Edda Technology, Inc.

Yuanfeng Mao

Jianzhong Qian

Guo-Qing Wei

Li Fan

Xiaolan Zeng

Firdous Saleheen

Yash Evalekar

The present teaching relates to automated trocar/robot base location determination. An input relates to a surgical operation with a 3D model for an organ including cut points thereon forming a surgical trajectory. A surgical instrument is controlled by a robot to reach the cut points to carry out the surgical operation. Candidate combinations of insertion location for inserting the surgical instrument and base location for deploying the robot are generated. One of the candidate combinations is identified based on evaluation vectors associated therewith. Each evaluation vector provides assessment information on characteristics and spatial relationships of the respective candidate locations. The selected combination of locations is used for the surgical operation.

System and method for automated simultaneous trocar and robot base location determination

The present teaching relates to surgical position marking. A 3D model for an organ includes cut points forming a surgical trajectory. Each cut point has a 3D coordinate and a surface norm in the model space. When projected into a workspace, a mapped cut point is created with a mapped 3D coordinate and a mapped surface norm in the workspace. With a surgical instrument with a tip and a force sensor attached thereto, some mapped cut points are marked along a direction determined based on a reaction force sensed by the force sensor when the tip touches the cut point.

System and method for automated surgical position marking in robot-assisted surgery

The present teaching relates to a method and system for path planning. A target is tracked via one or more sensors. Information of a desired pose of an end-effector with respect to the target and a current pose of the end-effector is obtained. Also, a minimum distance permitted between an arm including the end-effector and each of at least one obstacle identified between the current pose of the end-effector and the target is obtained. A weighting factor previously learned is retrieved and a cost based on a cost function is computed in accordance with a weighted smallest distance between the arm including the end-effector and the at least one obstacle, wherein the smallest distance is weighted by the weighting factor. A trajectory is computed from the current pose to the desired pose by minimizing the cost function.

Deterministic robot path planning method for obstacle avoidance

The present teaching relates to automated trocar/robot base location determination. An input related to a surgical operation includes a three-dimensional (3D) model for an organ with cut points on the organ forming a surgical trajectory. A surgical instrument is controlled by a robot to reach the cut points to carry out the operation. An insertion location for inserting the surgical instrument is identified. Based on the identified insertion location, a robot base location is determined with respect to the identified insertion location to deploy the robot for controlling the surgical instrument to reach the cut points. Signals for configuring the surgical setting are then generated based on the insertion location for the surgical instrument and the base location for the robot.

System and method for automated trocar and robot base location determination

The present teaching relates to a method and system for path planning. Information of a current pose of a robotic arm having a plurality of operable segments is obtained. The information includes a plurality of values, each of which corresponds to an angle formed between consecutive operable segments of the robotic arm. A desired pose where the robotic arm needs to reach is also obtained. An angle step-value is computed for the current pose of the robotic arm based on a function of a distance between the current pose and the desired pose, wherein the angle step value is to be used to determine a plurality of candidate next poses of the plurality of operable segments. One or more of candidate next poses is selected based on at least one criterion, and a trajectory is determined from the current pose to the desired pose based on the selected next poses.

Fast method for robot path planning with obstacle avoidance

The present teaching relates to method, system, medium, and implementations for robot path planning. Depth data of obstacles, acquired by depth sensors deployed in a 3D robot workspace and represented with respect to a sensor coordinate system, is transformed into depth data with respect to a robot coordinate system. The 3D robot workspace is discretized to generate 3D grid points representing a discretized 3D robot workspace. Based on the depth data with respect to the robot coordinate system, binarized values are assigned to at least some of 3D grid points to generate a binarized representation for the obstacles present in the 3D robot workspace. With respect to one or more sensing points associated with a part of a robot, it is determined whether the part is to collide with any obstacle. Based on the determining, a path is planned for the robot to move along while avoiding any obstacle.

Method and system for obstacle avoidance in robot path planning using depth sensors

The present disclosure relates to robot path planning. Depth information of a plurality of obstacles in an environment of a robot are obtained at a first time instance. A static distance map is generated based on the depth information. A path is computed for the robot based on the static distance map. At a second time instant, depth information of one or more obstacles is obtained. A dynamic distance map is generated based on the one or more obstacles, wherein for each obstacle that satisfies a condition: a vibration range of the obstacle is computed based on a position of the obstacle and the static distance map, and the obstacle is classified as a dynamic obstacle or a static obstacle based on a criterion associated with the vibration range. A repulsive speed of the robot is computed based on the dynamic distance map to avoid the dynamic obstacles.

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Mt. Laurel, NJ, United States of America

Yuanfeng Mao