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Many robotic systems need to interact with and manipulate their environments. Tasks include picking objects up, manipulating controls such as door handles, or pushing objects to one side. To achieve these interactions, a robot needs to find suitable positions and orientations of the robot’s body and arm(s), selecting appropriate manipulation synergies and control strategies, and sensing throughout the interaction process. This module will explain the fundamentals behind computation, control and sensing procedures in robotic manipulation and interaction tasks. It builds upon the core modules Modelling and Motion Planning and Robot Vision and Sensing and extends the material from both to specifically address task manipulation problems. Ìý

Aims:

The aims of this module are to:

• Provide students with a strong understanding of the computational and sensing aspects of robotic systems that interact with environment while performing actions.
• Extend students’ skills in sensing and mapping robots to include interaction and the special manipulation this entails.
• Support students in their in-depth exploration of the sensory systems used to capture information and in turn develop models that enable robots to cooperate with its environment in different ways.

Intended learning outcomes:

On successful completion of the module, a student will be able to:

1. On successful completion of this module, a student will be able to:
2. Describe key concepts related to robotic manipulation and sensing, and effectively articulate their complexities.
3. Develop methods for tackling uncertainty in robotic manipulation systems.Ìý
4. Apply theoretical knowledge from scientific literature in robotics to choose approaches for a particular problem linked to manipulation and interaction.Ìý
5. Implement state-of-the-art algorithms on simulated manipulators and sensors

Indicative content:

The following are indicative of the topics the module will typically cover:

• Coordinate frames.
• Forward and inverse kinematics.
• Manipulatibility (Jacobians)
• Graph-based search algorithms (A*, Dijkstra.)
• Rapidly exploring random trees.
• Kinodynamic planning.
• Smoothing algorithms including time elastic band.
• Energy efficiency.
• Ethics and societal impacts.

Requisite conditions:

To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.