A control system may receive a first plurality of measurements indicative of respective joint angles corresponding to a plurality of sensors connected to a robot. The robot may include a body and a plurality of jointed limbs connected to the body associated with respective properties. The control system may also receive a body orientation measurement indicative of an orientation of the body of the robot. The control system may further determine a relationship between the first plurality of measurements and the body orientation measurement based on the properties associated with the jointed limbs of the robot. Additionally, the control system may estimate an aggregate orientation of the robot based on the first plurality of measurements, the body orientation measurement, and the determined relationship. Further, the control system may provide instructions to control at least one jointed limb of the robot based on the estimated aggregate orientation of the robot.

The present invention relates to safety in a dynamic 3D healthcare environment. The invention in particular relates to a medical safety-system for dynamic 3D healthcare environments, a medical examination system with motorized equipment, an image acquisition arrangement, and a method for providing safe movements in dynamic 3D healthcare environments. In order to provide improved safety in dynamic 3D healthcare environments with a facilitated adaptability, a medical safety-system (10) for dynamic 3D healthcare environments is provided, comprising a detection system (12), a processing unit (14), and an interface unit (16). The detection system comprises at least one sensor arrangement (18) adapted to provide depth information of at least a part of an observed scene (22). The processing unit comprises a correlation unit (24) adapted to correlate the depth information. The processing unit comprises a generation unit (26) adapted to generate a 3D free space model (32). The interface unit is adapted to provide the 3D free space model.

Certain aspects relate to systems and techniques for detection of undesirable forces on one or more surgical robotic arms. In one aspect, there is provided a system including a robotic arm, including: two linkages, a joint, a torque sensor, and an instrument device manipulator (IDM). The system may further include a processor configured to measure a first torque value at the joint based on an output of the torque sensor and determine a second torque value at the joint based on a position of the robotic arm. The second torque value may be indicative of a gravitational component of the torque between the two linkages. The processor may be further configured to determine a force at the IDM based a difference between the first and second torque values and determine whether the robotic arm has collided with an object or misaligned based on the force at the IDM.

Embodiments are generally directed to generating a training dataset of labelled examples of sensor images and grasp configurations using a set of three-dimensional (3D) models of objects, one or more analytic mechanical representations of either or both of grasp forces and grasp torques, and statistical sampling to model uncertainty in either or both sensing and control. Embodiments can also include using the training dataset to train a function approximator that takes as input a sensor image and returns data that is used to select grasp configurations for a robot grasping or targeting mechanism.

A mobile robot is configured for operation in a commercial or industrial setting, such as an office building or retail store. The robot can patrol one or more routes within a building, and can detect violations of security policies by objects, building infrastructure and security systems, or individuals. In response to the detected violations, the robot can perform one or more security operations. The robot can include a removable fabric panel, enabling sensors within the robot body to capture signals that propagate through the fabric. In addition, the robot can scan RFID tags of objects within an area, for instance coupled to store inventory. Likewise, the robot can generate or update one or more semantic maps for use by the robot in navigating an area and for measuring compliance with security policies.

A self-learning robot, according to one embodiment of the present invention, comprises: a data receiving unit for sensing video data or audio data relating to an object located within a predetermined range; a data recognition unit for matching data received from the data receiving unit and data included in a database in the self-learning robot; a result output unit for outputting a matching result from the data recognition unit; a recognition result verifying unit for determining the accuracy of the matching result; a server communication unit for transmitting data received from the data receiving unit to a server, when the accuracy of the matching result determined by the recognition result verifying unit is lower than a predetermined level; and an action command unit for causing the self-learning robot to perform a pre-set object response action, when the accuracy of the matching result determined by the recognition result verifying unit is at least the predetermined level.

Certain aspects relate to systems and techniques for detection of undesirable forces on one or more surgical robotic arms. In one aspect, there is provided a system including a robotic arm, including: two linkages, a joint, a torque sensor, and an instrument device manipulator (IDM). The system may further include a processor configured to measure a first torque value at the joint based on an output of the torque sensor and determine a second torque value at the joint based on a position of the robotic arm. The second torque value may be indicative of a gravitational component of the torque between the two linkages. The processor may be further configured to determine a force at the IDM based a difference between the first and second torque values and determine whether the robotic arm has collided with an object or misaligned based on the force at the IDM.

In order to detect when a kinematic linkage leaves workspaces and/or enters safe spaces, using little computing power, and therefore doing so more quickly, at least a part of the kinematic linkage is modeled with a number of kinematic objects, and a monitoring space is specified. The number of kinematic objects is modeled in less than two dimensions D<2. For each modeled kinematic object, a geometric variable of a monitoring space is modified by a distance. Each distance is derived from at least one geometric parameter of the modeled kinematic object. The position of each of the number of kinematic objects is checked in relation to the modified monitoring spaces.

Techniques relate to determining a region associated with an object to assist in controlling a robotic arm. For example, a system can determine that the robotic arm is positioned adjacent to an object within an environment. The system can determine a region in the environment that is associated with the object based at least in part on a position of a distal end of the robotic arm. The system can control the robotic arm or another robotic arm to move in the environment based at least in part on the region.

A robot includes a robot body including a base, a first movable section provided turnably with respect to the base, and a second movable section provided turnably with respect to the first movable section, a first proximity sensor for detecting contact with or approach of an object to the first movable section, and a second proximity sensor for detecting contact with or approach of the object to the second movable section. The first proximity sensor includes a first electrode section having a capacitance that changes according to the contact or approach of the object, and a first circuit section for detecting the capacitance of the first electrode section. The second proximity sensor includes a second electrode section having a capacitance that changes according to the contact or approach of the object, and a second circuit section for detecting the capacitance of the second electrode section.