provided, along with how ROS was used to emulate flight software components that had not been developed yet. Finally, we discuss the effect of using the high-fidelity synthetic Lunar images for visual odometry. We also characterize the wheel slip model, and find some inconsistencies in the produced wheel slip behaviour

Nouveaux concepts de locomotion pour véhicules tout-terrain robotisés

Robotic ground vehicles are mechanisms that use gravity and contact forces with the ground to perform motion. They can either be wheeled, tracked or legged. In this thesis we will focus on n-wheeled vehicles able to perform ground following motion with all the wheels maintaining contact at the same time. The main goal of this work is to establish the implication of the topological architecture of the vehicle mechanism on criteria such as climbing skills, robustness, ground clearance, weight, power consumption, and price. Efficient tools will be provided to help the robot designer to understand the implications of important design parameters like the number of wheels, the vehicle mechanism, and the motorisation of joints on the above criteria. The general state of a robotic ground vehicle can be described using spatial vectors containing both the linear and angular components of physical quantities such as position, velocity, acceleration and linear force. By definition, there is motion when the vehicle's link velocity state vector (expressed from the ground reference) is greater than zero. Wheeled ground following motion is then a special case of vehicle constrained motion where all wheels maintain contact with the ground. This thesis will describe a general kinematic and dynamic analysis of n-wheeled ground following robots. We will then discuss "contact forces optimisation techniques" and show the relationship between the number of wheels of a vehicle mechanism, the topological structure and the optimised degrees of fredom that we can get for the contact forces distribution. We will conclude with some considerations concerning the sensors needs for on-board terrain estimation. We will emphasise our argument using our two robot designs as examples: Shrimp: A 6-wheeled ground vehicle based on a 3 DOF passive suspension mechanism. With this design, no sensor based control is necessary to maintain ground contact with all the wheels. The distribution of tangential contact forces is done passively but can be optimised with on board active control and sensors for contact properties estimation (gyro, joint position sensors). Octopus: A 8-wheeled ground vehicle based on a (6 DOF active + 1 DOF passive) suspension mechanism. The autonomous coordination of the active 14 DOF is based on the on-board integration of inclinometer, joint position sensors and tactile wheels able to sense ground contact properties (angle, curvature, force, ...). With this design, active control can distribute the contact forces to minimise tangential forces and increase traction. This decreases the need for friction to climb obstacles. The theoretical investigation and new sensing concepts enable the design these two robots that demonstrate excellent capabilities for rough terrain. Passive Wheeled Locomotion Mechanisms (WLM) solutions are now mature enough for real applications like space exploration. However, active WLM solutions demonstrate potential climbing skills that cannot be equalled passively. Enhanced integration of sensors, actuators and advanced embedded control algorithms will lead to greater applications for future field and service robotics applications

Autonomous Decision Making Facing Uncertainty, Risk, and Complexity

The creation of autonomous decision makers to handle potentially hazardous or logistically complicated tasks is desirable for the purpose of reducing human labor and exposure to risk, and for increasing the general performance of autonomous systems in a wide variety of environments. However, traditional autonomous decision makers perform poorly when faced with the uncertainty, risk, and complexity of these situations. In this dissertation, I explore these problems through three distinct case studies, and make significant improvements in the performance of autonomous decision makers through the application and synthesis of concepts from decision theory, risk assessment, and computational cognition. The first case study is a terrain navigation problem which shows how the availability of information and information fidelity affect performance, and develops and validates techniques to overcome these problems. Next, an automated design problem is used to explore and evaluate techniques for handling intractably large decision spaces. Finally, a mission planning and command simulation is performed to investigate how multiple time delineated decisions can be made efficiently. Through these three case studies, I show that autonomous decision-makers can be made to perform efficiently and effectively under uncertain, high-risk, and incredibly complex circumstances

Adaptive Locomotion: The Cylindabot Robot

Adaptive locomotion is an emerging field of robotics due to the complex interaction between the robot and its environment. Hybrid locomotion is where a robot has more than one mode of locomotion and potentially delivers the benefits of both, however, these advantages are often not quantified or applied to new scenarios. The classic approach is to design robots with a high number of degrees of freedom and a complex control system, whereas an intelligent morphology can simplify the problem and maintain capabilities. Cylindabot is designed to be a minimally actuated hybrid robot with strong terrain crossing capabilities. By limiting the number of motors, this reduces the robot's weight and means less reinforcement is needed for the physical frame or drive system. Cylindabot uses different drive directions to transform between using wheels or legs. Cylindabot is able to climb a slope of 32 degrees and a step ratio of 1.43 while only being driven by two motors. A physical prototype and simulation models show that adaptation is optimal for a range of terrain (slopes, steps, ridges and gaps). Cylindabot successfully adapts to a map environment where there are several routes to the target location. These results show that a hybrid robot can increase its terrain capabilities when changing how it moves and that this adaptation can be applied to wider environments. This is an important step to have hybrid robots being deployed to real situations