Kinodynamic motion planning with hardware demonstrations

Abstract — This paper provides proof-of-concept that state-of-the-art sampling-based motion planners that are tightly integrated with a physics-based simulator can compute paths that can be executed by a physical robotic system. Such a goal has been the subject of intensive research during the last few years and reflects the desire of the motion planning community to produce paths that are directly relevant to realistic mechanical systems and do not need a huge post-processing step in order to be executed on a robotic platform. To evaluate this approach, a recently developed motion planner is used to compute paths for a modular robot constructed from seven modules. These paths are then executed on hardware and compared with the paths predicted by the planner. For the system considered, the planner prediction and the paths achieved by the physical robot match, up to small errors. This work reveals the potential of modern motion planning research and its implications in the design and operation of complex robotic platforms. I

Reconfiguration for Modular Robots Using Kinodynamic Motion Planning

This paper presents computational and experimental evi-dence that it is possible to plan and execute dynamic motions that involve chain reconfiguration for modular reconfigurable robots in the presence of obstacles. At the heart of the approach is the use of a sampling-based motion planner that is tightly integrated with a physics-based dynamic simulator. To evaluate the method, the planner is used to compute motions for a chain robot con-structed from CKbot modules to perform a reconfiguration, at-taching more modules and continuing a dynamic motion while avoiding obstacles. These motions are then executed on hard-ware and compared with the ones predicted by the planner

Spatiotemporal state lattices for fast trajectory planning in dynamic on-road driving scenarios

Abstract-We present a method for motion planning in the presence of moving obstacles that is aimed at dynamic on-road driving scenarios. Planning is performed within a geometric graph that is established by sampling deterministically from a manifold that is obtained by combining configuration space and time. We show that these graphs are acyclic and shortest path algorithms with linear runtime can be employed. By reparametrising the configuration space to match the course of the road, it can be sampled very economically with few vertices, and this reduces absolute runtime further. The trajectories generated are quintic splines. They are second order continuous, obey nonholonomic constraints and are optimised for minimum square of jerk. Planning time remains below 20 ms on general purpose hardware

On the performance of random linear projections for sampling-based motion planning

Sampling-based motion planners are often used to solve very high-dimensional planning problems. Many recent algorithms use projections of the state space to estimate properties such as coverage, as it is impractical to compute and store this information in the original space. Such estimates help motion planners determine the regions of space that merit further exploration. In general, the employed projections are user-defined, and to the authors’ knowledge, automatically computing them has not yet been investigated. In this work, the feasibility of offline-computed random linear projections is evaluated within the context of a state-of-the art sampling-based motion planning algorithm. For systems with moderate dimension, random linear projections seem to outperform human intuition. For more complex systems it is likely that non-linear projections would be better suited

Computing fast search heuristics for physics-based mobile robot motion planning

Mobile robots are increasingly being employed to assist responders in search and rescue missions. Robots have to navigate in dangerous areas such as collapsed buildings and hazardous sites, which can be inaccessible to humans. Tele-operating the robots can be stressing for the human operators, which are also overloaded with mission tasks and coordination overhead, so it is important to provide the robot with some degree of autonomy, to lighten up the task for the human operator and also to ensure robot safety. Moving robots around requires reasoning, including interpretation of the environment, spatial reasoning, planning of actions (motion), and execution. This is particularly challenging when the environment is unstructured, and the terrain is \textit{harsh}, i.e. not flat and cluttered with obstacles. Approaches reducing the problem to a 2D path planning problem fall short, and many of those who reason about the problem in 3D don't do it in a complete and exhaustive manner. The approach proposed in this thesis is to use rigid body simulation to obtain a more truthful model of the reality, i.e. of the interaction between the robot and the environment. Such a simulation obeys the laws of physics, takes into account the geometry of the environment, the geometry of the robot, and any dynamic constraints that may be in place. The physics-based motion planning approach by itself is also highly intractable due to the computational load required to perform state propagation combined with the exponential blowup of planning; additionally, there are more technical limitations that disallow us to use things such as state sampling or state steering, which are known to be effective in solving the problem in simpler domains. The proposed solution to this problem is to compute heuristics that can bias the search towards the goal, so as to quickly converge towards the solution. With such a model, the search space is a rich space, which can only contain states which are physically reachable by the robot, and also tells us enough information about the safety of the robot itself. The overall result is that by using this framework the robot engineer has a simpler job of encoding the \textit{domain knowledge} which now consists only of providing the robot geometric model plus any constraints

Optimale Bahn- und Trajektorienplanung für Automobile

In dieser Arbeit werden verschiedene Ansätze für die automatische Trajektorienplanung eines vorderradgelenkten Fahrzeuges erarbeitet. Es werden dabei sowohl globale, kombinatorische Prinzipien untersucht als auch lokale, kontinuierliche. Es wird über umfangreichen Fahrversuche berichtet, mit denen die entwickelten Verfahren erprobt wurden

Task and motion planning for mobile manipulators

This thesis introduces new concepts and algorithms that can be used to solve the simultaneous task and motion planning (STAMP) problem. Given a set of actions a robot could perform, the STAMP problem asks for a sequence of actions that takes the robot to its goal and for motion plans that correspond to the actions in that sequence. This thesis shows how to solve the STAMP problem more efficiently and obtain more robust solutions, when compared to previous work. A solution to the STAMP problem is a prerequisite for most operations complex robots such as mobile manipulators are asked to perform. Solving the STAMP problem efficiently thus expands the range of capabilities for mobile manipulators, and the increased robustness of computed solutions can improve safety. A basic sub-problem of the STAMP problem is motion planning. This thesis generalizes KPIECE, a sampling-based motion planning algorithm designed specifically for planning in high-dimensional spaces. KPIECE offers computational advantages by employing projections from the searched space to lower-dimensional Euclidean spaces for estimating exploration coverage. This thesis further develops the original KPIECE algorithm by introducing a means to automatically generate projections to lower-dimensional Euclidean spaces. KPIECE and other state-of-the-art algorithms are implemented as part the Open Motion Planning Library (OMPL), and the practical applicability of KPIECE and OMPL is demonstrated on the PR2 hardware platform. To solve the STAMP problem, this thesis introduces the concept of a task motion multigraph (TMM), a data structure that can express the ability of mobile manipulators to perform specific tasks using different hardware components. The choice of hardware components determines the state space for motion planning. An algorithm that prioritizes the state spaces for motion planning using TMMs is presented and evaluated. Experimental results show that planning times are reduced by a factor of up to six and solution paths are shortened by a factor of up to four, when considering the available planning options. Finally, an algorithm that considers uncertainty at the task planning level based on generating Markov Decision Process (MDP) problems from TMMs is introduced