Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Integer and combinatorial optimization
Integer and combinatorial optimization
Autonomous robot vehicles
Linear network optimization: algorithms and codes
Linear network optimization: algorithms and codes
Motion planning for a steering-constrained robot through moderate obstacles
STOC '95 Proceedings of the twenty-seventh annual ACM symposium on Theory of computing
Robot Motion Planning
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The problem of finding a path for the motion of a small mobile robot froma starting point to a fixed target in a two dimensional domain is consideredin the presence of arbitrary shaped obstacles. No a priori information isknown in advance about the geometry and the dimensions of the workspace norabout the number, extension and location of obstacles. The robot has asensing device that detects all obstacles or pieces of walls lying beyond afixed view range. A discrete version of the problem is solved by aniterative algorithm that at any iteration step finds the smallest pathlength from the actual point to the target with respect to the actualknowledge about the obstacles, then the robot is steered along the pathuntil a new obstacle point interfering with the path is found, at this pointa new iteration is started. Such an algorithm stops in a number of stepsdepending on the geometry, finding a solution for the problem or detectingthat the problem is unfeasible. Since the algorithm must be applied on line,the effectiveness of the method depends strongly on the efficiency of theoptimization step. The use of the Auction method speeds up this step greatlyboth for the intrinsic properties of this method and because we fullyexploit a property relating two successive optimizations, proved on paper,that in practical instances enables the mean computational cost requested bythe optimization step to be greatly reduced. It is proved that the algorithmconverges in a finite number of steps finding a solution when the problem isfeasible or detecting the infeasibility condition otherwise. Moreover theworst case computational complexity of the whole algorithm is shown to bepolynomial in the number of nodes of the discretization grid. Finallynumerical examples are reported in order to show the effectiveness of thistechnique.