we present some new results on the existence and the approximationof common fixed point of expansive mappings and semigroups in probabilisticmetric spaces.

Planning motions for two robot arms to move an object collaboratively is a difficult problem, mainly because of the closed-chain constraint, which arises whenever two robot hands simultaneously grasp a single rigid object. In this paper, we propose a manipulation planning algorithm to bring an object from an initial stable placement (position and orientation of the object on the support surface...

Probabilistic roadmap (PRM) planners [5, 16] solve apparently difficult motion planning problems where the robot's configuration space C has dimensionality six or more, and the geometry of the robot and the obstacles is described by hundreds of thousands of triangles. While an algebraic planner would be overwhelmed by the high cost of computing an exact representation of the free space F , defi...

Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the d...

Estimating a robot’s reachable workspace is a fundamental problem in robotics. For simple kinematic chains within an empty environment this computation can be relatively straightforward. For mobile kinematic structures and cluttered environments, the problem becomes more challenging. An efficient probabilistic method for workspace estimation is developed by applying a hierarchical strategy and ...

This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot’s state×time space by picking control inputs at random and integrating its ...

This paper presents a novel randomized motion planner for robots that must achieve a specified goal under kinematic and/or dynamic motion constraints while avoiding collision with moving obstacles with known trajectories. The planner encodes the motion constraints on the robot with a control system and samples the robot’s state×time space by picking control inputs at random and integrating its ...

Multiped locomotion in cluttered environments is addressed as the problem of planning acyclic sequences of contacts, that characterize the motion. In order to overcome the inherent combinatorial difficulty of the problem, we separate it in two subproblems: first, planning a guide trajectory for the root of the robot and then, generating relevant contacts along this trajectory. This paper propos...

The probabilistic roadmap (PRM) planner is a popular method for robot motion planning problems with many degrees of freedom. However, it has been shown that the method performs less well in situations where the robot has to pass through a narrow passage in the scene. This is mainly due to the uniformity of the sampling used in the planner; it places many samples in large open regions and too fe...

Markov Decision Processes are a powerful framework for planning under uncertainty, but current algorithms have difficulties scaling to large problems. We present a novel probabilistic planner based on the notion of hybridizing two algorithms. In particular, we hybridize GPT, an exact MDP solver, with MBP, a planner that plans using a qualitative (nondeterministic) model of uncertainty. Whereas ...