Research on robotic manipulation has mainly focused on manipulating rigid objects so far. However, many important application domains require manipulating deformable objects, especially deformable linear objects (DLOs), such as ropes, cables, and sutures. Such objects are far more challenging to handle, as they can exhibit a much greater diversity of behaviors, and their manipulation almost inevitably requires two robotic arms, or more, performing well-coordinated motions. This paper describes a new motion planner for manipulating DLOs and tying knots (both self-knots and knots around simple static objects) using two cooperating robotic arms. This planner blends new ideas with preexisting concepts and techniques from knot theory, robot motion planning, and computational modeling. Unlike in traditional motion planning problems, the goal to be achieved by the planner is a topological state of the world, rather than a geometric one. To search for a manipulation path, the planner constructs a topologically biased probabilistic roadmap in the configuration space of the DLO. During roadmap construction, it uses inverse kinematics to determine the successive robot configurations implied by the DLO configurations and tests their feasibility. Also, inspired by the real life, the planner uses static ldquoneedlesrdquo (by analogy to the needles used in knitting) for maintaining the stability of the DLO during manipulation and to make the resulting manipulation plan robust to imperfections in the physical model of the DLO. The implemented planner has been tested both in graphic simulation and on a dual-PUMA-560 hardware platform to achieve various knots, like bowline, neck-tie, bow (shoe-lace), and stun-sail.

This paper presents a connection strategy for PRM-based motion planning in high-dimensional configuration spaces. The connection strategy identifies components of motion during the roadmap construction and plans the roadmap edges on a sequence of configuration slices approximating the configuration spaces of the components. The goal of the approach is to extend the dimensionality of the problems PRM planners can solve, and the strategy it deploys is dimensional subdivision. We report experimental results on serpentine robots with up to 150 degrees-of-freedom and multi-robot systems with up to 144 degrees-of-freedom. The test problems are beyond the capability of a representative state-of-the-art PRM based planner, and the approach presented in this paper enables the use of motion planning methods in solving new high-dimensional problems

Research on robotic manipulation has mainly focused on manipulating rigid objects so far. However, many important application domains require manipulating deformable objects, especially deformable linear objects (DLOs), such as ropes, cables, and sutures. Such objects are far more challenging to handle, as they can exhibit a much greater diversity of behaviors. This paper describes a new motion planner for manipulating DLOs and tying knots (self-knots and knots around simple static objects) using cooperating robot arms. The planner constructs a topologically-biased probabilistic roadmap in the DLO's configuration space. Unlike in traditional motion planning problems, the goal is a topological state of the world, rather than a geometric one. The implemented planner was tested in simulation to achieve various knots like bowline, neck-tie, bow (shoe-lace), and stun-sail

In a recent study, it was shown that high failure rates in decoupled multi-robot motion planning makes the decoupled planning approach an unsuitable choice, in comparison to the centralized approach, in many practical instances. Motivated by this study, we devised a new multi-robot planning strategy in which we partially merge the two stages of the basic decoupled planning approach. Our experimental results show that this new strategy significantly improves upon the reliability of the decoupled planning approach. Overall in our tests, the new strategy outperforms both the centralized and decoupled planning approach, indicating that in practice it can be a better choice, than both the centralized and decoupled approach, for solving multi-robot planning problems

The decades of research in motion planning have resulted in numerous algorithms. Many of the most successful algorithms are randomized and can have widely differing run-times for the same problem instance from run to run. While this property is known to be undesirable from user's point of view, it has been largely ignored in past research. This paper introduces the large run-cost variance of randomized motion planners as a distinct issue to be addressed in future research. Run-cost variance is an important performance characteristic of an algorithm that should be studied together with the mean run-cost. As a positive example of possibilities for reducing the run-cost variance of a randomized motion planner, simple heuristic techniques are introduced and investigated empirically.

This paper describes a new approach to probabilistic roadmap construction for path planning. The novel feature of the planner is that it uses a powerful local planner to produce highly connected roadmaps and path optimization to maintain the rapid query processing by a fast local operator. While most previous approaches obtain good roadmaps by advanced sampling methods, the presented approach concentrates on the method used to connect the samples. Empirical results show that the new approach outperforms the more traditional approach of using fast local planners in capability to produce roadmaps with only few connected components. Statistical analysis is used to identify features important for the efficiency of the local planners.

A two-level search algorithm for motion planning is presented in this paper. The algorithm combines a multiheuristic local search algorithm with a subgoal graph based global guidance. A novel feature of the planner is that it can adjust the balance between local and global planning. As the experimental data suggests that the optimal balance depends on the problem, a scheduling mechanism is added to the algorithm to adjust the balance during planning. The resulting motion planner is capable of solving very difficult motion planning problems.