A neural network methodology for path planning and coordination of car-like robots

A car-like indoor mobile robot is a kinematically constrained robot that can be modeled as a 2D object translating and rotating in the horizontal plane among well- defined obstacles. The kinematic constraints impose that the linear velocity of the robot is along its main axis (no sideways motion is possible) and restrict the range of admissible values for the steering angle. The goal of this study is to combine neural network techniques and motion planning algorithms to create a new methodology for coordinating the motion of multiple car-like robots avoiding collision with polygonal obstacles in a work environment. An incremental technique is used to develop this methodology. First, a strategy for planning the path of a point robot moving in the presence of obstacles is constructed. Second, this strategy is adapted to path planning for a polygonal robot. Third, holonomic and non-holonomic constraints are imposed on the robot and the method is further refined. Finally, a plan for the coordinated motion of multiple car-like robots is devised through use of the concept of coordination space --Abstract, page iii

A RELIABILITY-BASED ROUTING PROTOCOL FOR VEHICULAR AD-HOC NETWORKS

Vehicular Ad hoc NETworks (VANETs), an emerging technology, would allow vehicles to form a self-organized network without the aid of a permanent infrastructure. As a prerequisite to communication in VANETs, an efficient route between communicating nodes in the network must be established, and the routing protocol must adapt to the rapidly changing topology of vehicles in motion. This is one of the goals of VANET routing protocols. In this thesis, we present an efficient routing protocol for VANETs, called the Reliable Inter-VEhicular Routing (RIVER) protocol. RIVER utilizes an undirected graph that represents the surrounding street layout where the vertices of the graph are points at which streets curve or intersect, and the graph edges represent the street segments between those vertices. Unlike existing protocols, RIVER performs real-time, active traffic monitoring and uses this data and other data gathered through passive mechanisms to assign a reliability rating to each street edge. The protocol then uses these reliability ratings to select the most reliable route. Control messages are used to identify a node’s neighbors, determine the reliability of street edges, and to share street edge reliability information with other nodes

Reciprocally-rotating Velocity Obstacles

Modern multi-agent systems frequently use high-level planners to extract basic paths for agents, and then rely on local collision avoidance to ensure that the agents reach their destinations without colliding with one another or dynamic obstacles. One state-of-the-art local collision avoidance technique is Optimal Reciprocal Colli- sion Avoidance (ORCA). Despite being fast and efficient for circular-shaped agents, ORCA may deadlock when polygonal shapes are used. To address this shortcom- ing, we introduce Reciprocally-Rotating Velocity Obstacles (RRVO). RRVO extends ORCA by introducing a notion of rotation. This extension permits more realistic motion than ORCA for polygonally-shaped agents and does not suffer from as much deadlock. In this thesis, we present the theory of RRVO and show empirically that it does not suffer from the deadlock issue ORCA has, that it permits agents to reach goals faster, and that it has a comparable collision rate at the cost of some performance overhead

Adaptive Navigation Control for Swarms of Autonomous Mobile Robots

This paper was devoted to developing a new and general coordinated adaptive navigation scheme for large-scale mobile robot swarms adapting to geographically constrained environments. Our distributed solution approach was built on the following assumptions: anonymity, disagreement on common coordinate systems, no pre-selected leader, and no direct communication. The proposed adaptive navigation was largely composed of four functions, commonly relying on dynamic neighbor selection and local interaction. When each robot found itself what situation it was in, individual appropriate ranges for neighbor selection were defined within its limited sensing boundary and the robots properly selected their neighbors in the limited range. Through local interactions with the neighbors, each robot could maintain a uniform distance to its neighbors, and adapt their direction of heading and geometric shape. More specifically, under the proposed adaptive navigation, a group of robots could be trapped in a dead-end passage,but they merge with an adjacent group to emergently escape from the dead-end passage. Furthermore, we verified the effectiveness of the proposed strategy using our in-housesimulator. The simulation results clearly demonstrated that the proposed algorithm is a simple yet robust approach to autonomous navigation of robot swarms in highlyclutteredenvironments. Since our algorithm is local and completely scalable to any size, it is easily implementable on a wide variety of resource-constrained mobile robots andplatforms. Our adaptive navigation control for mobile robot swarms is expected to be used in many applications ranging from examination and assessment of hazardous environments to domestic applications

PPCPP: A Predator-Prey-Based Approach to Adaptive Coverage Path Planning

© 2004-2012 IEEE. Most of the existing coverage path planning (CPP) algorithms do not have the capability of enabling a robot to handle unexpected changes in the coverage area of interest. Examples of unexpected changes include the sudden introduction of stationary or dynamic obstacles in the environment and change in the reachable area for coverage (e.g., due to imperfect base localization by an industrial robot). Thus, a novel adaptive CPP approach is developed that is efficient to respond to changes in real-time while aiming to achieve complete coverage with minimal cost. As part of the approach, a total reward function that incorporates three rewards is designed where the first reward is inspired by the predator-prey relation, the second reward is related to continuing motion in a straight direction, and the third reward is related to covering the boundary. The total reward function acts as a heuristic to guide the robot at each step. For a given map of an environment, model parameters are first tuned offline to minimize the path length while assuming no obstacles. It is shown that applying these learned parameters during real-time adaptive planning in the presence of obstacles will still result in a coverage path with a length close to the optimized path length. Many case studies with various scenarios are presented to validate the approach and to perform numerous comparisons

SIMULATION AND ANALYSIS OF VEHICULAR AD-HOC NETWORKS IN URBAN AND RURAL AREAS

According to the American National Highway Traffic Safety Administration, in 2010, there were an estimated 5,419,000 police-reported traffic crashes, in which 32,885 people were killed and 2,239,000 people were injured in the US alone. Vehicular Ad-Hoc Network (VANET) is an emerging technology which promises to decrease car accidents by providing several safety related services such as blind spot, forward collision and sudden braking ahead warnings. Unfortunately, research of VANET is hindered by the extremely high cost and complexity of field testing. Hence it becomes important to simulate VANET protocols and applications thoroughly before attempting to implement them. This thesis studies the feasibility of common mobility and wireless channel models in VANET simulation and provides a general overview of the currently available VANET simulators and their features. Six different simulation scenarios are performed to evaluate the performance of AODV, DSDV, DSR and OLSR Ad-Hoc routing protocols with UDP and TCP packets. Simulation results indicate that reactive protocols are more robust and suitable for the highly dynamic VANET networks. Furthermore, TCP is found to be more suitable for VANET safety applications due to the high delay and packet drop of UDP packets.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Improved Connected-Component Expansion Strategies for Sampling-Based Motion Planning

Motion planning is the problem of computing valid paths through an environment. Since computing exact solutions is intractable, sampling-based algorithms, such as Probabilistic RoadMaps (PRMs), have gained popularity. PRMs compute an approximate mapping of the planning space by sacrificing completeness in favor of efficiency. However, these algorithms have certain bottlenecks that hinder performance, causing difficulty mapping narrow or crowded regions, with the asymptotic bottleneck of these algorithms being the nearest-neighbor queries required to connect the roadmap. Thus, roadmaps may fail to efficiently capture the connectivity of the planning space. In this thesis, we present a set of connected component (CC) expansion algorithms, each with different biases (random expand, expand to the nearest CC, expand away from the host CC, and expand along the medial-axis) and expansion node selection policies (random, farthest from CC's centroid, and difficult nodes), that create a linked-chain of configurations designed to enable efficient connection of CCs. Given an a priori roadmap quality requirement in terms of roadmap connectivity, we show that when our expansion methods augment PRMs, we reach the required roadmap connectivity in less time

Estimation of Distributed Fermat-Point Location for Wireless Sensor Networking

This work presents a localization scheme for use in wireless sensor networks (WSNs) that is based on a proposed connectivity-based RF localization strategy called the distributed Fermat-point location estimation algorithm (DFPLE). DFPLE applies triangle area of location estimation formed by intersections of three neighboring beacon nodes. The Fermat point is determined as the shortest path from three vertices of the triangle. The area of estimated location then refined using Fermat point to achieve minimum error in estimating sensor nodes location. DFPLE solves problems of large errors and poor performance encountered by localization schemes that are based on a bounding box algorithm. Performance analysis of a 200-node development environment reveals that, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error remains below 1% as the node density increases. Second, when the number of beacon nodes is less than 60, normal nodes lack sufficient beacon nodes to enable their locations to be estimated. However, the mean error changes slightly as the number of beacon nodes increases above 60. Simulation results revealed that the proposed algorithm for estimating sensor positions is more accurate than existing algorithms, and improves upon conventional bounding box strategies