Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
Conference registration code to access these videos can be accessed by visiting this link: PaperPlaza. Step-by-step to access these videos are here: step-by-step process .
Why some of the videos are missing? If you had provided your consent form for your video to be published and still it is missing, please contact email@example.com
Time-Optimal Multi-Stage Motion Planning with Guaranteed Collision Avoidance Via an Open-Loop Game FormulationWe present an efficient algorithm which computes, for a kinematic point mass moving in the plane, a time-optimal path that visits a sequence of target sets while conservatively avoiding collision with moving obstacles, also modelled as kine- matic point masses, but whose trajectories are unknown. The problem is formulated as a pursuit-evasion differential game, and the underlying construction is based on optimal control. The algorithm, which is a variant of the fast marching method for shortest path problems, can handle general dynamical constraints on the players and arbitrary domain geometry (e.g. obstacles, non-polygonal boundaries). Applications to a two- stage game, capture-the-flag, is presented.
Execution and Analysis of High-Level Tasks with Dynamic Obstacle AnticipationThis paper uniquely embeds high-level robot controllers with sensor data obtained from abstracting probabilistic anticipation of the behavior of dynamic obstacles. An example problem of an autonomous vehicle operating in an urban environment, in the presence of other vehicles and pedestrians, is used as motivation. The correct-by-construction controller is automatically synthesized from a set of high-level tasks, specified as temporal logic formulas. The anticipated behavior of other vehicles is abstracted to a set of propositions describing the safety of road segments at intersections, and used as the output of high-level sensors for the controller. Such an input to the controller is inherently probabilistic, and this paper investigates the types of probabilistic guarantees that can be made about the system using both formal and statistical analysis.
A Depth Space Approach to Human-Robot Collision AvoidanceIn this paper a real-time collision avoidance approach is presented for safe human-robot coexistence. The main contribution is a fast method to evaluate distances between the robot and possibly moving obstacles (including humans), based on the concept of depth space. With these distances, repulsive vectors are generated that are used to control the robot while executing a generic motion task. The repulsive vectors can also take advantage of an estimation of the obstacle velocity. In order to preserve the execution of a Cartesian task with a redundant manipulator, a simple collision avoidance algorithm has been implemented where different reaction behaviors are set up for the end-effector and for other control points along the robot structure. The complete collision avoidance framework, from perception of the environment to joint-level robot control, is presented for a 7-dof KUKA Light-Weight-Robot IV using the Microsoft Kinect sensor. Experimental results are reported for dynamic environments with obstacles and a human.
LQG-Obstacles: Feedback Control with Collision Avoidance for Mobile Robots with Motion and Sensing UncertaintyThis paper presents LQG-Obstacles, a new concept that combines linear-quadratic feedback control of mobile robots with guaranteed avoidance of collisions with obstacles. Our approach generalizes the concept of Velocity Obstacles to any robotic system with a linear Gaussian dynamics model. We integrate a Kalman filter for state estimation and an LQR feedback controller into a closed-loop dynamics model of which a higher-level control objective is the ``control input''. We then define the LQG-Obstacle as the set of control objectives that result in a collision with high probability. Selecting a control objective outside the LQG-Obstacle then produces collision-free motion. We demonstrate the potential of LQG-Obstacles by safely and smoothly navigating a simulated quadrotor helicopter with complex non-linear dynamics and motion and sensing uncertainty through three-dimensional environments with obstacles and narrow passages.
K-IOS: Intersection of Spheres for Efficient Proximity QueryWe present a new bounding volume structure, k-IOS that is an intersection of k spheres, for accelerating proximity query including collision detection and Euclidean distance computation between arbitrary polygon-soup models that undergo rigid motion. Our new bounding volume is easy to implement and highly efficient both for its construction and runtime query. In our experiments, we have observed up to 4.0 times performance improvement of proximity query compared to an existing well-known algorithm based on swept sphere volume (SSV) . Moreover, k-IOS is strictly convex that can guarantee a continuous gradient of distance function with respect to objectâ€™s configuration parameter.
Reciprocal Collision Avoidance for Multiple Car-Like RobotsIn this paper a method for distributed reciprocal collision avoidance among multiple non-holonomic robots with bike kinematics is presented. The proposed algorithm, bicycle reciprocal collision avoidance (B-ORCA), builds on the concept of optimal reciprocal collision avoidance (ORCA) for holonomic robots but furthermore guarantees collision-free motions under the kinematic constraints of car-like vehicles. The underlying principle of the B-ORCA algorithm applies more generally to other kinematic models, as it combines velocity obstacles with generic tracking control. The theoretical results on collision avoidance are validated by several simulation experiments between multiple car-like robots.