Hidden Markov Model for Visual Guidance of Robot Motion in Dynamic Environment

Models and control strategies for dynamic obstacle avoidance in visual guidance of mobile robot are presented. Characteristics that distinguish the visual computation and motion-control requirements in dynamic environments from that in static environments are discussed. Objectives of the vision and motion planning are formulated as: 1) finding a collision-free trajectory that takes account of any possible motions of obstacles in the local environment; 2) such a trajectory should be consistent with a global goal or plan of the motion; and 3) the robot should move at as high a speed as possible, subject to its kinematic constraints. A stochastic motion-control algorithm based on a hidden Markov model (HMM) is developed. Obstacle motion prediction applies a probabilistic evaluation scheme. Motion planning of the robot implements a trajectory-guided parallel-search strategy in accordance with the obstacle motion prediction models. The approach simplifies the control process of robot motion

Toward Online Probabilistic Path Replanning

In this talk we present work on sensor-based motion planning in initially unknown dynamic environments. Motion detection and probabilistic motion modeling are combined with a smooth navigation function to perform on-line path planning and replanning in cluttered dynamic environments such as public exhibitions. Human behavior is unforeseeable in most situations that include human-robot interaction, e.g. service robots or robotic companions. This makes motion prediction problematic (they rarely move e.g. with constant velocity along straight lines), especially in settings which include large numbers of humans. Additionally, the robot is usually required to react swiftly rather than optimally, in other words the time required to calculate the plan becomes part of the optimality criterion. The "Probabilistic Navigation Function" (PNF) is an approach for planning in these cluttered dynamic environments. It relies on probabilistic worst-case computations of the collision risk and weighs regions based on that estimate. The PNF is intended to be used for gradient-descent control of a vehicle, where the gradient indicates the best trade-off between risk and detour. An underlying reactive collision avoidance provides the tight perception-action loop to cope with the remaining collision probability. As this is work in progress, we present the approach and describe finished components and give an outlook on remaining implementation issues. Two algorithmic building blocks have been developed and tested: On-line motion detection from a mobile platform is performed by the SLIP scan alignment method to separate static from dynamic objects (it also helps with pose estimation). The interface between motion detection and path planning is a probabilistic co-occurrence estimation measuring the risk of future collisions given environment constraints and worst-case scenarios, which unifies dynamic and static elements. The risk is translated into traversal costs for an E* path planner, which produces smooth navigation functions that can incorporate new environmental information in near real-time

High-DOF Motion Planning in Dynamic Environments using Trajectory Optimization

Motion planning is an important problem in robotics, computer-aided design, and simulated environments. Recently, robots with a high number of controllable joints are increasingly used for different applications, including in dynamic environments with humans and other moving objects. In this thesis, we address three main challenges related to motion planning algorithms for high-DOF robots in dynamic environments: 1) how to compute a feasible and constrained motion trajectory in dynamic environments; 2) how to improve the performance of realtime computations for high-DOF robots; 3) how to model the uncertainty in the environment representation and the motion of the obstacles. We present a novel optimization-based algorithm for motion planning in dynamic environments. We model various constraints corresponding to smoothness, as well as kinematics and dynamics bounds, as a cost function, and perform stochastic trajectory optimization to compute feasible high-dimensional trajectories. In order to handle arbitrary dynamic obstacles, we use a replanning framework that interleaves planning with execution. We also parallelize our approach on multiple CPU or GPU cores to improve the performance and perform realtime computations. In order to deal with the uncertainty of dynamic environments, we present an efficient probabilistic collision detection algorithm that takes into account noisy sensor data. We predict the future obstacle motion as Gaussian distributions, and compute the bounded collision probability between a high-DOF robot and obstacles. We highlight the performance of our algorithms in simulated environments as well as with a 7-DOF Fetch arm.Doctor of Philosoph

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page