167 research outputs found

    Revisiting the Minimum Constraint Removal Problem in Mobile Robotics

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    The minimum constraint removal problem seeks to find the minimum number of constraints, i.e., obstacles, that need to be removed to connect a start to a goal location with a collision-free path. This problem is NP-hard and has been studied in robotics, wireless sensing, and computational geometry. This work contributes to the existing literature by presenting and discussing two results. The first result shows that the minimum constraint removal is NP-hard for simply connected obstacles where each obstacle intersects a constant number of other obstacles. The second result demonstrates that for nn simply connected obstacles in the plane, instances of the minimum constraint removal problem with minimum removable obstacles lower than (n+1)/3(n+1)/3 can be solved in polynomial time. This result is also empirically validated using several instances of randomly sampled axis-parallel rectangles.Comment: Accepted for presentation at the 18th international conference on Intelligent Autonomous System 202

    Probabilistic Collision Constraint for Motion Planning in Dynamic Environments

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    Online generation of collision free trajectories is of prime importance for autonomous navigation. Dynamic environments, robot motion and sensing uncertainties adds further challenges to collision avoidance systems. This paper presents an approach for collision avoidance in dynamic environments, incorporating robot and obstacle state uncertainties. We derive a tight upper bound for collision probability between robot and obstacle and formulate it as a motion planning constraint which is solvable in real time. The proposed approach is tested in simulation considering mobile robots as well as quadrotors to demonstrate that successful collision avoidance is achieved in real time application. We also provide a comparison of our approach with several state-of-the-art methods.Comment: Accepted for presentation at the 16th International Conference on Intelligent Autonomous Systems (IAS-16
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