Real time path planning for mobile robots requires fast convergence to optimal paths. Most rapid collision free path finding algorithms do not guarantee the optimality of the path. In this paper we present a Guided Autowave Pulse Coupled Neural Network (GAPCNN) approach for mobile robot path planning. The proposed model is a novel approach that improves upon the recently presented Modified PCNN by introducing directional autowave control and accelerated firing of neurons based on a dynamic thresholding technique. Simulation and experimental evaluation in both static and dynamic environments confirm GAPCNN to be a robust and time efficient path planning scheme for finding optimal paths.

Intelligent mobile robotic agents demand optimal motion planners with minimum query time. Most contemporary algorithms lack one of these two required aspects. This paper proposes a cellular automata (CA) based efficient path planning scheme that generates optimal paths in minimum time. A Cellular automata is evolved over the entire environment and subsequently used for shortest path determination. This approach generates a parent-child relationship for each cell in order to minimize the search time. Analysis and simulation results have proven it to be a robust, complete path planning scheme and time efficient both in static and dynamic environments.

Detection of a drivable space is a key step in the autonomous control of a vehicle. In this paper we propose an adaptive vision based algorithm for road detection in diverse outdoor conditions. Our novel approach employs feature based classification and uses the Kohonen Self-Organizing Map (SOM) for the purpose of road detection. The robustness of the algorithm lies in the unique ability of SOM to organize information while learning diverse inputs. Features used for the training and testing of SOM are identified. The proposed method is capable of working with structured as well as unstructured roads and noisy environments that may be encountered by an intelligent vehicle. The proposed technique is extensively compared with the k-Nearest Neighbor (KNN) algorithm. Results show that SOM outperforms KNN in classification consistency and is independent to the lighting conditions while taking comparable classification time which shows that the network can also be used as an online learning architecture.

In this paper we present the first comparative study of evolutionary classifiers for the problem of road detection. We use seven evolutionary algorithms (GAssist-ADI, XCS, UCS, cAnt, EvRBF,Fuzzy-AB and FuzzySLAVE) for this purpose and to develop better understanding we also compare their performance with two well-known non-evolutionary classifiers (kNN, C4.5). Further we identify vision based features that enable a single classifier to learn to successfully classify a variety of regions in various roads as opposed to training a new classifier for each type of road. For this we collect a real-world dataset of road images of various roads taken at different times of the day. Then, using Information Gain (I.G) and CfsSubsetMerit values we evaluate the efficacy of our features in facilitating the detection. Our results indicate that intelligent features coupled with right evolutionary technique provides a promising solution for the domain of road detection.

Autonomous steering control is the principal task in the development of an intelligent transportation system. This research paper proposes a novel approach for vision based intelligent control of unmanned vehicles. The paper addresses the problems of accurate and efficient intelligent vehicle control by incorporating a well known evolutionary algorithm cAnt-Miner. The uniqueness of the proposed algorithm lies in its ability to accurately steer the vehicle on structured and unstructured roads in real-time, without requiring meticulous calculation. The algorithm first uses the road edges to evaluate the direction of the path through classification via cAnt-Miner. Subsequently, it steers the vehicle according to the particular direction using fuzzy logic controller. The synergy of the evolutionary algorithm, cAnt-Miner, for trajectory mining with fuzzy logic control enables autonomous steering with high efficacy. Experiments were carried out on our university's Intelligent DRIving System (IDRIS). Experimental results show that the proposed algorithm can steer the car automatically in real-time.

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.

This paper presents a novel method for the time-optimal interception of moving objects in the presence of obstacles. The proposed method not only provides time-optimal positional interception, but, also simultaneously matches the velocity at which the object is moving in a dynamic environment with static and/or moving obstacles: A rendezvous-guidance technique is utilized in conjunction with a modified cell-decomposition method. Simulation and experimental results clearly demonstrate the efficiency of the proposed interception method.

This paper presents a novel method for the time-optimal rendezvous of an autonomous vehicle with moving targets in the presence of dynamic obstacles. This interception problem is addressed by utilizing the velocity obstacle approach of Fiorni and Shiller augmented with a rendezvous-guidance algorithm. However, the velocity obstacle approach had to be modified to ensure that the real-time planned path deviates minimally from the one generated by the rendezvous-guidance algorithm. Simulation and experimental results, some of which are presented in this paper, clearly demonstrate the efficiency of the proposed interception method.