real-time motion planning for multibody systems

The solution of constrained motion planning is an important task in a wide number of application fields. The real-time solution of such a problem, formulated in the framework of optimal control theory, is a challenging issue. We prove that a real-time solution of the constrained motion planning problem for multibody systems is possible for practical real-life applications on standard personal computers. The proposed method is based on an indirect approach that eliminates the inequalities via penalty formulation and solves the boundary value problem by a combination of finite differences and Newton–Broyden algorithm. Two application examples are presented to validate the method and for performance comparisons. Numerical results show that the approach is real-time capable if the correct penalty formulation and settings are chosen.     

新型势场模型及在实时运动规划中应用

针对复杂环境中移动机器人的实时运动规划问题,在传统人工势场法基础上,以机器人与动态障碍物的相对位置和相对速度为变量,构建了一种新型的位置和速度势场模型,提出了一种新的自适应势场法.通过变参数约束扰动法解决了势场法中的局部极小问题.实验结果表明,该方法能使机器人选择最佳避障策略实现主动避障,快速地摆脱动态障碍物,规划出光滑路径,从而提高避障效率,较好地解决了动态未知环境中的路径规划问题.     

Model-based testing of a real-time adaptive motion planning system

Abstract

To enable effective and safe operations of autonomous robots in environments with unknowns and unpredictability, a key practical problem is how to test the functionality and assess the performance of real-time motion planning systems. This is a challenge because the underlying algorithms are real-time, sensing-based, and often non-deterministic. These systems’ performance depends on task environments, which can vary in countless ways. Existing testing techniques are designed heavily based on testers’ experience and hardly provide a good coverage of possible test scenarios. This paper introduces a systematic model-based testing (MBT) approach to evaluate the functionality and performance of a real-time adaptive motion planning (RAMP) system. The MBT approach uses the formal communicating extended finite state machine model to model RAMP’s concurrent components and leverage graph traversal algorithms to systematically generate behavioral test cases. First, component integration is considered by modeling the RAMP components and their interactions. Next, system-level testing is considered by modeling mobile obstacles of unpredictable motion behavior. The behavior models are leveraged to generate Abstract Behavioral Test Cases, which are transformed by test data into executable test cases. The test results demonstrate the effectiveness of applying the systematic MBT approach to the evaluation of real-time robotic systems.     

A real-time 3D motion planning and simulation scheme for nonholonomic systems

We propose a new motion planning and simulation scheme for nonholonomic systems in this paper to provide a practical solution for these application problems taking into account of real-time obstacle avoidance and the continuous curvature path generation simultaneously in 3D unknown environment. The proposed motion planning and simulation scheme generates the motion path using a new universal Euler spiral generation algorithm, which is locally optimal based on perceived points of view. The generated Euler spiral solution can be non-symmetrical and easily implemented while maintaining a C² continuous. It is therefore more flexible and powerful in dealing with dynamic situations in real-time, compared with current symmetrical Euler spirals solutions. Real-time solutions are particularly important in navigation in unknown environments. The universal Euler spiral algorithm proposed displays a smaller maximum curvature value and smaller mean square curvature value than the conventional symmetrical algorithm in tested cases. Another significant contribution of our work is the new motion planning scheme which extend current 2D based motion planning into three-dimensional (3D) space. In this paper, we have conducted experiments and describe simulation results including 3D motion trajectory modeling for a flight simulation.     

A real-time strategy for dexterous manipulation: Fingertips motion planning,force sensing and grasp stability

This paper presents a global strategy for object manipulation with the fingertips with an anthropomorphic dexterous hand: the LMS Hand of the ROBIOSS team from PPRIME Institute in Poitiers (France). Fine manipulation with the fingertips requires to compute on one hand, finger motions able to produce the desired object motion and on the other hand, it is necessary to ensure object stability with a real time scheme for the fingertip force computation. In the literature, lot of works propose to solve the stability problem, but most of these works are grasp oriented; it means that the use of the proposed methods are not easy to implement for online computation while the grasped object is moving inside the hand. Also simple real time schemes and experimental results with full-actuated mechanical hands using three fingers were not proposed or are extremely rare. Thus we wish to propose in a same strategy, a robust and simple way to solve the fingertip path planning and the fingertip force computation. First, finger path planning is based on a geometric approach, and on a contact modelling between the grasped object and the finger. And as force sensing is required for force control, a new original approach based on neural networks and on the use of tendon-driven joints is also used to evaluate the normal force acting on the finger distal phalanx. And an efficient algorithm that computes fingertip forces involved is presented in the case of three dimensional object grasps. Based on previous works, those forces are computed by using a robust optimization scheme.In order to validate this strategy, different grasps and different manipulation tasks are presented and detailed with a simulation software, SMAR, developed by the PPRIME Institute. And finally experimental results with the real hand illustrate the efficiency of the whole approach.     

Geometry-based flipper motion planning for articulated tracked robots traversing rough terrain in real-time

Tracked robots operating on rough terrain are often equipped with controllable flippers to help themselves overcome large obstacles or gaps. How to automate the control of these auxiliary flippers to achieve autonomous traversal remains an open question, which still necessitates inefficient manual teleoperation in practice. To tackle this problem, this article presents a geometry-based motion planning method for an articulated tracked robot to self-control its flippers during autonomous or semiautonomous traversal over rough terrain in urban search and rescue environments. The proposed method is developed by combining dynamic programming with a novel geometry-based pose prediction method of high computational efficiency, which is applicable for typical challenging rescue terrains, such as stairs, Stepfields, and rails. The efficient pose prediction method allows us to make thousands of predictions about the robot poses at future locations for given flipper configurations within the onboard sensor range. On the basis of such predictions, our method evaluates the entire discretized configuration space and thereby determines the optimal flipper motion online for a smooth traversal over the terrain. The overall planning algorithm is tested with both simulated and real-world robots and compared with a reinforcement-learning-based method using the RoboCup Rescue Robot League standard testing scenarios. The experimental results show that our method enables the robots to automatically control the flippers, successfully go over challenging terrains, and outperform the baseline method in passing smoothness and robustness to different terrains.     

Global motion planning under uncertain motion, sensing, and environment map

Uncertainty in motion planning is often caused by three main sources: motion error, sensing error, and imperfect environment map. Despite the significant effect of all three sources of uncertainty to motion planning problems, most planners take into account only one or at most two of them. We propose a new motion planner, called Guided Cluster Sampling (GCS), that takes into account all three sources of uncertainty for robots with active sensing capabilities. GCS uses the Partially Observable Markov Decision Process (POMDP) framework and the point-based POMDP approach. Although point-based POMDPs have shown impressive progress over the past few years, it performs poorly when the environment map is imperfect. This poor performance is due to the extremely high dimensional state space, which translates to the extremely large belief space?B. We alleviate this problem by constructing a more suitable sampling distribution based on the observations that when the robot has active sensing capability, B can be partitioned into a collection of much smaller sub-spaces, and an optimal policy can often be generated by sufficient sampling of a small subset of the collection. Utilizing these observations, GCS samples B in two-stages, a subspace is sampled from the collection and then a belief is sampled from the subspace. It uses information from the set of sampled sub-spaces and sampled beliefs to guide subsequent sampling. Simulation results on marine robotics scenarios suggest that GCS can generate reasonable policies for motion planning problems with uncertain motion, sensing, and environment map, that are unsolvable by the best point-based POMDPs today. Furthermore, GCS handles POMDPs with continuous state, action, and observation spaces. We show that for a class of POMDPs that often occur in robot motion planning, given enough time, GCS converges to the optimal policy. To the best of our knowledge, this is the first convergence result for point-based POMDPs with continuous action space.     

Improving lifting motion planning and re-planning of cranes with consideration for safety and efficiency

Safe and efficient operation of cranes requires not only good planning, but also sufficient and appropriate support in real time. Due to the dynamic nature of construction sites, unexpected changes in site layout may create new obstacles for the crane that can result in collisions and accidents. Previous research on construction equipment motion planning focuses on off-line support, which considers static environment or predictable obstacles. These plans may not fit the reality when the environment has any change. In this case on-site safety and efficiency can be affected. In this research, a motion planning algorithm is proposed to efficiently generate safe and smooth paths for crane motions while taking into account engineering constraints and the path quality. Path smoothness is taken into account to provide a realistic path for cranes and to reduce unnecessary movements. A dynamic motion planning algorithm is proposed to ensure safety during the execution stage by quickly re-planning and avoiding collisions. In addition, an anytime algorithm is proposed to search for better solutions during a given time period by improving path smoothness and by reducing path execution time. The proposed algorithms are compared with other available algorithms to evaluate their performance in terms of planning and re-planning time and the cost of the path. Based on the literature review, this is the first time that dual-tree RRT algorithms have been applied to crane motion planning.     

Geometry and search in motion planning

Robot path planning is a typical example of a problem that requires searching a “continuous” space, the robot's configuration space, for a solution, a collision-free path. The global approach to path planning first captures the connectivity of the robot's free space into a concise connectivity path, and next searches this graph. The local approach directly embarks on a search procedure, and performs geometric computation according to the needs of the search. Global methods may waste a large amount of computation before they have any chance to find a path. On the other hand, local methods, which lack the global vision provided by the connectivity graph, have very poor worst-case complexity. Is it possible to instill some local opportunism in a global approach, or a limited amount of precomputed global information in a local approach? More generally: How can geometric computation and search help each other to produce a path quickly? These questions probably do not have definite domain-independent answers. However, raising them may help us engineer path planners that better meet specific application needs. This paper considers these questions through a series of informal case studies, each corresponding to a particular way to engineer the interaction between geometry and search in a path planner.     

On point-to-point motion planning for underactuated space manipulator systems

In free-floating mode, space manipulator systems have their actuators turned off, and exhibit nonholonomic behavior due to angular momentum conservation. The system is underactuated and a challenging problem is to control both the location of the end effector and the attitude of the base, using manipulator actuators only. Here a path planning methodology satisfying this requirement is developed. The method uses high order polynomials, as arguments in cosine functions, to specify the desired path directly in joint-space. In this way, the accessibility of final configurations is extended drastically, and the free parameters are determined by optimization techniques. It was found that this approach leads always to a path, provided that the desired change in configuration lies between physically permissible limits. Physical limitations, imposed by system’s dynamic parameters, are examined. Lower and upper bounds for base rotation, due to manipulator motions, are estimated and shown in the implementation section. The presented method avoids the need for many small cyclical motions, and uses smooth functions in the planning scheme, leading to smooth configuration changes in finite and prescribed time.     

Robust real-time periodic motion detection, analysis, andapplications

We describe new techniques to detect and analyze periodic motion as seen from both a static and a moving camera. By tracking objects of interest, we compute an object's self-similarity as it evolves in time. For periodic motion, the self-similarity measure is also periodic and we apply time-frequency analysis to detect and characterize the periodic motion. The periodicity is also analyzed robustly using the 2D lattice structures inherent in similarity matrices. A real-time system has been implemented to track and classify objects using periodicity. Examples of object classification (people, running dogs, vehicles), person counting, and nonstationary periodicity are provided     

Algorithmic motion planning in robotics

A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning     

Mixed-integer programming in motion planning

This paper presents a review of past and present results and approaches in the area of motion planning using MIP (Mixed-integer Programming). Although in the early 2000s MIP was still seen with reluctance as method for solving motion planning-related problems, nowadays, due to increases in computational power and theoretical advances, its extensive modeling capabilities and versatility are coming to the fore and enjoy increased application and appreciation. This class of control problems involves, essentially, either a selection from a limited number of alternatives or a constrained optimization problem over a non-convex domain. In both situations, MIP has proven to be an efficient modeling technique as it will be shown in the present review paper. Furthermore, an emphasis is laid on the existing alternatives for implementation and on various experimental validations documented in the literature.     

Collision-free motion planning and scheduling

The positioning, motion coordination and test ordering procedures of new testing equipment for printed circuit boards is presented. The equipment structure consists of four mobile probes whose movements must be coordinated to avoid collisions both with obstacles and with each other. This paper explains the algorithm used to obtain the optimum path between two points in a space with obstacles based on traditional methods but including new ideas to reduce the computation times. A new method for motion coordination that obtains the optimum sequence of consecutive or simultaneous probe movements is also introduced and is based on the decomposition of the sequence of movements into stages in which only movements without interferences are permitted. Finally, the ordering of the tests can be viewed as a traveling salesman problem, and the paper presents the results of various methods when applied to the specific characteristics of the equipment involved.     

On the interplay between consistency,completeness, and correctness in requirements evolution

The initial expression of requirements for a computer-based system is often informal and possibly vague. Requirements engineers need to examine this often incomplete and inconsistent brief expression of needs. Based on the available knowledge and expertise, assumptions are made and conclusions are deduced to transform this ‘rough sketch’ into more complete, consistent, and hence correct requirements. This paper addresses the question of how to characterize these properties in an evolutionary framework, and what relationships link these properties to a customer's view of correctness. Moreover, we describe in rigorous terms the different kinds of validation checks that must be performed on different parts of a requirements specification in order to ensure that errors (i.e. cases of inconsistency and incompleteness) are detected and marked as such, leading to better quality requirements.     

A genetic approach to motion planning of redundant mobile manipulator systems considering safety and configuration

This article presents a genetic algorithm approach to multi-criteria motion planning of mobile manipulator systems. For mobile robot path planning, traveling distance and path safety are considered. The workspace of a mobile robot is represented as a grid by cell decomposition, and a wave front expansion algorithm is used to build the numerical potential fields for both the goal and the obstacles. For multi-criteria position and configuration optimization of a mobile manipulator, least torque norm, manipulability, torque distribution and obstacle avoidance are considered. The emphasis of the study is placed on using genetic algorithms to search for global optimal solutions and solve the minimax problem for manipulator torque distribution. Various simulation results from two examples show that the proposed genetic algorithm approach performs better than the conventional methods.     

Game-theoreticmulti-agent motion planning in amixed environment

The motion planning problem for multi-agent systems becomes particularly challenging when humans or human-controlled robots are present in amixed environment. To address this challenge, this paper presents an interaction-awaremotion planning approach based on game theory in a receding-horizon manner. Leveraging the framework provided by dynamic potential games for handling the interactions among agents, this approach formulates the multi-agent motion planning problem as a differential potential game, highlighting the effectiveness of constrained potential games in facilitating interactive motion planning among agents. Furthermore, online learning techniques are incorporated to dynamically learn the unknown preferences and models of humans or human-controlled robots through the analysis of observed data. To evaluate the effectiveness of the proposed approach, numerical simulations are conducted, demonstrating its capability to generate interactive trajectories for all agents, including humans and human-controlled agents, operating within the mixed environment. The simulation results illustrate the effectiveness of the proposed approach in handling the complexities of multi-agent motion planning in real-world scenarios.     

Roadmap-based motion planning in dynamic environments

In this paper, a new method is presented for motion planning in dynamic environments, that is, finding a trajectory for a robot in a scene consisting of both static and dynamic, moving obstacles. We propose a practical algorithm based on a roadmap that is created for the static part of the scene. On this roadmap, an approximately time-optimal trajectory from a start to a goal configuration is computed, such that the robot does not collide with any moving obstacle. The trajectory is found by performing a two-level search for a shortest path. On the local level, trajectories on single edges of the roadmap are found using a depth-first search on an implicit grid in state-time space. On the global level, these local trajectories are coordinated using an A/sup */-search to find a global trajectory to the goal configuration. The approach is applicable to any robot type in configuration spaces with any dimension, and the motions of the dynamic obstacles are unconstrained, as long as they are known beforehand. The approach has been implemented for both free-flying and articulated robots in three-dimensional workspaces, and it has been applied to multirobot motion planning, as well. Experiments show that the method achieves interactive performance in complex environments.     

On the feasibility of real-time prediction of aircraft carrier motion at sea

Landing aircraft on board carriers is a most delicate phase of flight operations at sea. The ability to predict the aircraft carrier's motion over an interval of several seconds within reasonable error bounds may allow an improvement in touchdown dispersion and reduce the value of the ramp clearance due to a smoother aircraft trajectory. Also, improved information to the landing signal officer should decrease the number of waveoffs substantially. This paper indicates and shows quantitatively that, based upon the power density spectrum data for pitch and heave measured for various ships and sea conditions, the motion can be predicted well for up to 15 s. Moreover, the zero crossover times for both pitch and heave motions can be predicted with impressive accuracy. The predictor was designed on the basis of Kalman's optimum filtering theory (the discrete time case), being compatible with real-time digital computer operation.     

Reliability and safety of real-time systems

Not a day goes by that the general public does not come into contact with a real-time system. As their numbers and importance grow, so do the stakes for software developers. A failure in a critical application may result in great financial loss-or even loss of life. More effort must be expended to analyze the reliability and safety of such systems. Analysis of hardware components in critical applications has matured over the years and commonly followed techniques have emerged. However, methods and techniques for analyzing the reliability and safety of the software part of critical applications are relatively new and still maturing. Yet the vulnerability of the system to software failures is on the rise and may (and in some cases does) exceed hardware failures. Software is not only becoming more prevalent in real-time systems, it is becoming a larger part of real-time systems, in the sense that the amount of effort expended in designing and implementing the software is a larger proportion of the total expended effort