Motion Planning Algorithms Research Articles

Vector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape

This paper presents an algorithm for local motion planning in environments populated by moving elliptical obstacles whose velocity, shape and size are fully known but may change with time. We base the algorithm on a collision avoidance vector field (CAVF) that aims to steer an agent to a desired final state whose motion is described by a double integrator kinematic model. In addition to handling multiple obstacles, the method is applicable in bounded environments for more realistic applications (e.g., motion planning inside a building). We also incorporate a method to deal with agents whose control input is limited so that they safely navigate around the obstacles. To showcase our approach, extensive simulations results are presented in 2D and 3D scenarios.

Extended Reality and Geospatial Mapping Technologies, Behavioral Predictive and Mobile Location Analytics, and Motion Planning and Object Recognition Algorithms in Immersive Hyper-Connected Virtual Spaces

Extended Reality and Geospatial Mapping Technologies, Behavioral Predictive and Mobile Location Analytics, and Motion Planning and Object Recognition Algorithms in Immersive Hyper-Connected Virtual Spaces

Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and Object Recognition Algorithms in the Retail Metaverse

Visual and Spatial Analytics, Immersive Virtual Simulation Technologies, and Motion Planning and Object Recognition Algorithms in the Retail Metaverse

Nonlinear Model Predictive Control for Optimal Motion Planning in Autonomous Race Cars

Motorsport has historically driven automobile innovation by challenging the world's best car manufacturers to design, and develop vehicles that push limits of contemporary technology, and compete at physical vehicle limits. Autonomous driving is a rapidly evolving field that garners interest in industry, government, and research due to substantial improvements in road safety, and traffic flow. Autonomous racing is a byproduct of advancements in autonomous driving, and guarantees innovation in the field through the design of state-of-the-art perception, motion planning, and control algorithms developed to perform in fast-paced, multi-object environments at high speeds, operating at a vehicle's acceleration, and tire limits. We propose a high-level Nonlinear Model Predictive Control (NMPC) strategy incorporating a Pacejka tire model, and nonlinear vehicle dynamics in the global coordinate system with constraints based on track boundaries, and vehicle input limits for optimal motion planning to minimize lap time. The NMPC motion planner is evaluated in three real-world racetracks, Circuit of the Americas, Circuit de Spa-Francorchamps, and Autodromo Nazionale Monza for three race car classes, Formula 1 (F1), Le Mans Prototype (LMP1), and Grand Touring Endurance (GTE). The proposed NMPC strategy is shown to generate time-optimal trajectories for each vehicle class in the evaluated tracks, conforming to optimal racing lines demonstrated by professional racing drivers.

Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems

Motion Planning and Object Recognition Algorithms, Vehicle Navigation and Collision Avoidance Technologies, and Geospatial Data Visualization in Network Connectivity Systems

MP-RRT#: a Model Predictive Sampling-based Motion Planning Algorithm for Unmanned Aircraft Systems

This paper introduces a kinodynamic motion planning algorithm for Unmanned Aircraft Systems (UAS), called MP-RRT#. MP-RRT# joins the potentialities of RRT# with a strategy based on Model Predictive Control to efficiently solve motion planning problems under differential constraints. Similar to other RRT-based algorithms, MP-RRT# explores the map constructing an asymptotically optimal graph. In each iteration the graph is extended with a new vertex in the reference state of the UAS. Then, a forward simulation is performed using a Model Predictive Control strategy to evaluate the motion between two adjacent vertices, and a trajectory in the state space is computed. As a result, the MP-RRT# algorithm eventually generates a feasible trajectory for the UAS satisfying dynamic constraints. Simulation results obtained with a simulated drone controlled with the PX4 autopilot corroborate the validity of the MP-RRT# approach.

Sbp-env: A Python Package for Sampling-based Motion Planner and Samplers

Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quickly test different sampling-based algorithms for motion planning. sbp-env focuses on the flexibility of tinkering with different aspects of the framework, and had divided the main planning components into two categories (i) samplers and (ii) planners. The focus of motion planning research had been mainly on (i) improving the sampling efficiency (with methods such as heuristic or learned distribution) and (ii) the algorithmic aspect of the planner using different routines to build a connected graph. Therefore, by separating the two components one can quickly swap out different components to test novel ideas.

A Decentralized B&B Algorithm for Motion Planning of Robot Swarms With Temporal Logic Specifications

In this letter, we study the problem of decentralized motion planning of robot swarms under high-level temporal logic specifications with a top-down approach. We use Swarm Signal Temporal Logic (SwarmSTL) to express swarm-level specifications. By encoding SwarmSTL formulas as mixed binary-integer constraints on the swarm features, the motion planning problem is formulated as a mixed-integer quadratic programming (MIQP) problem. We develop a decentralized Branch and Bound (B&B) algorithm with a node decentralization scheme such that the nodes in the B&B tree can be processed in parallel with communication among the agents and the agents can achieve consensus on the solution. Also, several search strategies to accelerate the decentralized B&B algorithm are proposed, and the performance improvements are presented. We evaluate the proposed algorithm using a supply transportation example with different formulas.

Representation, learning, and planning algorithms for geometric task and motion planning

We present a framework for learning to guide geometric task-and-motion planning (G-TAMP). G-TAMP is a subclass of task-and-motion planning in which the goal is to move multiple objects to target regions among movable obstacles. A standard graph search algorithm is not directly applicable, because G-TAMP problems involve hybrid search spaces and expensive action feasibility checks. To handle this, we introduce a novel planner that extends basic heuristic search with random sampling and a heuristic function that prioritizes feasibility checking on promising state–action pairs. The main drawback of such pure planners is that they lack the ability to learn from planning experience to improve their efficiency. We propose two learning algorithms to address this. The first is an algorithm for learning a rank function that guides the discrete task-level search, and the second is an algorithm for learning a sampler that guides the continuous motion-level search. We propose design principles for designing data-efficient algorithms for learning from planning experience and representations for effective generalization. We evaluate our framework in challenging G-TAMP problems, and show that we can improve both planning and data efficiency.

Creating Better Collision-Free Trajectory for Robot Motion Planning by Linearly Constrained Quadratic Programming.

Many algorithms in probabilistic sampling-based motion planning have been proposed to create a path for a robot in an environment with obstacles. Due to the randomness of sampling, they can efficiently compute the collision-free paths made of segments lying in the configuration space with probabilistic completeness. However, this property also makes the trajectories have some unnecessary redundant or jerky motions, which need to be optimized. For most robotics applications, the trajectories should be short, smooth and keep away from obstacles. This paper proposes a new trajectory optimization technique which transforms a polygon collision-free path into a smooth path, and can deal with trajectories which contain various task constraints. The technique removes redundant motions by quadratic programming in the parameter space of trajectory, and converts collision avoidance conditions to linear constraints to ensure absolute safety of trajectories. Furthermore, the technique uses a projection operator to realize the optimization of trajectories which are subject to some hard kinematic constraints, like keeping a glass of water upright or coordinating operation with dual robots. The experimental results proved the feasibility and effectiveness of the proposed method, when it is compared with other trajectory optimization methods.

Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots

Planning smooth and energy-efficient paths for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics. Over the past decades, several sampling-based motion-planning algorithms, extend functions and post-smoothing algorithms have been introduced for such motion-planning systems. Choosing the best combination of components for an application is a tedious exercise, even for expert users. We therefore present Bench-MR, the first open-source motion-planning benchmarking framework designed for sampling-based motion planning for nonholonomic, wheeled mobile robots. Unlike related software suites, Bench-MR is an easy-to-use and comprehensive benchmarking framework that provides a large variety of sampling-based motion-planning algorithms, extend functions, collision checkers, post-smoothing algorithms and optimization criteria. It aids practitioners and researchers in designing, testing, and evaluating motion-planning systems, and comparing them against the state of the art on complex navigation scenarios through many performance metrics. Through several experiments, we demonstrate how Bench-MR can be used to gain extensive insights from the benchmarking results it generates.

Provably constant-time planning and replanning for real-time grasping objects off a conveyor belt

In warehouse and manufacturing environments, manipulation platforms are frequently deployed at conveyor belts to perform pick-and-place tasks. Because objects on the conveyor belts are moving, robots have limited time to pick them up. This brings the requirement for fast and reliable motion planners that could provide provable real-time planning guarantees, which the existing algorithms do not provide. In addition to the planning efficiency, the success of manipulation tasks relies heavily on the accuracy of the perception system which is often noisy, especially if the target objects are perceived from a distance. For fast-moving conveyor belts, the robot cannot wait for a perfect estimate before it starts executing its motion. In order to be able to reach the object in time, it must start moving early on (relying on the initial noisy estimates) and adjust its motion on-the-fly in response to the pose updates from perception. We propose a planning framework that meets these requirements by providing provable constant-time planning and replanning guarantees. To this end, we first introduce and formalize a new class of algorithms called constant-time motion planning (CTMP) algorithms that guarantee to plan in constant time and within a user-defined time bound. We then present our planning framework for grasping objects off a conveyor belt as an instance of the CTMP class of algorithms. We present it, provide its analytical properties, and perform an experimental analysis both in simulation and on a real robot.

How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control.

Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach.

Hierarchical deep reinforcement learning to drag heavy objects by adult-sized humanoid robot

Most research on robot manipulation focuses on objects that are light enough for the robot to pick them up. However, in our daily life, some objects are too big or too heavy to be picked up or carried, so that dragging them is necessary. Although bipedal humanoid robots have nowadays good mobility on level ground, dragging unfamiliar objects including large and heavy objects on various surfaces is an interesting research area with many applications, which will provide insights into human manipulation and will encourage the development of novel algorithms for robot motion planning and control. This is a challenging problem, not only because of the unknown and potentially variable friction of the foot, but also because the feet of the robot may slip during unbalanced poses. In this paper, we propose a novel hierarchical deep learning algorithm that learns how to drag heavy objects with an adult-sized humanoid robot for the first time. First, we present a Three-layered Convolution Volumetric Network (TCVN) for 3D object classification with point clouds volumetric occupancy grid integration. Second, we propose a lightweight real-time instance segmentation method named Tiny-YOLACT for the detection and classification of the floor surface. Third, we propose a deep Q-learning algorithm to learn the policy control of the Center of Mass of the robot (DQL-COM). The DQL-COM algorithm learning is bootstrapped using the ROS Gazebo simulator. After initial training, we complete training on the THORMANG-Wolf, a 1.4 m tall adult-sized humanoid robot with 27 degrees of freedom and weighing 48 kg, on three distinct types of surfaces. We evaluate the performance of our approach by dragging eight different types of objects (e.g., a small suitcase, a large suitcase, a chair). The extensive experiments (480 times on the real robot) included dragging a heavy object with a mass of 84.6 kg (two times greater than the robot’s weight) and showed remarkable success rates of 92.92% when combined with the force–torque sensors, and 83.75% without force–torque sensors.

A Generalized Voronoi Diagram-Based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots

The rapidly exploring random tree and its variants (RRTs) have been widely adopted as the motion planning algorithms for mobile robots. However, the trap space problem, such as mazes and S-shaped corridors, hinders their planning efficiency. In this article, we present a generalized Voronoi diagram (GVD)-based heuristic path planning algorithm to generate a heuristic path, guide the sampling process of RRTs, and further improve the motion planning efficiency of RRTs. Different from other heuristic algorithms that only work in certain environments or depend on specified parameter setting, the proposed algorithm can automatically identify the environment feature and provide a reasonable heuristic path. First, the given environment is initialized with a lightweight feature extraction from the GVD, which guarantees that any state in the free space can be connected to the feature graph without any collision. Second, to remove the redundancy of feature nodes, a feature matrix is proposed to represent connections among feature nodes and a corresponding feature node fusion technique is utilized to delete the redundant nodes. Third, based on the GVD feature matrix, a heuristic path planning algorithm is presented. This heuristic path is then used to guide the sampling process of RRTs and achieve real-time motion planning. The proposed GVD feature matrix can be also utilized to improve the efficiency of the replanning. Through a series of simulation studies and real-world implementations, it is confirmed that the proposed algorithm achieves better performance in heuristic path planning, feature extraction of free space, and real-time motion planning.

A Decision Algorithm for Motion Planning of Car-Like Robots in Dynamic Environments

Motion planning in dynamic environments is essential for many applications such as in search and rescue missions and in servicing tasks. In this paper, I present a new approach for motion planning for an autonomous mobile robot which is requested to operate in a dynamic environment. The robot’s working environment is cluttered with static obstacles with a priori knowledge of their shapes and positions and with moving obstacles with unknown geometry and motion. In order to ensure a safe motion for the robot I propose a two-stage approach. First, using the Bump-Surface concept I construct a path by considering only the static obstacles of the environment. Then, the robot starts its motion on the given path. When the robot detects a potential danger situation (i.e., collision), a decision algorithm tries to evaluate the risk of collision and simultaneously to find the safest action for the robot. The proposed approach is evaluated in randomly generated simulated scenarios.

Direct Policy Optimization Using Deterministic Sampling and Collocation

We present an approach for approximately solving discrete-time stochastic optimal-control problems by combining direct trajectory optimization, deterministic sampling, and policy optimization. Our feedback motion-planning algorithm uses a quasi-Newton method to simultaneously optimize a reference trajectory, a set of deterministically chosen sample trajectories, and a parameterized policy. We demonstrate that this approach exactly recovers LQR policies in the case of linear dynamics, quadratic objective, and Gaussian disturbances. We also demonstrate the algorithm on several nonlinear, underactuated robotic systems to highlight its performance and ability to handle control limits, safely avoid obstacles, and generate robust plans in the presence of unmodeled dynamics.

An Integrated Localization, Motion Planning and Obstacle Avoidance Algorithm in Belief Space

As robots are being increasingly used in close proximity to humans and objects, it is imperative that robots operate safely and efficiently under real-world conditions. Yet, the environment is seldom known perfectly. Noisy sensors and actuation errors compound to the errors introduced while estimating features of the environment. We present a novel approach (1) to incorporate these uncertainties for robot state estimation and (2) to compute the probability of collision pertaining to the estimated robot configurations. The expression for collision probability is obtained as an infinite series and we prove its convergence. An upper bound for the truncation error is also derived and the number of terms required is demonstrated by analyzing the convergence for different robot and obstacle configurations. We evaluate our approach using two simulation domains which use a roadmap-based strategy to synthesize trajectories that satisfy collision probability bounds.

A survey on population-based meta-heuristic algorithms for motion planning of aircraft

• The elements in AMP model is sorted by aircraft, environment and task. • The general format of solving AMP problems by PMH algorithms is presented. • Modifications of PMH algorithms are concluded from a new perspective. • Suggestions are made when selecting the PMH algorithms for particular AMP problems. Meta-heuristic algorithms have turned out to be good methods to address optimization problems with complicated constraints, and those algorithms have been widely applied to many aircraft motion planning problems. An optimal flight path is crucial for the aircraft to complete the specific task safely and efficiently. The existing surveys lack a broad review on the population-based meta-heuristic algorithms focusing on the aircraft motion planning problem, which fails to summarize the latest progress in this field. Therefore, a comprehensive survey on this topic is carried out from new perspectives. The mathematical model, the population-based meta-heuristic algorithms and their modifications regarding the aircraft motion planning problem are all included. Discussion is also made based on the statistical data from the collected literatures. It is anticipated that this survey will provide the researchers with some suggestions on how to select appropriate population-based meta-heuristic algorithms for a particular aircraft motion planning problem.

Benchmarking framework for robotic arc welding motion planning

Producing a small series of large complex parts using robotic arc welding is challenging due to the time it takes to program the robot. Currently, offline programming software assists the operator in programming hundreds of welds. This is often still an iterative process because the welding robot or torch may be in collision during part of the weld motion. Adding motion planning software in this workflow could reduce the time required to generate a robot program. However, the capabilities of motion planning algorithms in this context are not clear. We present a generic framework to evaluate and compare motion planning algorithms for robot arc welding. This framework allows integration of existing open source and commercial planners on different levels of abstraction depending on their capabilities. Different planners can be combined and benchmarked as a group if a single planner is not sufficient to solve all aspects of the problem. The framework is applied to two realistic welding cases to show its potential.