Collision-free Motion Planning Research Articles

A local collision-free motion planning strategy for hyper-redundant manipulators based on dynamic safety envelopes

A local collision-free motion planning strategy for hyper-redundant manipulators based on dynamic safety envelopes

Communication-Efficient and Collision-Free Motion Planning of Underwater Vehicles via Integral Reinforcement Learning.

Motion planning of underwater vehicles is regarded as a promising technique to make up the flexibility deficiency of underwater sensor networks (USNs). Nonetheless, the unique characteristics of underwater channel and environment make it challenging to achieve the above mission. This article is concerned with a communication-efficient and collision-free motion planning issue for underwater vehicles in fading channel and obstacle environment. We first develop a model-based integral reinforcement learning (IRL) estimator to predict the stochastic signal-to-noise ratio (SNR). With the estimated SNR, an integrated optimization problem for the codesign of communication efficiency and motion planning is constructed, in which the underwater vehicle dynamics, communication capacity, collision avoidance, and position control are all considered. In order to tackle this problem, a model-free IRL algorithm is designed to drive underwater vehicles to the desired position points while maximizing the communication capacity and avoiding the collision. It is worth mentioning that, the proposed motion planning solution in this article considers a realistic underwater communication channel, as well as a realistic dynamic model for underwater vehicles. Finally, simulation and experimental results are demonstrated to verify the effectiveness of the proposed approach.

Certified polyhedral decompositions of collision-free configuration space

Understanding the geometry of collision-free configuration space (C-free) in the presence of Cartesian-space obstacles is an essential ingredient for collision-free motion planning. While it is possible to check for collisions at a point using standard algorithms, to date no practical method exists for computing C-free regions with rigorous certificates due to the complexity of mapping Cartesian-space obstacles through the kinematics. In this work, we present the first to our knowledge rigorous method for approximately decomposing a rational parametrization of C-free into certified polyhedral regions. Our method, called C-Iris (C-space Iterative Regional Inflation by Semidefinite programming), generates large, convex polytopes in a rational parameterization of the configuration space which are rigorously certified to be collision-free. Such regions have been shown to be useful for both optimization-based and randomized motion planning. Based on convex optimization, our method works in arbitrary dimensions, only makes assumptions about the convexity of the obstacles in the 3D Cartesian space, and is fast enough to scale to realistic problems in manipulation. We demonstrate our algorithm’s ability to fill a non-trivial amount of collision-free C-space in several 2-DOF examples where the C-space can be visualized, as well as the scalability of our algorithm on a 7-DOF KUKA iiwa, a 6-DOF UR3e, and 12-DOF bimanual manipulators. An implementation of our algorithm is open-sourced in Drake . We furthermore provide examples of our algorithm in interactive Python notebooks .

Solving the Real-Time Motion Planning Problem for Non-Holonomic Robots With Collision Avoidance in Dynamic Scenes

Collision-free motion planning allows robots to perform tasks in complex environments, but the conservative bounding curves during collision detection will reduce the robot's dexterity. This paper presents a novel algorithm for real-time motion planning of non-holonomic robots in dynamic scenes. To achieve this, the robot and the obstacles are generally decomposed into a series of superquadric objects. The expanded closed-form Minkowski sums are used to construct the velocity obstacle to deal with the updating orientation of the robot. The algorithm is extended to collision avoidance for different numbers and types of obstacles, which share the same workspace with the robot. We implement our algorithm in various simulations and experiments, where the robot has to avoid collisions with obstacles in real-time. The results demonstrate the effectiveness and efficiency of the proposed algorithm in collision-free motion planning at real-time computation rates and the enhanced dexterity control in complex environments.

Parametrized topological complexity of collision-free motion planning in the plane

Parametrized motion planning algorithms have high degrees of universality and flexibility, as they are designed to work under a variety of external conditions, which are viewed as parameters and form part of the input of the underlying motion planning problem. In this paper, we analyze the parametrized motion planning problem for the motion of many distinct points in the plane, moving without collision and avoiding multiple distinct obstacles with a priori unknown positions. This complements our prior work Cohen et al. [3] (SIAM J. Appl. Algebra Geom. 5, 229–249), where parametrized motion planning algorithms were introduced, and the obstacle-avoiding collision-free motion planning problem in three-dimensional space was fully investigated. The planar case requires different algebraic and topological tools than its spatial analog.

Comparison of different sample-based motion planning methods in redundant robotic manipulators

AbstractThe main objective of a motion planning algorithm is to find a collision-free path in the workspace of a robotic manipulator in a point-to-point motion. Among the various motion planning methods available, sample-based motion planning algorithms are easy to use, quick and powerful in redundant robotic systems applications. In this study, different sampling-based motion planning algorithms are employed to select the most appropriate method for efficient collision-free motion planning. As a case study, finding a collision-free robotic displacement for welding a main pipe with other intersecting pipes and joints is considered. The robotic manipulator employed in this study has seven degrees of freedom, where six degrees are related to the manipulator joints and one degree is related to its base linear movement suspended from ceiling. Five criteria, time, path length, path time, path smoothness and process time are used to evaluate the efficiency of different sample-based motion planning algorithms. Finally, a smaller set of more efficient algorithms are introduced based on the defined efficiency evaluation criteria.

Collision Free Motion Planning for Double Turret System Operating in a Common Workspace

Collision Free Motion Planning for Double Turret System Operating in a Common Workspace

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.

Collision-free motion planning for the litchi-picking robot

In unstructured environments, picking robots could collide with branches, thus reducing the success rate of picking. In this study, collision-free motion planning is performed in two steps based on the binocular stereo vision information of the environment. First, according to the 3D information collected in the stereo vision environment, the improved adaptive weight particle swarm optimization (APSO) algorithm is adopted to solve the inverse kinematics problem of robots and obtain the collision-free picking posture. Second, to address the limitations of the Bi-RRT algorithm in a high-dimensional environment, such as randomness and slow convergence speed, the target gravity concept and adaptive coefficient adjustment method are introduced to the Bi-RRT algorithm, which is called the AtBi-RRT algorithm. The AtBi-RRT algorithm is used to determine the collision-free path of the robot. Simulation results indicate that the APSO algorithm can swiftly obtain the proper collision-free picking posture, and the average path-determination time of the AtBi-RRT algorithm is 4.24 s. The success rate of path determination is 100% for the laboratory’s picking scene. Experimental results verify that the proposed collision-free motion-planning method allows the picking robot to avoid obstacles in the workspace and efficiently complete the picking task.

Collision Free Motion Planning on a Wedge of Circles

We exhibit an algorithm with continuous instructions for two robots moving without collisions on a track shaped as a wedge of three circles. We show that the topological complexity of the configuration space associated with this problem is 3. The topological complexity is a homotopy invariant that can be thought of as the minimum number of continuous instructions required to describe the movement of the robots between any initial configuration to any final one without collisions. The algorithm presented is optimal in the sense that it requires exactly 3 continuous instructions.

A Distributed Pipeline for Scalable, Deconflicted Formation Flying

Reliance on external localization infrastructure and centralized coordination are main limiting factors for formation flying of vehicles in large numbers and in unprepared environments. While solutions using onboard localization address the dependency on external infrastructure, the associated coordination strategies typically lack collision avoidance and scalability. To address these shortcomings, we present a unified pipeline with onboard localization and a distributed, collision-free motion planning strategy that scales to a large number of vehicles. Since distributed collision avoidance strategies are known to result in gridlock, we also present a decentralized task assignment solution to deconflict vehicles. We experimentally validate our pipeline in simulation and hardware. The results show that our approach for solving the optimization problem associated with motion planning gives solutions within seconds in cases where general purpose solvers fail due to high complexity. In addition, our lightweight assignment strategy leads to successful and quicker formation convergence in 96-100% of all trials, whereas indefinite gridlocks occur without it for 33-50% of trials. By enabling large-scale, deconflicted coordination, this pipeline should help pave the way for anytime, anywhere deployment of aerial swarms.

Recurrent Neural Networks-Based Collision-Free Motion Planning for Dual Manipulators Under Multiple Constraints

Dual robotic manipulators are robotic systems that are developed to imitate human arms, which shows great potential in performing complex tasks. Collision-free motion planning in real time is still a challenging problem for controlling a dual robotic manipulator because of the overlap workspace. In this paper, a novel planning strategy under physical constraints of dual manipulators using dynamic neural networks is proposed, which can satisfy the collision avoidance and trajectory tracking. Particularly, the problem of collision avoidance is first formulated into a set of inequality formulas, whereas the robotic trajectory is then transformed into an equality constraint by introducing negative feedback in outer loop. The planning problem subsequently becomes a Quadratic Programming (QP) problem by considering the redundancy, the boundaries of joint angles and velocities of the system. The QP is solved using a convergent provable recurrent neural network that without calculating the pseudo-inversion of the Jacobian. Consequently, numerical experiments on 8-DoF modular robot and 14-DoF Baxter robot are conducted to show the superiority of the proposed strategy.

Hardware-efficient interval analysis based collision detection and avoidance for mobile robots

Collision detection and avoidance is challenging when the mobile robot is moving among multiple dynamic obstacles. A hardware-efficient architecture supporting parallel implementation is presented in this work for low-power, faster and reliable collision-free motion planning. An approach based on interval analysis is developed for designing an efficient hardware architecture. The proposed architecture achieves parallelism which can be combined with any robotic task involving multiple obstacles. Interval arithmetic is used for representing the pose of the robot and the obstacle as velocity intervals in a fixed time period. These intervals correspond to sub-intervals such as arcs and line-segments. In particular, the collision detection problem for dynamic objects involves the computation of line segment-arc intersections and segment-segment intersections. The intersection of these boundary curves is carried out in a hardware-efficient manner so that it avoids complex arithmetic computations such as multiplication, division etc and exploits parallelism. We develop several results on intersection of these sub-intervals for collision detection and use them to obtain a hardware-efficient collision detection algorithm that requires only shift and add-type of computations. The algorithm is further used in developing a hardware-efficient technique for finding an exhaustive set of solutions for avoiding collision of the robot with dynamic obstacles. Simulation results in MatLab and experiments with a Field Programmable Gate Array (FPGA)-based robot show that a variety of collision avoidance techniques can be implemented using the proposed solution set that guarantees collision avoidance with multiple obstacles.

Whole-body motion and footstep planning for humanoid robots with multi-heuristic search

In this paper, we present a motion planning framework for humanoid robots that combines whole-body motions as well as footsteps under a quasi-static flat ground plane assumption. Traditionally, these two have been treated as separate research domains. One of the major challenges behind whole-body motion planning is the high DoF (Degrees of Freedom) nature of the problem, in addition to strict constraints on obstacle avoidance and stability. On the other hand footstep planning on its own is a comparatively simpler problem due to the low DoF search space, but coalescing it into a larger framework that includes whole-body motion planning adds further complexity in reaching a solution within a suitable time frame that satisfies all the constraints. In this work, we treat motion planning as a graph search problem, and employ Shared Multi-heuristic A* (SMHA*) to generate efficient, stable and collision-free motion plans given only the starting state of the robot and the desired end-effector pose.

Topology-guided path integral approach for stochastic optimal control in cluttered environment

This paper addresses planning and control of robot motion under uncertainty that is formulated as a continuous-time, continuous-space stochastic optimal control problem, by developing a topology-guided path integral control method. The path integral control framework, which forms the backbone of the proposed method, re-writes the Hamilton–Jacobi–Bellman equation as a statistical inference problem; the resulting inference problem is solved by a sampling procedure that computes the distribution of controlled trajectories around the trajectory by the passive dynamics. For motion control of robots in a highly cluttered environment, however, this sampling can easily be trapped in a local minimum unless the sample size is very large, since the global optimality of local minima depends on the degree of uncertainty. Thus, a homology-embedded sampling-based planner that identifies many (potentially) local-minimum trajectories in different homology classes is developed to aid the sampling process. In combination with a receding-horizon fashion of the optimal control the proposed method produces a dynamically feasible and collision-free motion plans without being trapped in a local minimum. Numerical examples on a synthetic toy problem and on quadrotor control in a complex obstacle field demonstrate the validity of the proposed method.

Adaptive Obstacle Detection for Mobile Robots in Urban Environments Using Downward-Looking 2D LiDAR.

Environment perception is important for collision-free motion planning of outdoor mobile robots. This paper presents an adaptive obstacle detection method for outdoor mobile robots using a single downward-looking LiDAR sensor. The method begins by extracting line segments from the raw sensor data, and then estimates the height and the vector of the scanned road surface at each moment. Subsequently, the segments are divided into either road ground or obstacles based on the average height of each line segment and the deviation between the line segment and the road vector estimated from the previous measurements. A series of experiments have been conducted in several scenarios, including normal scenes and complex scenes. The experimental results show that the proposed approach can accurately detect obstacles on roads and could effectively deal with the different heights of obstacles in urban road environments.

Closed-Loop Global Motion Planning for Reactive, Collision-Free Execution of Learned Tasks

We present a robot motion planning approach for performing a learned task while reacting to the movement of obstacles and task-relevant objects. We employ a closed-loop, sampling-based motion planner operating multiple times a second that senses obstacles and task-relevant objects and generates collision-free motion plans based on a learned-task model. The task model is learned from expert demonstrations prior to task execution and is represented as a hidden Markov model. During task execution, our motion planner quickly searches in the Cartesian product of the task model and a probabilistic roadmap for a collision-free plan with features most similar to the demonstrations given the current locations of the task-relevant objects. We accelerate replanning using a fast bidirectional search and by biasing the sampling distribution using information from the learned-task model. We show the efficacy of our approach with the Baxter robot performing two tasks.

Potential-field constructions in an MPC framework: application for safe navigation in a variable coastal environment

Abstract This work presents an NMPC (Nonlinear Model Predictive Control)-based algorithm for safe navigation of multi-agent dynamical systems in a variable environment with fixed or moving obstacles. The contribution lies in the construction of repulsive potential fields for the fixed and moving obstacles which are introduced in a predictive control optimization problem to penalize collision. The constraints are activated only in the view range of the agent using on-off barrier functions. Collision-free motion planning of ships in Trondheim fjord harbor serves as a benchmark for testing our algorithm. Some scenarios are simulated in order to prove the efficiency of the algorithm and its feasibility to run in real-time.

Human-robot collaboration while sharing production activities in dynamic environment: SPADER system

Interactive robot doing collaborative work in hybrid work cell need adaptive trajectory planning strategy. Indeed, systems must be able to generate their own trajectories without colliding with dynamic obstacles like humans and assembly components moving inside the robot workspace. The aim of this paper is to improve collision-free motion planning in dynamic environment in order to insure human safety during collaborative tasks such as sharing production activities between human and robot. Our system proposes a trajectory generating method for an industrial manipulator in a shared workspace. A neural network using a supervised learning is applied to create the waypoints required for dynamic obstacles avoidance. These points are linked with a quintic polynomial function for smooth motion which is optimized using least-square to compute an optimal trajectory. Moreover, the evaluation of human motion forms has been taken into consideration in the proposed strategy. According to the results, the proposed approach is an effective solution for trajectories generation in a dynamic environment like a hybrid workspace.

Goal-directed robot manipulation through axiomatic scene estimation

Performing robust goal-directed manipulation tasks remains a crucial challenge for autonomous robots. In an ideal case, shared autonomous control of manipulators would allow human users to specify their intent as a goal state and have the robot reason over the actions and motions to achieve this goal. However, realizing this goal remains elusive due to the problem of perceiving the robot’s environment. We address and describe the problem of axiomatic scene estimation for robot manipulation in cluttered scenes which is the estimation of a tree-structured scene graph describing the configuration of objects observed from robot sensing. We propose generative approaches to scene inference (as the axiomatic particle filter, and the axiomatic scene estimation by Markov chain Monte Carlo based sampler) of the robot’s environment as a scene graph. The result from AxScEs estimation are axioms amenable to goal-directed manipulation through symbolic inference for task planning and collision-free motion planning and execution. We demonstrate the results for goal-directed manipulation of multi-object scenes by a PR2 robot.