A Historical Overview of Artificial Intelligence in China

Artificial intelligence (AI) refers to the interdisciplinary field of study that involves the development of computer systems and machines capable of performing tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI has undergone a tumultuous developmental trajectory since its inception as a distinct field of study in 1956. This paper provides a concise review of the historical development of AI in China, with a particular focus on the country’s recent advancements in AI research, innovation, and application

Collision-free motion of two robot arms in a common workspace

Collision-free motion of two robot arms in a common workspace is investigated. A collision-free motion is obtained by detecting collisions along the preplanned trajectories using a sphere model for the wrist of each robot and then modifying the paths and/or trajectories of one or both robots to avoid the collision. Detecting and avoiding collisions are based on the premise that: preplanned trajectories of the robots follow a straight line; collisions are restricted to between the wrists of the two robots (which corresponds to the upper three links of PUMA manipulators); and collisions never occur between the beginning points or end points on the straight line paths. The collision detection algorithm is described and some approaches to collision avoidance are discussed

Towards building a team of intelligent robots

Topics addressed include: collision-free motion planning of multiple robot arms; two-dimensional object recognition; and pictorial databases (storage and sharing of the representations of three-dimensional objects)

A Robot Navigation Algorithm for Moving Obstacles

In recent years, considerable progress has been made towards the development of intelligent autonomous mobile robots which can perform a wide variety of tasks. Although the capabilities of these robots vary significantly, each must have the ability to navigate within its environment from a starting location to a goal without experiencing collisions with obstacles in the process - a capability commonly referred to as robot navigation . Numerous algorithms for robot navigation have been developed which allow the robot to operate in static environments. However, little work has been accomplished in the development of algorithms which allow the robot to navigate in a dynamic environment. This thesis presents a mathematically-based navigation algorithm for a robot operating in a continuous-time environment inhabited by moving obstacles whose trajectories and velocities can be detected. In this methodology, the obstacles are represented as sheared cylinders to depict the areas swept out by the obstacle disks of influence over time. The robot is represented by the cone of positions it can reach by traveling at a constant speed in any direction. The methodology utilizes a three-dimensional navigation planning approach in which the search points, or tangent points, are the points in time at which the robot tangentially meets the obstacles. These tangent points are determined by calculating the intersection curves between the robot and the obstacles, and then using the first derivative of the intersection curves to make the tangent selections. Paths are created as sequences of these tangent points leading from the robot starting location to the goal, and are searched using the A* strategy, with a heuristic of the Euclidean distance from the tangent point to the goal. The main contribution of this thesis is the development of a methodology which produces optimal tangent paths to the goal for a dynamic robot environment. This feature is significant, since no other algorithm located in the literature survey as background to this thesis has shown the ability to produce paths with optimal properties

Robotics handbook. Version 1: For the interested party and professional

This publication covers several categories of information about robotics. The first section provides a brief overview of the field of Robotics. The next section provides a reasonably detailed look at the NASA Robotics program. The third section features a listing of companies and organization engaging in robotics or robotic-related activities; followed by a listing of associations involved in the field; followed by a listing of publications and periodicals which cover elements of robotics or related fields. The final section is an abbreviated abstract of referred journal material and other reference material relevant to the technology and science of robotics, including such allied fields as vision perception; three-space axis orientation and measurement systems and associated inertial reference technology and algorithms; and physical and mechanical science and technology related to robotics

Real-time path planning for robot arms.

SIGLEAvailable from British Library Document Supply Centre- DSC:D80326 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

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