Motion planning and coordination for robot systems based on representation space

Motion Planning for Mobile Manipulators—A Systematic Review

One of the fundamental fields of research is motion planning. Mobile manipulators present a unique set of challenges for the planning algorithms, as they are usually kinematically redundant and dynamically complex owing to the different dynamic behavior of the mobile base and the manipulator. The purpose of this article is to systematically review the different planning algorithms specifically used for mobile manipulator motion planning. Depending on how the two subsystems are treated during planning, sampling-based, optimization-based, search-based, and other planning algorithms are grouped into two broad categories. Then, planning algorithms are dissected and discussed based on common components. The problem of dealing with the kinematic redundancy in calculating the goal configuration is also analyzed. While planning separately for the mobile base and the manipulator provides convenience, the results are sub-optimal. Coordinating between the mobile base and manipulator while utilizing their unique capabilities provides better solution paths. Based on the analysis, challenges faced by the current planning algorithms and future research directions are presented.

Modular Robotic Design and Reconfiguring Path Planning

The modular robot is becoming a prevalent research object in robots because of its unique configuration advantages and performance characteristics. It is possible to form robot configurations with different functions by reconfiguring functional modules. This paper focuses on studying the modular robot’s configuration design and self-reconfiguration process and hopes to realize the industrial application of the modular self-reconfiguration robot to a certain extent. We design robotic configurations with different DOF based on the cellular module of the hexahedron and perform the kinematic analysis of the structure. An innovative design of a modular reconfiguration platform for conformational reorganization is presented, and the collaborative path planning between different modules in the reconfiguration platform is investigated. We propose an optimized ant colony algorithm for reconfiguration path planning and verify the superiority and rationality of this algorithm compared with the traditional ant colony algorithm for platform path planning through simulation experiments.

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

Visual Servoing of Wheeled Mobile Robots Without Desired Images

This paper proposes a novel monocular visual servoing strategy, which can drive a wheeled mobile robot to the desired pose without a prerecorded desired image. Compared with existing methods that adopt the teaching pattern for visual regulation, this scheme can still work well in the situation that the desired image has not been previously acquired. Thus, with the aid of this method, it is more convenient for mobile robots to execute visual servoing tasks. Specifically, to deal with nonexistence of the desired image, the reference frame is craftily defined by taking advantage of visual targets and the planar motion constraint, and the pose estimation algorithm is designed for the mobile robot with respect to the reference frame. Then, an adaptive visual regulation controller is developed to drive the mobile robot to the intermediate frame, where the parameter updating law is constructed for the unknown feature height based on the concurrent learning framework. Stability analysis shows that regulation errors and height identification error can converge simultaneously. Afterwards, the mobile robot is driven to the metric desired pose with the identified feature height. Both simulation and experimental results are provided to validate the performance of this strategy.

Cooperative Robots to Observe Moving Targets: Review

The deployment of multiple robots for achieving a common goal helps to improve the performance, efficiency, and/or robustness in a variety of tasks. In particular, the observation of moving targets is an important multirobot application that still exhibits numerous open challenges, including the effective coordination of the robots. This paper reviews control techniques for cooperative mobile robots monitoring multiple targets. The simultaneous movement of robots and targets makes this problem particularly interesting, and our review systematically addresses this cooperative multirobot problem for the first time. We classify and critically discuss the control techniques: cooperative multirobot observation of multiple moving targets, cooperative search, acquisition, and track, cooperative tracking, and multirobot pursuit evasion. We also identify the five major elements that characterize this problem, namely, the coordination method, the environment, the target, the robot and its sensor(s). These elements are used to systematically analyze the control techniques. The majority of the studied work is based on simulation and laboratory studies, which may not accurately reflect real-world operational conditions. Importantly, while our systematic analysis is focused on multitarget observation, our proposed classification is useful also for related multirobot applications.

Robotic Online Path Planning on Point Cloud

This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.

A Heuristic Distributed Task Allocation Method for Multivehicle Multitask Problems and Its Application to Search and Rescue Scenario

Using distributed task allocation methods for cooperating multivehicle systems is becoming increasingly attractive. However, most effort is placed on various specific experimental work and little has been done to systematically analyze the problem of interest and the existing methods. In this paper, a general scenario description and a system configuration are first presented according to search and rescue scenario. The objective of the problem is then analyzed together with its mathematical formulation extracted from the scenario. Considering the requirement of distributed computing, this paper then proposes a novel heuristic distributed task allocation method for multivehicle multitask assignment problems. The proposed method is simple and effective. It directly aims at optimizing the mathematical objective defined for the problem. A new concept of significance is defined for every task and is measured by the contribution to the local cost generated by a vehicle, which underlies the key idea of the algorithm. The whole algorithm iterates between a task inclusion phase, and a consensus and task removal phase, running concurrently on all the vehicles where local communication exists between them. The former phase is used to include tasks into a vehicle's task list for optimizing the overall objective, while the latter is to reach consensus on the significance value of tasks for each vehicle and to remove the tasks that have been assigned to other vehicles. Numerical simulations demonstrate that the proposed method is able to provide a conflict-free solution and can achieve outstanding performance in comparison with the consensus-based bundle algorithm.

On Configuration Trajectory Formation in Spatiotemporal Profile for Reproducing Human Hand Reaching Movement

Most functional reaching activities in daily living generally require a hand to reach the functional position in appropriate orientation with invariant spatiotemporal profile. Effectively reproducing such spatiotemporal feature of hand configuration trajectory in real time is essential to understand the human motor control and plan human-like motion on anthropomorphic robotic arm. However, there are no novel computational models in literature toward reproducing hand configuration-to-configuration movement in spatiotemporal profile. In response to the problem, this paper presents a computational framework for hand configuration trajectory formation based on hierarchical principle of human motor control. The composite potential field is constructed on special Euclidean Group to induce time-varying configuration toward target. The dynamic behavior of hand is described by a second-order kinematic model to produce the external representation of high-level motor control. The multivariate regression relation between intrinsic and extrinsic coordinates of arm, is statistically analyzed for determining the arm orientation in real time, which produces the external representation of low-level motor control. The proposed method is demonstrated in an anthropomorphic arm by performing several highly curved self-reaching movements. The generated configuration trajectories are compared with actual human movement in spatiotemporal profile to validate the proposed method.

Unbounded Motion Optimization by Developmental Learning

An algorithm is presented for autonomous motion development with unbounded waveform resolution. Rather than a single optimization in a very large space, memory is built to support incremental improvements; therefore, complexity is balanced by experience. Analogously, human development manages complexity by limiting it during initial learning stages. Motions are represented by cubic spline interpolation; therefore, the development technique applies broadly to function optimization. Adding a node to the splines allows all previous memory samples to transfer to the higher dimension space exactly. The memory-based model, which is a locally weighted regression (LWR), predicts the expected outcome for a motion and provides gradient information for optimizing the motion. Results are compared against bootstrapping a direct optimization (DO) on a mathematical problem. Additionally, the method has been implemented to learn voltage profiles with the lowest peak current for starting a motor. This method shows practical accuracy and scalability.