Mobile robot

Résumé

A mobile robot is an automatic machine that is capable of locomotion. Mobile robotics is usually considered to be a subfield of robotics and information engineering. Mobile robots have the capability to move around in their environment and are not fixed to one physical location. Mobile robots can be "autonomous" (AMR - autonomous mobile robot) which means they are capable of navigating an uncontrolled environment without the need for physical or electro-mechanical guidance devices. Alternatively, mobile robots can rely on guidance devices that allow them to travel a pre-defined navigation route in relatively controlled space. By contrast, industrial robots are usually more-or-less stationary, consisting of a jointed arm (multi-linked manipulator) and gripper assembly (or end effector), attached to a fixed surface. The joint. Mobile robots have become more commonplace in commercial and industrial settings. Hospitals have been using autonomous mobile robots to move materials for m

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https://en.wikipedia.org/wiki/Mobile_robot

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We present a system for interaction between co-located humans and mobile robots, which uses pointing gestures sensed by a wrist-mounted IMU. The operator begins by pointing, for a short time, at a moving robot. The system thus simultaneously determines: that the operator wants to interact; the robot they want to interact with; and the relative pose among the two. Then, the system can reconstruct pointed locations in the robot's own reference frame, and provide real-time feedback about them so that the user can adapt to misalignments. We discuss the challenges to be solved to implement such a system and propose practical solutions, including variants for fast flying robots and slow ground robots. We report different experiments with real robots and untrained users, validating the individual components and the system as a whole.

2019

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Piezoelectric Grippers for Mobile Micromanipulation

Tristan Abondance, Kaushik Jayaram

The ability to efficiently and precisely manipulate objects in inaccessible environments is becoming an essential requirement for many applications of mobile robots, particularly at small sizes. Here, we propose and implement a mobile micromanipulation solution using a piezoelectric microgripper integrated into a dexterous robot, HAMR (the Harvard Ambulatory MicroRobot), that has a size of approximately 4.5 cm by 4 cm by 2.3 cm and a maximum payload of approximately 3 g. Our 100 mg miniature gripper is composed of recurve piezoelectric actuators that produce parallel jaw motions (stroke of 236 +/- 32 mu m at 200 V) while providing high gripping forces (blocked force of 0.575 N at 200 V), making it effective for micromanipulation applications with tiny objects. Using this gripper, we successfully demonstrated a grasping and lifting task with an object of 1.3 g and thickness of 250 mu m at an operating voltage of 100 V. Finally, by taking advantage of the locomotion capabilities ofHAMR, wedemonstrate mobile manipulation by changing the position and orientation of small objects weighing up to 2.8 g controlled by the movement of the robot. We expect that the addition of this novel manipulation capability will increase the effectiveness of such miniature robots for accomplishing real-world tasks.

2020

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We propose a novel approach to establish the relative pose of a mobile robot with respect to an operator that wants to interact with it; we focus on scenarios in which the robot is in the same environment as the operator, and is visible to them. The approach is based on comparing the trajectory of the robot, which is known in the robot's odometry frame, to the motion of the arm of the operator, who, for a short time, keeps pointing at the robot they want to interact with. In multi-robot scenarios, the same approach can be used to simultaneously identify which robot the operator wants to interact with. The main advantage over alternatives is that our system only relies on the robot's odometry, on a wearable inertial measurement unit (IMU), and, crucially, on the operator's own perception. We experimentally show the feasibility of our approach using real-world robots.

2019

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