The use of artificial potential fields is beneficial in most two dimensional environments but they are bound by limitations. Introduction of the third dimension eradicates some of the limitations and brings about a set of new problems. If the emergent problems are solved then the new approach can give way to smarter helicopters and consequently a new game-play experience or possibly safer flight. This thesis aims to solve the emergent problems and proposes a new solution for guidance of autonomous helicopter agents in 3D games based on artificial potential fields. This new approach is compared to the most used alternative the A* pathfinding algorithm. Our experiments reveal that potential fields is a formidable alternative for navigation of helicopters. It can perform many times faster than the A* alternative and has lower rate of collision. / Neoaxis PFA

The use of potential field based navigation schemes in robotics has been limited by inherent local minima issues. Local minima traps, small passages, unstable motion, and targets positioned near objects all pose major concerns when using potential fields for local vehicle control. This work proposes a new algorithm, "Re-Active Vector Equilibrium" (RAVE) that mitigates many of these issues. The vehicle representation model is expanded to use multiple points subject to potential calculation and the addition of two forces, a velocity dependent "risk force" (F_rsk) and a velocity and direction dependent "tangential force" (F_tan). The vehicle representation model is also expanded from a single reactive point to a series of points that define the vehicle body, providing better and simpler vehicle control. This has the effect of simplifying the required calculations at the cost of increasing the calculation count. The risk force, F_rsk, allows for dynamic adaptation to the immediate environment by acting in opposition to the net obstacle force, and is inversely proportional to the vehicle speed. The tangential force, F_tan, encourages better wall-following behaviour and provides a biasing mechanism to resolve obstacle aligned with target local minima issues.

In this thesis we present a hybrid control methodology using Artificial Potential Fields (APF) integrated with a modified Simulated Annealing (SA) optimization algorithm for motion planning of a multi-link robots team. The principle of this work is based on the locomotion of a snake where subsequent links follow the trace of the head. The proposed algorithm uses the APF method which provides simple, efficient and effective path planning and the modified SA is applied in order for the robots to recover from a local minima. Modifications to the SA algorithm improve the performance of the algorithm and reduce convergence time. Validation on a three-link snake robot shows that the derived control laws from the motion planning algorithm that combine APF and SA can successfully navigate the robot to reach its destination, while avoiding collisions with multiple obstacles and other robots in its path as well as recover from local minima. To improve the performance of the algorithm, the gradient descent method is replaced by Newton’s method which helps in reducing the zigzagging phenomenon in gradient descent method while the robot moves in the vicinity of an obstacle. / UOIT

Context. Building autonomous racing car controllers is a growing field of computer science which has been receiving great attention lately. An approach named Artificial Potential Fields (APF) is used widely as a path finding and obstacle avoidance approach in robotics and vehicle motion controlling systems. The use of APF results in a collision free path, it can also be used to achieve other goals such as overtaking and maneuverability. Objectives. The aim of this thesis is to build an autonomous racing car controller that can achieve good performance in terms of speed, time, and damage level. To fulfill our aim we need to achieve optimality in the controller choices because racing requires the highest possible performance. Also, we need to build the controller using algorithms that does not result in high computational overhead. Methods. We used Particle Swarm Optimization (PSO) in combination with APF to achieve optimal car controlling. The Open Racing Car Simulator (TORCS) was used as a testbed for the proposed controller, we have conducted two experiments with different configuration each time to test the performance of our APF- PSO controller. Results. The obtained results showed that using the APF-PSO controller resulted in good performance compared to top performing controllers. Also, the results showed that the use of PSO proved to enhance the performance compared to using APF only. High performance has been proven in the solo driving and in racing competitions, with the exception of an increased level of damage, however, the level of damage was not very high and did not result in a controller shut down. Conclusions. Based on the obtained results we have concluded that the use of PSO with APF results in high performance while taking low computational cost.

Potential Fields is an obstacle avoidance and general path-finding technique that has only quite recently started to be used in AI for video games. It has previously mainly been used in robotics for robot navigation. Although quite unexplored, Potential Fields have so far worked well in video games. Previous research has mainly focused on RTS (Real-Time Strategy) games. This research explores the use of Potential Fields in another genre called arena games (which is a quite unexplored genre as well). In the implementation, multiple Potential Fields have been used together, where each field had a different task. Also, weights were used on the different Potential Fields to give them different importance depending on some factors that are dynamic through the game, hence the use of the word weighted. The main focus of the user studies conducted was the impact the weights had on the computer controlled unit's general behaviour. The user studies conducted showed that it was hard to determine who was a computer controlled character and who was human controlled, therefore telling that multiple Potential Fields worked well for movement. The test participants became better at determining this the second match they played, no matter the properties of the match. However, the user studies did not show that the weights made a remarkable difference; there was no significant improvement on the situation adaptation and team cooperation, but no deterioration either. The concept of using weights needs to be explored further.

Η παρούσα διπλωματική ασχολείται με την πλοήγηση κινούμενου ρομπότ. Δεδομένου ενός χώρου με εμπόδια και στόχο, ασχολείται με την δημιουργία ενός αλγορίθμου για την οδήγηση του ρομπότ διαμέσου του χώρου στο στόχο, αποφεύγοντας τα εμπόδια κατά την κίνηση. Επικεντρώνεται σε δίτροχα ρομπότ και αναλύει βήμα βήμα την διαδικασία εύρεση μονοπατιού, δημιουργία τροχιάς και έλεγχο του ρομπότ. / The present thesis deals with the navigation of moving robots. Granted an area with obstacles and target, it deals with the creation of an algorithm for guiding the robot through space at target, avoiding obstacles during movement. It focuses on two-wheeled robots and analyzes step by step the process of finding a path, creating the trajectory and controlling the robot.

The Car Racing competition platform is used for evaluating different car control solutions under competitive conditions [1]. These competitions are organized as part of the IEEE Congress on Evolutionary Computation (CEC) and Computational Intelligence and Games Sym-posium (CIG). The goal is to learn and develop a controller for a car in the TORCS open source racing game [2]. Oussama Khatib [3] (1986) introduced Artificial potential fields (APFs) for the first time while looking for new ways to avoid obstacles for manipulators and mobile robots in real time. In car racing games a novel combination of artificial potential fields as the major control paradigm for car controls in a multi-agent system is being used to coordinate control interests in different scenarios [1]. Here we extend the work of Uusitalo and Stefan J. Johansson by introducing effective collision detection, overtaking maneuvers, run time evaluation and detailed analysis of the track using the concept of multi-agent artificial potential fields MAPFs. The results of our extended car controller in terms of lap time, number of damages and position in the race is improved. / We have concluded by implementing a controller that make use of multi agent based artificial potential field approach to achieve the tricky and complex task of collision detection and overtaking while driving in a car racing game with different other controllers as opponents. We exploited the advantages of APFs to the best of our knowledge using laws of physics and discrete mathematics in order to achieve successful overtaking behavior and overcome its drawbacks as being very complex to implement and high memory requirements for time critical applications e.g. car racing games (Section 3.1, RQ1). Dynamic objects in a fast changing environment like a car racing game are likely to collide more often with each other, thus resulting in higher number of damages. Using APFs instead of traditionally used collision avoidance techniques resulted in less number of damages during the race, thus minimizing the lap’s time which in turn contribute to better position in the race as shown in experiment 3 (Section 3.1, RQ2 and Section 6.6). Overtaking maneuvers are complex and tricky and is the major cause of collision among cars both in real life as well as in car racing games, thus the criteria to measure the performance regarding overtaking behavior of different controllers in the race is based on number of damages taken during the race. The comparison between the participating controllers in terms of damages taken during various rounds of the race is analyzed in experiment 3 (Section 3.1, RQ3 and Section 6.6). The results of the quick race along with opponents shows good results on three tracks while having bad performance on the remaining other track. Our controller got 1st position on the CG track 2 while kept 2nd position on CS Speed way 1 and Alpine 1. It had worse performance on wheel 2 which needs to be optimized in the future for better results on this track and other similar complex and tricky tracks. / +46723266771

Artifi cial potential fi elds is a technique that use attractive and repelling forces to control e.g. robots, or non player characters in games. We show how this technique may be used in a controller for Super Mario in a way create a human-like playing style. By combining fi elds of progression, opponent avoidance and rewards, we get a controller that tries to collect the rewards and avoid the opponents at the same time as it is progressing towards the goal of the level. We use human test persons to improve the controller further by letting them make pair-wise comparisons with human play recordings, and use the feed-back to calibrate the bot for human-like play. / Student 1: Likith Poovanna Kelapanda Staish Mob: +46735542609 Student 2: Vinay Sudha Ethiraj Mob: +46736135683