ABSTRACT
Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.
ABSTRACT
The use of somatosensory evoked potentials (SEPs) in localizing the level, extent, and laterality of nerve root entrapment is clinically important. In patients with lumbar spinal stenosis, this is especially true. This study defines a prospective investigation of 20 patients with preoperative SEPs of which 11 patients had intraoperative SEPs correlated with their computed tomographic (CT) scan and/or myelographic findings. The results confirm a high incidence of 4th and 5th lumbar and 1st sacral nerve root involvement. The posterior tibial nerve was abnormal in 95%, the peroneal in 90%, and the sural in 60% in the symptomatical lower extremity. Upper lumbar segments were barely affected as evident by the low incidence of saphenous nerve abnormality in only 12% of the patients. The posterior tibial nerve had the highest yield and was useful for screening. Bilateral lower extremity abnormalities were found in seven of 20 cases studied with two patients having bilateral symptoms and findings. Therefore, bilateral lower extremity SEP evaluation can reveal previously unsuspected pathology and is strongly recommended in preoperative evaluations. SEPs can serve as a useful intraoperative tool to guide the surgeon during a decompressive surgical procedure. SEPs are specifically helpful in spinal stenosis with a paucity of clinical findings and equivocal CT scan or myelographic studies. SEPs seem much more sensitive and effective than conventional electrodiagnostic tests in detecting spinal nerve root compression secondary to spinal stenosis.
