Artificial Potential Field Simulation Framework for Semi-Autonomous Car Conception

Artificial potential field is investigated to provide a high level of synergy between driver and semiautonomous vehicle. This article presents a framework developed to test the performances of this approach. Stand-alone performances of this system is tested for a lane keeping and cruise control application. Performances are promising and future development is discussed

Collision-free path planning

Motion planning is an important challenge in robotics research. Efficient generation of collision-free motion is a fundamental capability necessary for autonomous robots;In this dissertation, a fast and practical algorithm for moving a convex polygonal robot among a set of polygonal obstacles with translations and rotations is presented. The running time is O(c((n + k)N + nlogn)), where c is a parameter controlling the precision of the results, n is the total number of obstacle vertices, k is the number of intersections of configuration space obstacles, and N is the number of obstacles, decomposed into convex objects. This dissertation exploits a simple 3D passage-network to incorporate robot rotations as an alternative to complex cell decomposition techniques or building passage networks on approximated 3D C-space obstacles;A common approach in path planning is to compute the Minkowski difference of a polygonal robot model with the polygonal obstacle environment. However such a configuration space is valid only for a single robot orientation. In this research, multiple configuration spaces are computed between the obstacle environment and the robot at successive angular orientations spanning [pi] . Although the obstacles do not intersect, each configuration space may contain intersecting configuration space obstacles (C-space obstacles). For each configuration space, the algorithm finds the contour of the intersected C-space obstacles and the associated passage network by slabbing the collision-free space. The individual configuration spaces are then related to one another by a heuristic called proper links that exploit spatial coherence. Thus, each level is connected to the adjacent levels by proper links to construct a 3D network. Dijkstra\u27s algorithm is used to search for the shortest path in the 3D network. Finally, the path is projected onto the plane to show the final locus of the path

Motion planning for mobile robots in unknown environments with real time configuration space construction.

by Wong Hon-chuen.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 83-87).Abstracts in English and Chinese.Acknowledgements --- p.iList of Figures --- p.vList of Table --- p.viiiAbstract --- p.ixContentsChapter 1 --- Introduction --- p.1Chapter 2 --- Algorithm Outline --- p.7Chapter 2.1 --- Assumptions --- p.7Chapter 2.2 --- Algorithm Outline --- p.8Chapter 3 --- Obstacle Detection --- p.11Chapter 3.1 --- Introduction --- p.11Chapter 3.2 --- Image Processing --- p.14Chapter 3.3 --- Coordinate Transformation --- p.14Chapter 3.4 --- Example --- p.20Chapter 4 --- Real-time Construction of Configuration Space --- p.22Chapter 4.1 --- Introduction --- p.22Chapter 4.2 --- Configuration Space --- p.23Chapter 4.3 --- Type-A Contact --- p.26Chapter 4.4 --- Type-B Contact --- p.27Chapter 4.5 --- Inverse Mapping Method --- p.29Chapter 4.6 --- Simulation --- p.31Chapter 5 --- Motion Planning and Re-Construction of C-space --- p.34Chapter 5.1 --- Introduction --- p.34Chapter 5.2 --- Path Planning --- p.36Chapter 5.3 --- Update of C-space --- p.41Chapter 5.4 --- Re-planning of Robot Path --- p.44Chapter 6 --- Implementation and Experiments --- p.55Chapter 6.1 --- Introduction --- p.55Chapter 6.2 --- Architecture of the Mobile Robot System --- p.55Chapter 6.3 --- Algorithm Implementation --- p.56Chapter 6.4 --- Experiment --- p.58Chapter 6.4.1 --- Experiment on a Fixed Unknown Environment --- p.58Chapter 6.4.2 --- Experiment on a Dynamic Unknown Environment --- p.70Chapter 7 --- Conclusions --- p.81References --- p.8

Path planning for robotic truss assembly

A new Potential Fields approach to the robotic path planning problem is proposed and implemented. Our approach, which is based on one originally proposed by Munger, computes an incremental joint vector based upon attraction to a goal and repulsion from obstacles. By repetitively adding and computing these 'steps', it is hoped (but not guaranteed) that the robot will reach its goal. An attractive force exerted by the goal is found by solving for the the minimum norm solution to the linear Jacobian equation. A repulsive force between obstacles and the robot's links is used to avoid collisions. Its magnitude is inversely proportional to the distance. Together, these forces make the goal the global minimum potential point, but local minima can stop the robot from ever reaching that point. Our approach improves on a basic, potential field paradigm developed by Munger by using an active, adaptive field - what we will call a 'flexible' potential field. Active fields are stronger when objects move towards one another and weaker when they move apart. An adaptive field's strength is individually tailored to be just strong enough to avoid any collision. In addition to the local planner, a global planning algorithm helps the planner to avoid local field minima by providing subgoals. These subgoals are based on the obstacles which caused the local planner to fail. A best-first search algorithm A* is used for graph search

Wireless mosaic eyes based robot path planning and control. Autonomous robot navigation using environment intelligence with distributed vision sensors.

As an attempt to steer away from developing an autonomous robot with complex centralised intelligence, this thesis proposes an intelligent environment infrastructure where intelligences are distributed in the environment through collaborative vision sensors mounted in a physical architecture, forming a wireless sensor network, to enable the navigation of unintelligent robots within that physical architecture. The aim is to avoid the bottleneck of centralised robot intelligence that hinders the application and exploitation of autonomous robot. A bio-mimetic snake algorithm is proposed to coordinate the distributed vision sensors for the generation of a collision free Reference-snake (R-snake) path during the path planning process. By following the R-snake path, a novel Accompanied snake (A-snake) method that complies with the robot's nonholonomic constraints for trajectory generation and motion control is introduced to generate real time robot motion commands to navigate the robot from its current position to the target position. A rolling window optimisation mechanism subject to control input saturation constraints is carried out for time-optimal control along the A-snake. A comprehensive simulation software and a practical distributed intelligent environment with vision sensors mounted on a building ceiling are developed. All the algorithms proposed in this thesis are first verified by the simulation and then implemented in the practical intelligent environment. A model car with less on-board intelligence is successfully controlled by the distributed vision sensors and demonstrated superior mobility