Fuzzy-based collision avoidance system for autonomous driving in complicated traffic scenarios

Collision avoidance is an important requirement for safe and autonomous driving in modern transportation system. In this paper, we present a fuzzy based control approach for smart and safe obstacle avoidance in complicated traffic scenario where there are static and dynamic obstacles (e.g. broken-down vehicles, wrong parking road-side vehicles, or moving vehicles, etc.) The fuzzy system makes an optimal decision to control the car throttle, braking, and steering to avoid collision using the available information on the road map (i.e. the distance to obstacles, the current traffic in the neighbouring lanes, the velocity of the front and rear car, etc.). Simulation results from three different scenarios involving a combination of dynamic and static or broken-down vehicles show that the fuzzy controlled car can effectively avoid obstacle or collision in complicated traffic situations. ©2018 IEEE

Q-learning by the nth step state and multi-agent negotiation in unknown environment

U ovom radu je predstavljen novi postupak Q-učenja kod kojega agent odluku o sljedećoj akciji donosi na osnovu korisnosti nekog budućeg stanja, a ne na osnovu trenutno optimalne akcije. Implementirana je komunikacija agenata u okolini koji si međusobno javljaju svoje buduće akcije što doprinosi kvalitetnijem odabiru akcija pojedinog agenta. Nova metoda nazvana je Q-učenje prema stanju n-tog koraka i dogovaranjem više agenata. Uspoređeni su rezultati testiranja ovdje predstavljenog algoritma s osnovnim QL algoritmom što je i grafički prikazano te su navedene prednosti novog algoritma. Postignuto je prosječno smanjenje od 40 % sudara tijekom postupka učenja.This work will show a new procedure of Q-learning in which the agent’s decision, regarding the next step, is not based on the optimal action at that moment but on the usefulness of a future state. A near agent communication has been implemented so that the agents signal each other their future actions which contribute to a better choice of actions for each of the agents. The new method is named Q-learning by the nth step and multi-agent negotiation. The results of the testing of this algorithm are compared with the basic QL algorithm which is also graphically demonstrated and the advantages of the new algorithm are listed too. An average of 40 % collision decrease is obtained during learning procedure

Design of an obstacle avoidance system for automated guided vehicles

Most Industrial Automated Guided Vehicles CAGV s) follow fixed guide paths embedded in the floor or bonded to the floor surface. Whilst reliable in their basic operation, these AGV systems fail if unexpected obstacles are placed in the vehicle path. This can be a problem particularly in semi-automated factories where men and AGVs share the same environment. The perfonnance of line-guided AGVs may therefore be enhanced with a capability to avoid unexpected obstructions in the guide path. The research described in this thesis addresses some fundamental problems associated with obstacle avoidance for utomated vehicles. A new obstacle avoidance system has been designed which operates by detecting obstacles as they disturb a light pattern projected onto the floor ahead of the AGV. A CCD camera mounted under the front of the vehicle senses obstacles as they emerge into the projection area and reflect the light pattern. Projected light patterns have been used as an aid to static image analysis in the fields f Computer Aided Design and Engineering. This research extends these ideas in a real-time mobile application. A novel light coding system has been designed which simplifies the image analysis task and allows a low-cost embedded microcontroller to carry out the image processing, code recognition and obstacle avoidance planning functions. An AGV simulation package has been developed as a design tool for obstacle avoidance algorithms. This enables potential strategies to be developed in a high level language and tested via a Graphical User Interface. The algorithms designed using the simulation package were successfully translated to assembler language and implemented on the embedded system. An experimental automated vehicle has been designed and built as a test bed for the research and the complete obstacle avoidance system was evaluated in the Flexible Manufacturing laboratory at the University of Huddersfield

A study of mobile robot motion planning

This thesis studies motion planning for mobile robots in various environments. The basic tools for the research are the configuration space and the visibility graph. A new approach is developed which generates a smoothed minimum time path. The difference between this and the Minimum Time Path at Visibility Node (MTPVN) is that there is more clearance between the robot and the obstacles, and so it is safer. The accessibility graph plays an important role in motion planning for a massless mobile robot in dynamic environments. It can generate a minimum time motion in 0(n2»log(n)) computation time, where n is the number of vertices of all the polygonal obstacles. If the robot is not considered to be massless (that is, it requires time to accelerate), the space time approach becomes a 3D problem which requires exponential time and memory. A new approach is presented here based on the improved accessibility polygon and improved accessibility graph, which generates a minimum time motion for a mobile robot with mass in O((n+k)2»log(n+k)) time, where n is the number of vertices of the obstacles and k is the number of obstacles. Since k is much less than n, so the computation time for this approach is almost the same as the accessibility graph approach. The accessibility graph approach is extended to solve motion planning for robots in three dimensional environments. The three dimensional accessibility graph is constructed based on the concept of the accessibility polyhedron. Based on the properties of minimum time motion, an approach is proposed to search the three dimensional accessibility graph to generate the minimum time motion. Motion planning in binary image representation environment is also studied. Fuzzy logic based digital image processing has been studied. The concept of Fuzzy Principal Index Of Area Coverage (PIOAC) is proposed to recognise and match objects in consecutive images. Experiments show that PIOAC is useful in recognising objects. The visibility graph of a binary image representation environment is very inefficient, so the approach usually used to plan the motion for such an environment is the quadtree approach. In this research, polygonizing an obstacle is proposed. The approaches developed for various environments can be used to solve the motion planning problem without any modification. A simulation system is designed to simulate the approaches