7 accepted papers
In this letter, we consider the Multi-Robot Efficient Search Path Planning (MESPP) problem, where a team of robots is deployed in a graph-represented environment to capture a moving target within a given deadline. We prove this problem to be NP-hard, and present the first set of Mixed-Integer Linear
An autonomous mobile robot may encounter adversarial environments in which an attacker tries to influence its decisions. Through physical or software-level attacks, some of the robot's sensors might be compromised-a special concern for self-driving vehicles. Motivated by this scenario, this letter i
This letter presents a pedestrian motion model that includes both low level trajectory patterns, and high level discrete transitions. The inclusion of both levels creates a more general predictive model, allowing for more meaningful prediction and reasoning about pedestrian trajectories, as compared
Autonomous mobile robots perform many tasks, such as grasping and inspection, that may require complete models of three-dimensional (3-D) objects in the environment. If little or no knowledge about an object is known a priori, the robot must take sensor measurements from strategically determined vie
An efficient surface normal estimation method is presented. The new algorithm estimates surface normal direction for each cell in a grid based on the occupancy information (both occupied and empty) of the neighboring cells. This grid representation allows user-defined sizes and scaling with the envi
Efficient robotic exploration of an unknown, sensor limited, global-information-deficient environment poses a unique challenge to path planning algorithms because no deterministic guarantees on path completion and mission success can be made. Integrated Exploration (IE), which strives to combine loc
A probabilistic qualitative relational mapping (PQRM) algorithm is developed to enable robots to robustly map environments using noisy sensor measurements. Qualitative state representations provide soft, relative map information which is robust to metrical errors. In this paper, probabilistic distri