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Complete Coverage Path Planning (CCPP) is the process of finding a path that passes through all the accessible positions within the environment. CCPP is inherent to many real world robotic applications such as cleaning, agriculture, painting, lawn mowing, underwater operations, mine sweeping, search and rescue operations. The total operational time required for performing coverage is significantly influenced by total number of turns, optimization of backtracking sequence, and incorporating smoothness in the complete coverage path. A considerate amount of operational time is required by the backtracking sequence optimization algorithm in CCPP. The problem becomes evident when real world applications are involved. This thesis proposes two new methods to maximize coverage efficiency by first improvising current state of the art backtracking sequence optimization scheme and later generating smooth coverage path for feasible trajectory following by the robots. An efficient complete coverage path planning technique, online Boustrophedon using Two-way Proximity Search (B-TWPS) is proposed to minimize the total operational time, memory requirements and total coverage path length. The proposed algorithm first performs online boustrophedon motion until a critical point is detected. An intelligent backtracking point detection strategy is used, that efficiently detects all the backtracking points. The nearest point is selected as the next starting point by the algorithm. An optimal path from the critical point to the new starting point is computed using TWPS. The proposed online coverage technique is tested and validated in Matlab simulations. The proposed complete coverage path planning algorithm not only generated shorter complete coverage paths but also reduced the memory requiremens by 79.21% and total computational time requirements upto 97.49%. The current state of the art CCPP algorithms generate rectilinear coverage path comprising of straight lines with sharp turns. Such paths are infeasible for nonxi holonomic mobile robots, as the robot has to decelerate and reorient to follow the rectilinear path. A computationally efficient smoothness approach using rational conic Bezier spline is proposed. The proposed smoothness approach ensures ob- ´ stacle avoidance by automatically adjusting weight parameter. The proposed approach ensures suitable level of smoothness for slow speed non-holonomic mobile robots. The proposed coverage path smoothing algorithm reduced total coverage path length by 18.96% and total operational time upto 45.59% as compared to traditional rectilinear coverage path planning algorithms.
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