A Method for Modifying Closed-Loop Motion Plans to Satisfy Unpredictable Dynamic Constraints at Runtime

In this paper; the problem of motion planning in environments with both known static obstacles and unpredictable dynamic constraints is considered. A methodology is introduced in which the motion plan for the static environment is modified on-line to accommodate the unpredictable constraints in such a way that the completeness properties of the original motion plan are preserved. At the heart of the approach is the idea that Navigation functions are indeed Lyapunov functions; and that the traditional method of forcing the robot to track the negative gradient of field is not the only input which stabilizes the system. This extra freedom in selecting the input is used to accommodate the dynamic constraints. A computational method for selecting the appropriate inputs is given. The method is used to solve two sample problems. The constraints in these cases are used to model collisions with other robots and, in the second example, a team of robots traveling in formation. Finally, some preliminary work on extending the approach to nonholonomic systems is presented

Autonomous Motion Planning for Avatar Limbs

In this work, a new algorithm for autonomous avatar motion is presented. The new algorithm is based in the Rapidly-exploring Random Tree (RRT) and an appropriate ontology. It uses a novel approach for calculating the motion sequence planning for the different avatar limbs: legs or arms. First, the algorithm uses the information stored in the ontology concerning the avatar structure and the Degrees Of Freedom (DOFs) to obtain the basic actions for motion planning. Second, this information is used to perform the growth process in the RRT algorithm. Then, all this information is used to produce planning. The plans are generated by a random search for possible motions that respect the structural restrictions of the avatar on kinesiology studies. To avoid a big configuration space search, exploration, exploitation, and hill climbing are used in order to obtain motion plans

Safe Navigation of a Car-Like Robot in a Dynamic Environment

voir basilic : http://emotion.inrialpes.fr/bibemotion/2005/PF05a/ address: Ancona (IT)This paper addresses the problem of navigation of a car-like robot in dynamic environments. Such environments impose a hard real time constraint. However, computing a complete motion to the goal within a limited time is impossible to achieve in most real situations. Besides, the limited duration validity of the model used for planning requires the model and therefore the plan to be updated. In this paper, we present a Partial Motion Planning (PMP) approach as the answer to this problem. The issue of safety raised by this approach is addressed using the Inevitable Collision State formalism and effectiveness of the approach is demonstrated with several simulation examples. The quality of the generated trajectories is discussed and continuous curvature metric is integrated as a mean to improve it

Real-Time Replanning in High-Dimensional Configuration Spaces Using Sets of Homotopic Paths

Real-time replanning is a prerequisite for motion execution in unpredictably changing environments. This paper presents a framework that allows real-time replanning in high-dimensional configuration spaces. Initially, a planning operation generates a path. The path is augmented by a set of paths homotopic to it. This set is represented implicitly by a volume of free space in the work space. Effectively, this corresponds to delaying part of the planning operation for the homotopic paths until motion execution. During execution reactive control algorithms are used to select a valid path from the set of homotopic paths, using proximity to the environment as a simple and effective heuristic and thereby significantly pruning the search in the configuration space. Experimental results are presented to validate the real-time performance of this framework in high-dimensional configuration spaces

Constraint-based navigation for safe, shared control of ground vehicles

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 138-147).Human error in machine operation is common and costly. This thesis introduces, develops, and experimentally demonstrates a new paradigm for shared-adaptive control of human-machine systems that mitigates the effects of human error without removing humans from the control loop. Motivated by observed human proclivity toward navigation in fields of safe travel rather than along specific trajectories, the planning and control framework developed in this thesis is rooted in the design and enforcement of constraints rather than the more traditional use of reference paths. Two constraint-planning methods are introduced. The first uses a constrained Delaunay triangulation of the environment to identify, cumulatively evaluate, and succinctly circumscribe the paths belonging to a particular homotopy with a set of semi autonomously enforceable constraints on the vehicle's position. The second identifies a desired homotopy by planning - and then laterally expanding - the optimal path that traverses it. Simulated results show both of these constraint-planning methods capable of improving the performance of one or multiple agents traversing an environment with obstacles. A method for predicting the threat posed to the vehicle given the current driver action, present state of the environment, and modeled vehicle dynamics is also presented. This threat assessment method, and the shared control approach it facilitates, are shown in simulation to prevent constraint violation or vehicular loss of control with minimal control intervention. Visual and haptic driver feedback mechanisms facilitated by this constraint-based control and threat-based intervention are also introduced. Finally, a large-scale, repeated measures study is presented to evaluate this control framework's effect on the performance, confidence, and cognitive workload of 20 drivers teleoperating an unmanned ground vehicle through an outdoor obstacle course. In 1,200 trials, the constraint-based framework developed in this thesis is shown to increase vehicle velocity by 26% while reducing the occurrence of collisions by 78%, improving driver reaction time to a secondary task by 8.7%, and increasing overall user confidence and sense of control by 44% and 12%, respectively. These performance improvements were realized with the autonomous controller usurping less than 43% of available vehicle control authority, on average.by Sterling J. Anderson.Ph.D

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described