Motion Planning Algorithms Research Articles

A comparative review on mobile robot path planning: Classical or meta-heuristic methods?

The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.

Virtual Target-Based Overtaking Decision, Motion Planning, and Control of Autonomous Vehicles

This paper describes the design, implementation, and evaluation of a virtual target-based overtaking decision, motion planning, and control algorithm for autonomous vehicles. Both driver acceptance and safety, when surrounded by other vehicles, must be considered during autonomous overtaking. These are considered through safe distance based on human driving behavior. Since all vehicles cannot be equipped with a vehicle to vehicle communications at present, autonomous vehicles should perceive the surrounding environment based on local sensors. In this paper, virtual targets are devised to cope with the limitation of cognitive range. A probabilistic prediction is adopted to enhance safety, given the potential behavior of surrounding vehicles. Then, decision-making and motion planning has been designed based on the probabilistic prediction-based safe distance, which could achieve safety performance without a heavy computational burden. The algorithm has considered the decision rules that drivers use when overtaking. For this purpose, concepts of target space, demand, and possibility for lane change are devised. In this paper, three driving modes are developed for active overtaking. The desired driving mode is decided for safe and efficient overtaking. To obtain desired states and constraints, intuitive motion planning is conducted. A stochastic model predictive control has been adopted to determine vehicle control inputs. The proposed autonomous overtaking algorithm has been evaluated through simulation, which reveals the effectiveness of virtual targets. Also, the proposed algorithm has been successfully implemented on an autonomous vehicle and evaluated via real-world driving tests. Safe and comfortable overtaking driving has been demonstrated using a test vehicle.

A Progressive Motion-Planning Algorithm and Traffic Flow Analysis for High-Density 2D Traffic

Unmanned aircraft systems (UASs) are a promising new mode of transportation for cargo delivery. Although control, navigation, and communication technologies are becoming available on individual flight units, system-level motion management for dense UAS traffic remains an open question. This paper presents a motion-planning model based on nonlinear optimization techniques to centrally coordinate paths for all vehicles traversing a shared two-dimensional (2D) space. An exact bound is derived to characterize the discrete-time separation constraints, and a set of new metrics is proposed to measure 2D traffic flow efficiency. The grand goal is to make all vehicles that come under dispatch reach their respective destinations quickly and efficiently while maintaining a safe intervehicle separation at all times. This infinite-horizon operational problem is formulated as a nonlinear nonconvex optimization model that must be solved in a progressive, receding-horizon fashion. To ensure feasibility and overcome path deadlocks attributed to local optima, a series of heuristic measures are developed. By pivoting on artfully coined intermediate feasibility, the algorithm is able to circumvent imminent deadlocks in a predictive manner and progressively construct the solution with guaranteed feasibility. Simulation experiments are performed at various traffic density levels to generate useful insights for airspace regulators and traffic managers.

Design and Motion Planning of a Biped Climbing Robot with Redundant Manipulator

A novel robot capable of performing maintenance and inspection tasks for railway bridges is proposed in this paper. Termed CMBOT (climbing manipulator robot), the robot is a combination of a five-degrees-of-freedom (5-Dof) biped climbing robot with two electromagnetic feet and a redundant manipulator with 7-Dof. This capability offers important advantages for performing maintenance and inspection tasks for railway bridges. Several fundamental issues of the CMBOT, such as robotic system development and motion planning algorithms, are addressed in this paper. A series of simulations and prototype experiments were conducted to validate the proposed robotic systems and motion planning algorithm. The results of the experiments show the reliability of the robotic systems and the efficiency of the motion planning algorithm.

An Advanced Motion Planning Algorithm for the Multi Transportation Mobile Robot

An Advanced Motion Planning Algorithm for the Multi Transportation Mobile Robot

A Review of Motion Planning for Highway Autonomous Driving

Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people’s way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.

A sampling-based optimized algorithm for task-constrained motion planning

We consider a motion planning problem with task space constraints in a complex environment for redundant manipulators. For this problem, we propose a motion planning algorithm that combines kinematics control with rapidly exploring random sampling methods. Meanwhile, we introduce an optimization structure similar to dynamic programming into the algorithm. The proposed algorithm can generate an asymptotically optimized smooth path in joint space, which continuously satisfies task space constraints and avoids obstacles. We have confirmed that the proposed algorithm is probabilistically complete and asymptotically optimized. Finally, we conduct multiple experiments with path length and tracking error as optimization targets and the planning results reflect the optimization effect of the algorithm.

Sampling-based incremental information gathering with applications to robotic exploration and environmental monitoring

We propose a sampling-based motion-planning algorithm equipped with an information-theoretic convergence criterion for incremental informative motion planning. The proposed approach allows dense map representations and incorporates the full state uncertainty into the planning process. The problem is formulated as a constrained maximization problem. Our approach is built on rapidly exploring information-gathering algorithms and benefits from the advantages of sampling-based optimal motion-planning algorithms. We propose two information functions and their variants for fast and online computations. We prove an information-theoretic convergence for an entire exploration and information-gathering mission based on the least upper bound of the average map entropy. A natural automatic stopping criterion for information-driven motion control results from the convergence analysis. We demonstrate the performance of the proposed algorithms using three scenarios: comparison of the proposed information functions and sensor configuration selection, robotic exploration in unknown environments, and a wireless signal strength monitoring task in a lake from a publicly available dataset collected using an autonomous surface vehicle.

PSO-based Minimum-time Motion Planning for Multiple Vehicles Under Acceleration and Velocity Limitations

This paper discusses a particle swarm optimization (PSO)-based motion-planning algorithm in a multiple-vehicle system that minimizes the traveling time of the slowest vehicle by considering, as constraints, the radial and tangential accelerations and maximum linear velocities of all vehicles. A class of continuous-curvature curvesthree-degree Bezier curvesis selected as the basic shape of the vehicle trajectories to minimize the number of parameters required to express them mathematically. In addition, velocity profile generation using the local minimum of the radial-accelerated linear velocity profile, which reduces the calculation effort, is introduced. A new PSO-based search algorithm, called “particle-group-based PSO,” is introduced to find the best combination of trajectories that minimizes the traveling time of the slowest vehicle. A particle group is designed to wrap a set of particles representing each vehicle. The first and last two control points characterizing a curve are used as the state vector of a particle. Simulation results demonstrating the performance of the proposed method are presented. The main advantage of the proposed method is its minimization of the velocity-profile-generation time, and thereby, its maximization of the search time.

Motion Plan of Maritime Autonomous Surface Ships by Dynamic Programming for Collision Avoidance and Speed Optimization.

Maritime Autonomous Surface Ships (MASS) with advanced guidance, navigation, and control capabilities have attracted great attention in recent years. Sailing safely and efficiently are critical requirements for autonomous control of MASS. The MASS utilizes the information collected by the radar, camera, and Autonomous Identification System (AIS) with which it is equipped. This paper investigates the problem of optimal motion planning for MASS, so it can accomplish its sailing task early and safely when it sails together with other conventional ships. We develop velocity obstacle models for both dynamic and static obstacles to represent the potential conflict-free region with other objects. A greedy interval-based motion-planning algorithm is proposed based on the Velocity Obstacle (VO) model, and we show that the greedy approach may fail to avoid collisions in the successive intervals. A way-blocking metric is proposed to evaluate the risk of collision to improve the greedy algorithm. Then, by assuming constant velocities of the surrounding ships, a novel Dynamic Programming (DP) method is proposed to generate the optimal multiple interval motion plan for MASS. These proposed algorithms are verified by extensive simulations, which show that the DP algorithm provides the lowest collision rate overall and better sailing efficiency than the greedy approaches.

Bidirectional Potential Guided RRT* for Motion Planning

Requirement for high accuracy and speed of grasping operation for motion planning is very important. Motion planning algorithms for avoiding obstacles in narrow channels play a vital role for robotic arm effectively operating grasp tasks. The potential function-based RRT*-connect (P-RRT*-connect) algorithm for motion planning is presented by combining the bidirectional artificial potential field into the rapidly exploring random tree star (RRT*) in order to enhance the performance of the RRT*. The motion path is found out by exploring two path trees from the start node and destination node, respectively, with the rapidly exploring random tree star. Two trees advance each other at the same time according to the attractive potential field and the repulsion potential field generated by the artificial potential field method of sampled nodes until they meet. The P-RRT*-connect algorithm is especially suitable for solving the problem of narrow channels. The simulation results prove that the P-RRT*-connect algorithm is more efficient than potential Function-based RRT* (P-RRT*) regardless of the number of iterations or the running time. The experimental data show that the time for the P-RRT*-connect to find the optimal path from the starting node to the target node is half than that of the P-RRT*, and the number of iterations of the P-RRT*-connect is also about one-third less than that of the P-RRT* which is useful for real time.

LaLo-Check: A Path Optimization Framework for Sampling-Based Motion Planning With Tree Structure

Motion planning is a basic problem in many areas, such as robotics, computer games, and animation. It is a challenge to generate a better solution in less time. Line-of-Sight (LoS) is the straight path between two points, and LoS-Check is often used as a path shortcutting method. Lazy evaluation is a successful strategy in reducing the amount of collision detection, which accelerates the motion planning algorithms, especially in high-dimensional spaces. In this paper, we present Lazy LoS-Check (LaLo-Check), which employs a lazy evaluation strategy with the help of a lower bound technique to delay the LoS-Check until it is necessary. The lower bound technique only accepts the states which could provide a better solution, and it is commonly applied in the sample rejection and candidate path selection. Actually, LaLo-Check can be considered as a general path optimization framework, which could be used in most tree-structure motion planners. We choose three representative sampling-based motion planning algorithms, RRT*, Informed RRT*, and BIT*, to evaluate the performance of LaLo-Check. The experimental results show that the new planners which employ the LaLo-Check could find better solutions than the original algorithms within the equivalent time.

Motion Planning and Reactive Control Algorithms for Object Manipulation in Uncertain Conditions

This work proposes the application of several smart strategies for object manipulation tasks. A real-time flexible motion planning method was developed to be adapted to typical in-store logistics scenarios. The solution combines and optimizes some state-of-the-art techniques to solve object recognition and localization problems with a new hybrid pipeline. The algorithm guarantees good robustness and accuracy for object detection through depth images. A standard planner plans collision-free trajectories throughout the whole task while a proposed reactive motion control is active. Distributed proximity sensors were adopted to locally modify the planned trajectory when unexpected or misplaced obstacles intervene in the scene. To implement a robust grasping phase, a novel slipping control algorithm was used. It dynamically computes the grasp force by adapting it to the actual object physical properties so as to prevent slipping. Experimental results carried out in a typical supermarket scenario demonstrate the effectiveness of the presented methods.

Real-time motion planning of multiple nanowires in fluid suspension under electric-field actuation

The ability to precisely and efficiently manipulate nano- and micro-scale objects is a key step in enabling applications in areas such as nano-medicine and nano/micro-device assembly. In this paper, we propose and demonstrate real-time motion-planning algorithms for effectively steering multiple nanowires simultaneously in fluid suspension using electric fields. The proposed motion planners $$\mathtt{{SRRT}}^*$$ and learning- $$\mathtt{{SRRT}}^*$$ ( $$\mathtt{{LSRRT}}^*$$ ) are built on and extended by sparse-structure, Rapidly-exploring Random Tree (RRT)-based motion-planning algorithms. The algorithms also use heuristics to effectively guide the search process to quickly generate initial solutions. In order to reduce the online computational cost, the $$\mathtt{{LSRRT}}^*$$ algorithm shifts the intensive computation of optimal additive cost metric to offline training using a supervised learning method. Compared with the previously developed network-flow and RRT-based algorithms, the $$\mathtt{{SRRT}}^*$$ and $$\mathtt{{LSRRT}}^*$$ guarantee the asymptotically near-optimal trajectories of multiple nanowires. Moreover, the algorithms do not restrict in theory the maximum number of nanowires that can be simultaneously controlled. We present simulation and experimental results to demonstrate the minimum-time performance of the motion-planning and control design. The results demonstrate that the proposed algorithms significantly reduce the computational cost and increase the efficiency of manipulating multiple nanowires simultaneously when compared with the $${\mathtt{RRT}}^*$$ and other $${\mathtt{RRT}}^*$$ enhanced algorithms.

A G3-Continuous Extend Procedure for Path Planning of Mobile Robots with Limited Motion Curvature and State Constraints

Provably correct and computationally efficient path planning in the presence of various constraints is essential for autonomous driving and agile maneuvering of mobile robots. In this paper, we consider the planning of G 3-continuous planar paths with continuous and limited curvature in a motion environment that is bounded and contains obstacles modeled by a set of (non-convex) polygons. In practice, the curvature constraints often arise from mechanical limitations for the robot, such as limited steering and articulation angles in wheeled robots, or aerodynamic constraints in unmanned aerial vehicles. To solve the planning problem under those stringent constraints, we improve upon known path primitives, such as Reeds–Shepp (RS) and CC-steer (curvature-continuous) paths. Given the initial and final robot configuration, we developed extend-procedure computing paths that can approximate RS paths with arbitrary precision, but guaranteeing G 3-continuity. We show that satisfaction of all stated path constraints is guaranteed and, contrary to many other methods known from the literature, the method of checking for collisions between the planned path and obstacles is given by a closed-form analytic expression. Furthermore, we demonstrate that our approach is not conservative, i.e., it allows for precise maneuvers in tight environments under the assumption of a rectangular robot footprint. The presented extend procedure can be integrated into various motion-planning algorithms available in the literature. In particular, we utilized the Rapidly exploring Random Trees (RRT*) algorithm in conjunction with our extend procedure to demonstrate its feasibility in motion environments of nontrivial complexity and low computational cost in comparison to a G 3-continuous extend procedure based on η 3-splines.

A Motion-Planning System for a Domestic Service Robot

Service robots are intended to help humans in non-industrial environments such as houses or offices. To accomplish their goal, service robots must have several skills such as object recognition and manipulation, face detection and recognition, speech recognition and synthesis, task planning and, one of the most important, navigation in dynamic environments. This paper describes a fully implemented motion-planning system which comprehends from motion and path planning algorithms to spatial representation and behavior-based active navigation. The proposed system is implemented in Justina, a domestic service robot whose design is based on the ViRBot, an architecture to operate virtual and real robots that encompasses serveral layers of abstraction, from low-level control to symbolic planning. We evaluated our proposal both in simulated and real environments and compared it to classical implementations. For the tests, we used maps obtained from real environments (the Biorobotics Laboratory and the Robocup@Home arena) and maps generated from obstacles with random positions and shapes. Several parameters were used for comparison: the total traveled distance, the number of collisions, the number of reached goal points and the average execution speed. Our proposal performed significantly better both in real and simulated tests. Finally, we show our results in the context of the RoboCup@Home competition, where the system was successfully tested.

Improved Performance of Asymptotically Optimal Rapidly Exploring Random Trees

Three algorithms that improve the performance of the asymptotically optimal Rapidly exploring Random Tree (RRT*) are presented in this paper. First, we introduce the Goal Tree (GT) algorithm for motion planning in dynamic environments where unexpected obstacles appear sporadically. The GT reuses the previous RRT* by pruning the affected area and then extending the tree by drawing samples from a shadow set. The shadow is the subset of the free configuration space containing all configurations that have geodesics ending at the goal and are in conflict with the new obstacle. Smaller, well defined, sampling regions are considered for Euclidean metric spaces and Dubins' vehicles. Next, the Focused-Refinement (FR) algorithm, which samples with some probability around the first path found by an RRT*, is defined. The third improvement is the Grandparent-Connection (GP) algorithm, which attempts to connect an added vertex directly to its grandparent vertex instead of parent. The GT and GP algorithms are both proven to be asymptotically optimal. Finally, the three algorithms are simulated and compared for a Euclidean metric robot, a Dubins' vehicle, and a seven degrees-of-freedom manipulator.

The Provable Virtue of Laziness in Motion Planning

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along candidate shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.

Control of a manipulator robot by neuro-fuzzy subsets form approach control optimized by the genetic algorithms

In this paper, we describe a new form of neuro-fuzzy-genetic controller design for nonlinear system derived from a manipulator robot. The proposed method combines fuzzy logic and neuronal networks which are of growing interest in robotics, the neuro-fuzzy controller does not require the knowledge of the robot parameters values. Furtheremore, the genetic algorithms (GAs) for complex motion planning of robots require an evaluation function which takes into account multiple factors. An optimizing algorithm based on the genetic algorithms is applied in order to provide the most adequate shape of the fuzzy subsets that are considered as an interpolation functions. The proposed approach provides a well learning of the manipulator robot dynamics whatever the assigned task. Simulation and practical results illustrate the effectiveness of the proposed strategy. The advantages of the proposed method and the possibilities of further improvements are discussed.

Making Bertha Cooperate–Team AnnieWAY’s Entry to the 2016 Grand Cooperative Driving Challenge

This paper presents the concepts and methods utilized by Team AnnieWAY for the 2016 Grand Cooperative Driving Challenge. The paper introduces the automated vehicle BerthaOne . The vehicle, even though being based on the Bertha platform, distinguishes itself from its siblings by its software modules and algorithms. We, therefore, describe its system architecture and algorithms for perception, cooperation and motion planning. In Particular, we present a motion planner that plans different maneuvers flexibly by augmenting the cost function with situation specific cost terms. We subsequently describe the requirements of the 2016 GCDC and evaluate our performance during the competition.