Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

物件導向軟體之架構(Object-Oriented Software Construction)探討

https://web.cs.wpi.edu/~rail/files/ArgallEtAlRAS09.pdf

A survey of robot learning from demonstration

Also referred to as learning by imitation, tutelage, or apprenticeship learning, Programming by Demonstration (PbD) develops methods by which new skills can be transmitted to a robot. This book examines methods by which robots learn new skills through human guidance. Taking a practical perspective, it covers a broad range of applications, including service robots. The text addresses the challenges involved in investigating methods by which PbD is used to provide robots with a generic and adaptive model of control. Drawing on findings from robot control, human-robot interaction, applied machine learning, artificial intelligence, and developmental and cognitive psychology, the book contains a large set of didactic and illustrative examples. Practical and comprehensive machine learning source codes are available on the books companion website: http://www.programming-by-demonstration.org

Robot Programming by Demonstration

A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches.

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

Robust trajectory learning and approximation for robot programming by demonstration

This paper describes a virtual reality based programming by demonstration system for grasp recognition in manipulation tasks and robot pregrasp planning. The system classifies the human hand postures taking advantage of virtual grasping and information about the contact points and normals computed in the virtual reality environment. A pregrasp planning algorithm mimicking the human hand motion is also proposed. Reconstruction of human hand trajectories, approaching the objects in the environment, is based on NURBS curves and a data smoothing algorithm. Some experiments involving grasp classification and pregrasp planning, while avoiding obstacles in the workspace, show the viability and effectiveness of the approach

Grasp recognition in virtual reality for robot pregrasp planning by demonstration

Leveraging on a virtual environment for robot programming by demonstration

In this paper, we investigate the potential of vision-based object detection algorithms in underwater environments using several datasets to highlight the issues arising in different scenarios. Und...

https://journals.sagepub.com/doi/pdf/10.5772/60526

Investigation of Vision-Based Underwater Object Detection with Multiple Datasets

A method for robot to human object hand-over is presented that takes into account user comfort. Comfort is addressed by serving the object to facilitate user's convenience. The object is delivered so that the most appropriate part is oriented towards the person interacting with the robot. This approach, aimed at contributing to the development of socially aware robots, has not been considered in previous works. The robot system also supports sensory-motor skills like object and people detection, robot grasping and motion planning. The experimental setup consists of a six degrees of freedom robot arm with both an eye-in-hand laser scanner and a fixed range sensor. The user interacting with the robot can assume an arbitrary position in front of the robot. Experiments are reported from a user study.

Stefano Caselli

Papers

Robust trajectory learning and approximation for robot programming by demonstration

Grasp recognition in virtual reality for robot pregrasp planning by demonstration

Leveraging on a virtual environment for robot programming by demonstration

Investigation of Vision-Based Underwater Object Detection with Multiple Datasets

Comfortable robot to human object hand-over