Motion Planning Technique Research Articles

Optimal motion planning and stopping test for 3-D object reconstruction

In this work, two aspects of motion planning for object reconstruction are investigated. First, the effect of using a sampling-based optimal motion planning technique to move a mobile manipulator robot with 8 degrees of freedom, during the reconstruction process, in terms of several performance criteria is studied. Based on those criteria, the results of the reconstruction task using rapidly exploring random tree (RRT) approaches are compared, more specifically RRT* smart versus RRT* versus standard RRT. Second, the problem of defining a convenient stopping probabilistic test to terminate the reconstruction process is addressed. Based on our results, it is concluded that the use of a RRT* improves the measured performance criteria compared with a standard RRT. The simulation experiments show that the proposed stopping test is adequate. It stops the reconstruction process when all the portions of object that are possible to be seen have been covered with the field of view of the sensor.

Optimal Player Policies for Continuous Ambush Games

An autonomous navigation problem is considered, whereby a traveler aims at traversing an environment in which an adversary sets an ambush. A two-player zero-sum game is introduced describing the players’ initial strategy based on the traveler’s initial location and desired goal, and the description of the environment. The process is single step in the sense that agents do not reevaluate their strategy after the traveler has started moving. The players’ strategies are computed as probabilistic distributions, a realization of which is the path chosen by the traveler and the ambush location chosen by the ambusher. Assumptions from the minimal cut–maximal flow literature for continuous domains are used. The optimal value of the game is shown to be related to the maximum flow on the environment for subclasses of games in which the reward function for the ambusher is uniform over the environment. To relax the assumptions for the computation of the players’ optimal strategies, a sampling-based approach is proposed, inspired by sampling-based motion-planning techniques. Given a uniform reward function for the ambusher, optimal strategies of the sampling-based ambush game are proven to converge to the optimal strategy of the continuous ambush game. A linear program is introduced that allows for the computation of optimal policies. The sampling-based approach is more general in the sense that it is compatible with constrained motion primitives for the traveler and nonuniform reward functions for the ambusher.

Realtime Hand-Object Interaction Using Learned Grasp Space for Virtual Environments.

We present a realtime virtual grasping algorithm to model interactions with virtual objects. Our approach is designed for multi-fingered hands and makes no assumptions about the motion of the user's hand or the virtual objects. Given a model of the virtual hand, we use machine learning and particle swarm optimization to automatically pre-compute stable grasp configurations for that object. The learning pre-computation step is accelerated using GPU parallelization. At runtime, we rely on the pre-computed stable grasp configurations, and dynamics/non-penetration constraints along with motion planning techniques to compute plausible looking grasps. In practice, our realtime algorithm can perform virtual grasping operations in less than 20ms for complex virtual objects, including high genus objects with holes. We have integrated our grasping algorithm with Oculus Rift HMD and Leap Motion controller and evaluated its performance for different tasks corresponding to grabbing virtual objects and placing them at arbitrary locations. Our user evaluation suggests that our virtual grasping algorithm can increase the user's realism and participation in these tasks and offers considerable benefits over prior interaction algorithms, such as pinch grasping and raycast picking.

Literature survey for autonomous vehicles: sensor fusion, computer vision, system identification and fault tolerance

Autonomous vehicle technologies are receiving great attention with increasing demands for autonomy for both civilian and military purposes. In previous work (Mohamed et al., 2016), the recent developments in autonomous vehicles in the fields of advanced control, perception and motion planning techniques is surveyed. In this paper, the state of research w.r.t. autonomous vehicles from different perspectives will be described. The capability to integrate data and knowledge from different sensors are essential. In addition, advanced perception techniques and the capability to locate obstacles and targets are necessary to properly operate autonomous systems. Moreover, achieve reliable levels of performance by determining the faults and enabling the system to operate with these faults in mind. Fault tolerance is required to analysing the measured input/output signals of the system. This paper will briefly survey the recent developments in the field of autonomous vehicles from the perspectives of sensor fusion, computer vision, system identification and fault tolerance.

Boundary Control of Reacting Species in Semi-infinite and Finite Diffusion Processes

Abstract Boundary control of oxidizing species at the cathode surface is considered in an electrochemical system, which is the plant of two electrodes and plant of three electrodes submerged in the stirred and unstirred electrolytes. The exact feedforward controls are found using ideas of motion planning, Laplace transform and other techniques that in the end solve the inverse problem for the given time-variable reference of species at the cathode surface. The uncertainty of surface reaction existing in the system is suppressed with two modifications of controls. In the first case, the feedforward control is extended with a PI feedback that uses measured in the process electric current passing through the system. In the second case, an adaptive feedforward control is established for the case when the exchange current density is unknown drifting parameter and is estimated as it changes using a conditionally Gaussian filter. In both cases, the process control is simulated and revealed to be effective.

Investigation of Machine Learning Techniques for Motion Planning and Control in Animation

Investigation of Machine Learning Techniques for Motion Planning and Control in Animation

SDRE controller for motion design of cable-suspended robot with uncertainties and moving obstacles

In this paper an optimal control approach for nonlinear dynamical systems was proposed based on State Dependent Riccati Equation (SDRE) and its robustness against uncertainties is shown by simulation results. The proposed method was applied on a spatial six-cable suspended robot, which was designed to carry loads or perform different tasks in huge workspaces. Motion planning for cable-suspended robots in such a big workspace is subjected to uncertainties and obstacles. First, we emphasized the ability of SDRE to construct a systematic basis and efficient design of controller for wide variety of nonlinear dynamical systems. Then we showed how this systematic design improved the robustness of the system and facilitated the integration of motion planning techniques with the controller. In particular, obstacle avoidance technique based on artificial potential field (APF) can be easily combined with SDRE controller with efficient performance. Due to difficulties of exact solution for SDRE, an approximation method was used based on power series expansion. The efficiency and robustness of the SDRE controller was illustrated on a six-cable suspended robot with proper simulations.

Heuristic Guidance Techniques for the Exploration of Small Celestial Bodies

Sampling Based Motion Planning (SBMP) techniques are widely used in robotics to plan feasible trajectories of a vehicle/robot evolving in a complex and constrained environment. Algorithms such as Rapidly Exploring Random Trees (RRT) and Sampling Based Model Predictive Optimization (SBMPO) allow for an efficient exploration of the state space, and the construction of a feasible sequence of maneuvers and trajectories that respect the kinodynamic and path constraints of the system. Proximity operations around small bodies are characterized by complex dynamics and constraints that can be easily and autonomously handled by motion planning techniques. This paper presents two motion planning algorithms designed to solve two different guidance problems: the landing on a small body and its observation. The mission scenarios considered to test the algorithms are the landing of Rosetta on the comet 67P/Churyumov-Gerasimenko and the observation of Didymain in the Didymos binary asteroid system. To conclude, the applicability of SBMP techniques to small body proximity operations are discussed. In particular, the advantages of implementing SBMP algorithms to solve complex high-level planning problems or to guide a spacecraft in a cluttered environment are highlighted.

Cooperative minimum expected length planning for robot formations in stochastic maps

This paper addresses a tightly integrated multi-robot planning, localization and navigation system in stochastic scenarios. We present a novel motion planning technique for robot formations in such kinds of environments, which computes the most likely global path in terms of a defined minimum expected length (EL). EL evaluates the expected cost of a path considering the probability of finding a non traversable zone and the cost of using an alternative traversable path. A local real time re-planning technique based on the probabilistic model is also developed for the formation when the scenario changes. The formation adapts its configuration to the shape of the free room. The partial views of all the robots are integrated to update the multi-robot localization using a modified EKF based on the measurement differencing technique which improves estimation consistency. As a result, a lower uncertainty map of the local navigation area is obtained for re-planning purposes. Experimental results, both in simulation and in real office-like settings, illustrate the performance of the described approach where a hybrid, centralized–distributed, architecture with wireless communication capabilities is used.

Robot Motion Planning Using Adaptive Hybrid Sampling in Probabilistic Roadmaps

Motion planning deals with finding a collision-free trajectory for a robot from the current position to the desired goal. For a high-dimensional space, sampling-based algorithms are widely used. Different sampling algorithms are used in different environments depending on the nature of the scenario and requirements of the problem. Here, we deal with the problems involving narrow corridors, i.e., in order to reach its destination the robot needs to pass through a region with a small free space. Common samplers used in the Probabilistic Roadmap are the uniform-based sampler, the obstacle-based sampler, maximum clearance-based sampler, and the Gaussian-based sampler. The individual samplers have their own advantages and disadvantages; therefore, in this paper, we propose to create a hybrid sampler that uses a combination of sampling techniques for motion planning. First, the contribution of each sampling technique is deterministically varied to create time efficient roadmaps. However, this approach has a limitation: The sampling strategy cannot adapt as per the changing configuration spaces. To overcome this limitation, the sampling strategy is extended by making the contribution of each sampler adaptive, i.e., the sampling ratios are determined on the basis of the nature of the environment. In this paper, we show that the resultant sampling strategy is better than commonly used sampling strategies in the Probabilistic Roadmap approach.

A Review of Motion Planning Techniques for Automated Vehicles

Intelligent vehicles have increased their capabilities for highly and, even fully, automated driving under controlled environments. Scene information is received using onboard sensors and communication network systems, i.e., infrastructure and other vehicles. Considering the available information, different motion planning and control techniques have been implemented to autonomously driving on complex environments. The main goal is focused on executing strategies to improve safety, comfort, and energy optimization. However, research challenges such as navigation in urban dynamic environments with obstacle avoidance capabilities, i.e., vulnerable road users (VRU) and vehicles, and cooperative maneuvers among automated and semi-automated vehicles still need further efforts for a real environment implementation. This paper presents a review of motion planning techniques implemented in the intelligent vehicles literature. A description of the technique used by research teams, their contributions in motion planning, and a comparison among these techniques is also presented. Relevant works in the overtaking and obstacle avoidance maneuvers are presented, allowing the understanding of the gaps and challenges to be addressed in the next years. Finally, an overview of future research direction and applications is given.

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles

Self-driving vehicles are a maturing technology with the potential to reshape mobility by enhancing the safety, accessibility, efficiency, and convenience of automotive transportation. Safety-critical tasks that must be executed by a self-driving vehicle include planning of motions through a dynamic environment shared with other vehicles and pedestrians, and their robust executions via feedback control. The objective of this paper is to survey the current state of the art on planning and control algorithms with particular regard to the urban setting. A selection of proposed techniques is reviewed along with a discussion of their effectiveness. The surveyed approaches differ in the vehicle mobility model used, in assumptions on the structure of the environment, and in computational requirements. The side by side comparison presented in this survey helps to gain insight into the strengths and limitations of the reviewed approaches and assists with system level design choices.

Safe and efficient motion planning of multiple mobile robots based on artificial potential for human behavior and robot congestion

In order for robots to exist together with humans, safety for the humans has to be strictly ensured. On the other hand, safety might decrease working efficiency of robots. Namely, this is a trade-off problem between human safety and robot efficiency in a field of human–robot interaction. For this problem, we propose a novel motion planning technique of multiple mobile robots. Two artificial potentials are presented for generating repulsive force. The first potential is provided for humans. The von Mises distribution is used to consider the behavioral property of humans. The second potential is provided for the robots. The Kernel density estimation is used to consider the global robot congestion. Through simulation experiments, the effectiveness of the behavior and congestion potentials of the motion planning technique for human safety and robot efficiency is discussed. Moreover, a sensing system for humans in a real environment is developed. From experimental results, the significance of the behavior potential based on the actual humans is discussed. For the coexistence of humans and robots, it is important to evaluate a mutual influence between them. For this purpose, a virtual space is built using projection mapping. Finally, the effectiveness of the motion planning technique for the human–robot interaction is discussed from the point of view of not only robots but also humans.

Hybrid Motion Planning Method for Autonomous Robots Using Kinect Based Sensor Fusion and Virtual Plane Approach in Dynamic Environments

A new reactive motion planning method for an autonomous vehicle in dynamic environments is proposed. The new dynamic motion planning method combines a virtual plane based reactive motion planning technique with a sensor fusion based obstacle detection approach, which results in improving robustness and autonomy of vehicle navigation within unpredictable dynamic environments. The key feature of the new reactive motion planning method is based on a local observer in the virtual plane which allows the effective transformation of complex dynamic planning problems into simple stationary in the virtual plane. In addition, a sensor fusion based obstacle detection technique provides the pose estimation of moving obstacles by using a Kinect sensor and a sonar sensor, which helps to improve the accuracy and robustness of the reactive motion planning approach in uncertain dynamic environments. The performance of the proposed method was demonstrated through not only simulation studies but also field experiments using multiple moving obstacles even in hostile environments where conventional method failed.

Improved APF strategies for dual-arm local motion planning

Manipulator motion planning is a classic problem in robotics, with a number of complete solutions available for their motion in controlled (industrial) environments. Owing to recent technological advances in the field of robotics, there has been a significant development of more complex robots with high-fidelity sensors and more computational power. One such example has been a rise in the production of humanoid robots equipped with dual-arm manipulators which require complex motion planning algorithms. Also, the technological advances have resulted in a shift from using manipulators in strictly controlled environments, to investigating the deployment of manipulators in dynamic or unknown environments. As a result, a greater emphasis has been put on the development of local motion planners, which can provide real-time solutions to these problems. Artificial Potential Fields (APFs) is one such popular local motion planning technique, which can be applied to manipulator motion planning, however, the basic algorithm is severely prone to local minima problems. Here, two modified APF-based strategies for solving the dual-arm motion planning task in unknown environments are proposed. Both techniques make use of configuration sampling and subgoal selection to assist the APFs in avoiding these local minima scenarios. Extensive simulation results are presented to validate the efficacy of the proposed methodology.

Towards Synthesizing Energy-efficient Controllers for Modern Production Systems from Scenario-based Specifications

Abstract Due to continuously rising electricity prices, the energy efficiency of production systems is an increasingly important factor in industrial manufacturing. One goal is therefore to reuse the braking energy of manipulator axes. The acceleration and deceleration phases of axes must be synchronized such that, ideally, the braking energy can be reused immediately within the system. In our current technique for the energy-efficient motion planning of concurrent movements of axes, we do not yet consider changing the discrete control logic to reorder movement sequences. This is difficult, since this reordering must not violate critical system requirements. In this paper, we outline a new technique for automatically synthesizing energy-efficient discrete controllers from a scenario-based specification of a production system, which we enrich with information about consumed and generated energy. Last, we provide an outlook on challenging open research problems.

Path-Constrained Motion Analysis: An Algorithm to Understand Human Performance on Hydraulic Manipulators

We propose a novel method to analyze how human operators use hydraulic manipulators of heavy-duty equipment. The approach is novel in the sense that it applies knowledge of motion planning and optimization techniques used in robotics. As an example, we consider the case of operating a forestry crane. To that end, we use motion data that has been recorded during standard operation with the help of sensors and a data acquisition unit. The data backs up the notion that operators work by performing repeatable patterns observed in the trajectories of the manipulator's joints. We show how this nominal behavior is computed, and consequently, this allows us to present the following: 1) an analytical procedure to analyze motions, 2) how to represent the “performance” of the operator in a 2-D plot, 3) an example of how to use this information to suggest customized control settings, and 4) some complementary ideas needed for improving efficiency through automation.

Sensor-based Navigation and Integrated Control of Ambient Intelligent Wheeled Robots with Tire-Ground Interaction Uncertainties

This paper regards the synthesis of intelligent non-visual sensor-based navigation, motion planning and the integrated control of indoor ambient adaptive wheel- based mobile robots in unknown environments with tire-ground interaction uncertainties. The problem relates to searching appropriate techniques how to navigate towards a target position in an unknown environment when the obstacles to avoid are discovered in real time, and how to maintain collision free motion of a high dynamic performance. Environments characterized by variable ground surface conditions with immobile obstacles of different shapes and sizes will be considered in the paper as unexpected disturbances, i.e. system uncertainties. The tools developed to address this issue thus consist of the combination of cognitive motion planning and control theory techniques, including a non- linear model-based approach. Two characteristic approaches to integrated control are evaluated in the paper: a kinematical as well as dynamic one, in the sense of control efficiency and robustness to the environmental and model uncertainties. Characteristic simulation tests are performed to verify the proposed algorithms.

Solving Optimal Control Problems by Exploiting Inherent Dynamical Systems Structures

Computing globally efficient solutions is a major challenge in optimal control of nonlinear dynamical systems. This work proposes a method combining local optimization and motion planning techniques based on exploiting inherent dynamical systems structures, such as symmetries and invariant manifolds. Prior to the optimal control, the dynamical system is analyzed for structural properties that can be used to compute pieces of trajectories that are stored in a motion planning library. In the context of mechanical systems, these motion planning candidates, termed primitives, are given by relative equilibria induced by symmetries and motions on stable or unstable manifolds of e.g. fixed points in the natural dynamics. The existence of controlled relative equilibria is studied through Lagrangian mechanics and symmetry reduction techniques. The proposed framework can be used to solve boundary value problems by performing a search in the space of sequences of motion primitives connected using optimized maneuvers. The optimal sequence can be used as an admissible initial guess for a post-optimization. The approach is illustrated by two numerical examples, the single and the double spherical pendula, which demonstrates its benefit compared to standard local optimization techniques.

Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance

This paper provides a survey of motion planning techniques under uncertainty with a focus on their application to autonomous guidance of unmanned aerial vehicles (UAVs). The paper first describes the primary sources of uncertainty arising in UAV guidance and then describes relevant practical techniques that have been reported in the literature. The paper makes a point of distinguishing between contributions from the field of robotics and artificial intelligence, and the field of dynamical systems and controls. Mutual and individual contributions for these fields are highlighted providing a roadmap for tackling the UAV guidance problem.