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A purely geometric planning method for a mobile robot in unknown environments is proposed to ensure collision avoidance with obstacles within the safety time interval while moving toward the goal. The robot initially detects a point cloud of obstacles using a 2D LiDAR. Euclidean clustering is employed to classify the point cloud into distinct point classes. Each point class is then identified as a directed closed-loop rectangle representing the obstacle. A relative orientation k d-tree is designed to store the vertices of the obstacles and determine which obstacles should be considered in the obstacle avoidance algorithm. A velocity divider is introduced to obtain a linear convex area of possible obstacle avoidance velocities. Linear planning is then used to calculate the optimal obstacle avoidance velocity for control. A virtual reference point method is proposed to address the problem of an unreachable goal in a singular configuration. Experimental results show that the directed closed-loop rectangle and relative orientation k d-tree facilitate rapid updates of required obstacle points with low-cost sensor equipment. A deterministic path for a given scenario is demonstrated, confirming the reliability of the geometric planning method for the obstacle avoidance velocity region and the optimal obstacle avoidance velocity. The proposed algorithm is further validated in scenarios with multiple obstacles and dynamic environments involving moving obstacles. [ABSTRACT FROM AUTHOR]

A segmented hyper-redundant manipulator can perform complicated operation tasks in a confined space due to its high flexibility and dexterity. However, the trajectory planning in a narrow space and obstacles environment is very challenging for the manipulator. In this paper, we propose a geometry method to simultaneously plan the end-effector pose and manipulator's configuration. Firstly, the geometries of each segment are described by an inscribed arc (IA) and a circumscribed arc (CA). Then, the whole kinematics chain is considered as an inscribed curve (IC) or a circumscribed curve (CC) which are composed of multiple IAs or CAs. Furthermore, the IC and CC of the manipulator are divided into multiple spatial single-arc and double-arc groups according to requirements. The pose-configuration simultaneous planning is realized by the spatial single-arc/double-arc modeling and joints angles solving. By numerical iteration, the spatial arcs' parameters are determined according to desired pose and boundary condition of obstacle avoidance. The angles of joints are analytically solved when the above parameters are known. Finally, a narrow space detection task is simulated and experimented respectively. The results verify the proposed method. [ABSTRACT FROM AUTHOR]

Objective To explore decision support systems (DSS) used in spine-pelvic surgery, analyze its functional possibilities and approaches that allow the surgeon to make a correct decision. Material and methods Functional possibilities with modern DSSs used in surgery of spine-pelvic complex were reviewed with unified criteria using websites of DSS manufacturers and publications in scientific journals. Results MediCAD, TraumaCAD, Surgimap, Sectra AB and OrthoView are most common DSSs used for spine and hip surgery planning. The above systems can be applied in orthopedic surgery of several independent anatomical regions (e.g., spine, pelvis, femur, tibia, foot). But none of the systems can be applied to spine-pelvic complex. DSS facilitates only geometrical planning with geometric measurements, simulation of physiologically normal location of anatomical elements as well as selection and semi-automatic implant positioning. Conclusion Both geometrical planning and biomechanical simulation are required to achieve positive long-term follow-up of surgical treatment. Biomechanical simulation allows assessment of an extent and pattern of injury caused by malalignment of spine-pelvic complex and surgical intervention planning with the help of reconstruction options offered. The use of DSS should involve geometric planning, biomechanical simulation of the expected surgical outcome and prediction of a long-term follow-up. Introduction of DSS into clinical practice will facilitate the quality of medical care and rehabilitation with concurrent optimization of the national expenditure on health care.