Safe motion planning under uncertainty for mobile manipulators in unknown environments

For a mobile manipulator to operate and perform useful tasks in human-centered environments, it is important to work toward the realization of robust motion planners that incorporate uncertainty inherent in robot\u27s control and sensing and provide safe motion plans for reliable robot operation. Designing such planners pose a significant challenge because of computational complexity associated with mobile manipulator planning and planning under uncertainty. Current planning approaches for mobile manipulation are often conservative in nature and the uncertainty is largely ignored. In this thesis, we propose sampling-based efficient and robust mobile manipulator planners that use smart strategies to deal with computational complexity and incorporate uncertainty to generate safer plans. The first part of the research addresses the design of an efficient planner for deterministic case, where robot state is fully known, and then subsequent extension to incorporate base pose uncertainty. In the first part, we propose a Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) that plans both for the base and the arm in a judicious manner - allowing the manipulator to change its configuration autonomously when needed if the current arm configuration is in collision with the environment as the mobile manipulator moves along the planned path. We show that HAMP is probabilistically complete. We then propose an extension of HAMP (HAMP-U) to account for localization uncertainty associated with the mobile base position. The advantages of our planners are illustrated and discussed. The second part of the research deals with the computational complexity involved in planning under uncertainty. For that, we propose localization aware sampling and connection strategies that help to reduce the planning time significantly with little compromise on the quality of path. In the third part, we learnt from the shortcomings of HAMP-U and took advantage of our smart strategies developed to combat the computational complexity. We propose an efficient and robust mobile manipulator planner (HAMP-BAU) that plans judiciously and considers the base pose uncertainty and the effects of this uncertainty on manipulator motions. It uses our localization aware sampling and connection strategies to consider only those nodes and edges which contribute toward better localization. This helps to find the same quality of path in shorter time. We also extend HAMP-BAU to incorporate task space constraints (HAMP-BAU-TC). Finally, in the last part of the work, we incorporate our planners (HAMP-BAU and HAMP-BAU-TC) within an integrated and fully autonomous system for mobile pick-and-place tasks in unknown static environments. We demonstrate our system both in simulation and real experiments on SFU mobile manipulator

Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

An intelligent, free-flying robot

The ground based demonstration of the extensive extravehicular activity (EVA) Retriever, a voice-supervised, intelligent, free flying robot, is designed to evaluate the capability to retrieve objects (astronauts, equipment, and tools) which have accidentally separated from the Space Station. The major objective of the EVA Retriever Project is to design, develop, and evaluate an integrated robotic hardware and on-board software system which autonomously: (1) performs system activation and check-out; (2) searches for and acquires the target; (3) plans and executes a rendezvous while continuously tracking the target; (4) avoids stationary and moving obstacles; (5) reaches for and grapples the target; (6) returns to transfer the object; and (7) returns to base