Design of a force sensing system to assist robotic space servicing and exploration operations

The focus of this research has been the design and fabrication of a rail sensor system (RSS) that employs an array of commercially available load cells to reconstruct contact forces by determining a centroid of force. The proposed RSS system can be divided into two coherent systems: a mechanical system and an electrical system. The mechanical system is composed of two load cells, two aluminum support structures, and a friction resistant shoulder screw. The electrical system consists of a commercially available USB interface board responsible for capturing and transmitting raw voltage values from each load cell to the data logging software. Computer simulations and ground based testing were conducted and compared to validate the proof of concept and a fuzzy logic control scheme was developed to simulate real-time angle and trajectory corrections based on the output of each load cell. Tests conducted with the Rail Sensor System (RSS) reinforce the concept of reconstructing contact forces using an array of strain gages and their calculated centroid of force. The raw voltage values reported by the load cells contain valuable information that can potentially provide teleoperators and autonomous algorithms the information necessary to determine nominal service vehicle approach angles

INTELLIGENT VEHICLE PARKING SYSTEM WITH WARNING CONTROL

This paper is about a system for assisting a car driver while parking in reverse direction. This system is used in vehicles, and continuously detects the obstacle so as to avoid accidental situation while reversing the vehicle. It makes easy for a driver to park the car in reverse direction. Driver gets immediate warning on LCD display about the obstacle. This system also gives warning to the pedestrians using buzzer while reversing the car. It not only gives warning but also apply automatic brakes when distance between an obstacle and car is below some threshold value. This is a microcontroller based system which is useful in automobiles as an intelligent vehicle assistant for safe driving. Most of the car drivers used the reverse radar or reverse camera to detect the road situation behind the vehicle when it is engaged in reverse gear. As a matter of fact, the pedestrians can virtually know if the vehicle is backing up or not only by seeing the permanent bright reverse lamps. And as there is not much change with the reverse lamp to be seen, therefore their warning function for pedestrians seems to be still insufficient eventually. Not only the warning feature of the reverse lamps is virtually not sufficient but their function will be influenced owing tothe different installation positions. Hence we propose the new technology to overcome this issue

Path following with backtracking based on fuzzy controllers for forward and reverse driving

International audienceAutonomous navigation is one of the most important challenges in the outdoor mobile robot field. For an automatic vehicle (which can be considered a type of outdoor mobile robot), path following can be implemented using global positioning systems (GPS) to allow the configuration of different navigation styles such as the shortest or fastest route, toll avoidance, etc., and even the definition of new routes. The main problem is when an unexpected circumstance occurs - traffic accident, road closure, etc. This paper presents an autonomous vehicle guidance system based on fuzzy logic systems to resolve unexpected road situations. A fuzzy steering controller performs the autonomous navigation, allowing reverse as well as forward driving in urban environments. Good performance was obtained in trials performed with a commercial electric Citroën Berlingo van on a private driving circuit

Navigational fuzzy logic control of an autonomous vehicle

The control of an autonomous passenger vehicle to reach a target amid static and dynamic obstacles is presented. To accommodate various model and environmental uncertainties, fuzzy logic is used in designing the vehicle\u27s controller. The controller evaluates sensor information and outputs signals to change the vehicle\u27s steering and throttle angles. To emulate human behavior, the controller is divided into separate modules. Each module deals with a specific navigational problem such as target steering, target throttle control, cornering throttle control, collision avoidance steering, and collision avoidance throttle control. Several simulation examples are included

Hardware Implementation of Soft Computing Approaches for an Intelligent Wall-following Vehicle

Soft computing techniques are generally well-suited for vehicular control systems that are usually modeled by highly nonlinear differential equations and working in unstructured environment. To demonstrate their applicability, two intelligent controllers based upon fuzzy logic theories and neural network paradigms are designed for performing a wall-following task and an autonomous parking task. Based on performance and flexibility considerations, the two controllers are implemented onto a reconfigurable hardware platform, namely a Field Programmable Gate Array (FPGA). As the number of comparative studies of these two embedded controllers designed for the same application is limited in the literature, one of the main goals of this research work has been to evaluate and compare the two controllers in terms of hardware resource requirements, operational speeds and trajectory tracking errors in following different pre-defined trajectories. The main advantages and disadvantages of each of the controllers are presented and discussed in details. Challenging issues for implementation of the controllers on the FPGA platform are also highlighted. As the two controllers exhibit benefits and drawbacks under different circumstances, this research suggests as well a hybrid controller scheme as an attempt to integrate the benefits of both control units. To evaluate its performance, the hybrid controller is tested on the same pre-defined trajectories and the corresponding results are compared to that of the fuzzy logic and the neural network based controllers. For further demonstration of the capabilities of the wall-following controllers in other applications, the fuzzy logic and the neural network controllers are used in a parallel parking system. We see this work to be a stepping stone for further research work aiming at real world implementation of the controllers on Application Specified Integrated Circuit (ASIC) type of environment

Fuzzy logic control of automated guided vehicle

This thesis describes the fuzzy logic based control system for an automated guided vehicle ( AGV ) designed to navigate from one position and orientation to another while avoiding obstacles. A vehicle with an onboard computer system and a beacon based location system has been used to provide experimental confirmation of the methods proposed during this research. A simulation package has been written and used to test control techniques designed for the vehicle. A series of navigation rules based upon the vehicle's current position relative to its goal produce a fuzzy fit vector, the entries in which represent the relative importance of sets defined over all the possible output steering angles. This fuzzy fit vector is operated on by a new technique called rule spreading which ensures that all possible outputs have some activation. An obstacle avoidance controller operates from information about obstacles near to the vehicle. A method has been devised for generating obstacle avoidance sets depending on the size, shape and steering mechanism of a vehicle to enable their definition to accurately reflect the geometry and dynamic performance of the vehicle. Using a set of inhibitive rules the obstacle avoidance system compiles a mask vector which indicates the potential for a collision if each one of the possible output sets is chosen. The fuzzy fit vector is multiplied with the mask vector to produce a combined fit vector representing the relative importance of the output sets considering the demands of both navigation and obstacle avoidance. This is operated on by a newly developed windowing technique which prevents any conflicts produced by this combination leading to an undesirable output. The final fit vector is then defuzzified to give a demand steering angle for the vehicle. A separate fuzzy controller produces a demand velocity. In tests carried out in simulation and on the research vehicle it has been shown that the control system provides a successful guidance and obstacle avoidance scheme for an automated vehicle

Advanced Navigation for Planetary Vehicles Applying an Approximate Mapping Technique

This thesis provides a method for compressing the information provided by JPL Mars rover obstacle sensors by creating an approximate map of the terrain around the vehicle. This thesis demonstrates that this method provides adequate information for a human operator to negotiate complex obstacles fields. By dividing the area around the vehicle into regions and classifying each region as to how dangerous (impassable), the sensor data can be accumulated with minimal overhead. The terrain in each region has a number between zero and one, with zero meaning completely passable and one meaning completely impassable. A continuum of possible values between the extremes classify in the sense of fuzzy set theory. This process allows obstacles to be represented in the map as an abstraction of the data instead of being arduously tracked individually, requiring much memory and complex processing. The map concept is also valuable in the respect that via translation of the vehicle information is passed to regions without direct sensor inputs. This allows the system to track obstacles to the side and to some extent behind the vehicle. The system, therefore, could potentially deal with complex situations where this information would be valuable such as a situation where it needs to recognize and back out of a trap. This thesis includes the development of the approximate mapping algorithm, explanation of the integration with a test bed vehicle, demonstration of the algorithm using the test bed vehicle, and ix ground work for the development of an automatic decision making scheme, which will constitute the continuing research effort

Continuous control of an underground loader using deep reinforcement learning

Reinforcement learning control of an underground loader is investigated in simulated environment, using a multi-agent deep neural network approach. At the start of each loading cycle, one agent selects the dig position from a depth camera image of the pile of fragmented rock. A second agent is responsible for continuous control of the vehicle, with the goal of filling the bucket at the selected loading point, while avoiding collisions, getting stuck, or losing ground traction. It relies on motion and force sensors, as well as on camera and lidar. Using a soft actor-critic algorithm the agents learn policies for efficient bucket filling over many subsequent loading cycles, with clear ability to adapt to the changing environment. The best results, on average 75% of the max capacity, are obtained when including a penalty for energy usage in the reward.Comment: 9 pages, 7 figure