Category Archives: Robot Motion Planning

Path planning by merging random sampling (RRT) with informed heuristics (A*)

September 14, 2021 12:44 ,

Jonathan D Gammell, Timothy D Barfoot, Siddhartha S Srinivasa, Batch Informed Trees (BIT*): Informed asymptotically optimal anytime search, The International Journal of Robotics Research. 2020;39(5):543-567, DOI: 10.1177/0278364919890396.

Path planning in robotics often requires finding high-quality solutions to continuously valued and/or high-dimensional problems. These problems are challenging and most planning algorithms instead solve simplified approximations. Popular approximations include graphs and random samples, as used by informed graph-based searches and anytime sampling-based planners, respectively.

Informed graph-based searches, such as A*, traditionally use heuristics to search a priori graphs in order of potential solution quality. This makes their search efficient, but leaves their performance dependent on the chosen approximation. If the resolution of the chosen approximation is too low, then they may not find a (suitable) solution, but if it is too high, then they may take a prohibitively long time to do so.

Anytime sampling-based planners, such as RRT*, traditionally use random sampling to approximate the problem domain incrementally. This allows them to increase resolution until a suitable solution is found, but makes their search dependent on the order of approximation. Arbitrary sequences of random samples approximate the problem domain in every direction simultaneously, but may be prohibitively inefficient at containing a solution.

This article unifies and extends these two approaches to develop Batch Informed Trees (BIT*), an informed, anytime sampling-based planner. BIT* solves continuous path planning problems efficiently by using sampling and heuristics to alternately approximate and search the problem domain. Its search is ordered by potential solution quality, as in A*, and its approximation improves indefinitely with additional computational time, as in RRT*. It is shown analytically to be almost-surely asymptotically optimal and experimentally to outperform existing sampling-based planners, especially on high-dimensional planning problems.

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Modelling robot motion sequences through context-free grammars

October 20, 2020 19:38 ,

Rudolf Lioutikov, Guilherme Maeda, Filipe Veiga, Kristian Kersting, Jan Peters, Learning attribute grammars for movement primitive sequencing, The International Journal of Robotics Research, Vol 39, Issue 1, 2020, DOI: 10.1177/0278364919868279.

Movement primitives are a well studied and widely applied concept in modern robotics. However, composing primitives out of an existing library has shown to be a challenging problem. We propose the use of probabilistic context-free grammars to sequence a series of primitives to generate complex robot policies from a given library of primitives. The rule-based nature of formal grammars allows an intuitive encoding of hierarchically structured tasks. This hierarchical concept strongly connects with the way robot policies can be learned, organized, and re-used. However, the induction of context-free grammars has proven to be a complicated and yet unsolved challenge. We exploit the physical nature of robot movement primitives to restrict and efficiently search the grammar space. The grammar is learned by applying a Markov chain Monte Carlo optimization over the posteriors of the grammars given the observations. The proposal distribution is defined as a mixture over the probabilities of the operators connecting the search space. Moreover, we present an approach for the categorization of probabilistic movement primitives and discuss how the connectibility of two primitives can be determined. These characteristics in combination with restrictions to the operators guarantee continuous sequences while reducing the grammar space. In addition, a set of attributes and conditions is introduced that augments probabilistic context-free grammars in order to solve primitive sequencing tasks with the capability to adapt single primitives within the sequence. The method was validated on tasks that require the generation of complex sequences consisting of simple movement primitives using a seven-degree-of-freedom lightweight robotic arm.

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A comparison / evaluation of bug algorithms for mobile robots and their bad performance when relying in only one sensor

October 10, 2019 15:16 ,

K.N. McGuire, G.C.H.E. de Croon, K. Tuyls, A comparative study of bug algorithms for robot navigation,. Robotics and Autonomous Systems, Volume 121, DOI: 10.1016/j.robot.2019.103261.

This paper presents a literature survey and a comparative study of Bug Algorithms, with the goal of investigating their potential for robotic navigation. At first sight, these methods seem to provide an efficient navigation paradigm, ideal for implementations on tiny robots with limited resources. Closer inspection, however, shows that many of these Bug Algorithms assume perfect global position estimate of the robot which in GPS-denied environments implies considerable expenses of computation and memory — relying on accurate Simultaneous Localization And Mapping (SLAM) or Visual Odometry (VO) methods. We compare a selection of Bug Algorithms in a simulated robot and environment where they endure different types noise and failure-cases of their on-board sensors. From the simulation results, we conclude that the implemented Bug Algorithms’ performances are sensitive to many types of sensor-noise, which was most noticeable for odometry-drift. This raises the question if Bug Algorithms are suitable for real-world, on-board, robotic navigation as is. Variations that use multiple sensors to keep track of their progress towards the goal, were more adept in completing their task in the presence of sensor-failures. This shows that Bug Algorithms must spread their risk, by relying on the readings of multiple sensors, to be suitable for real-world deployment.

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Analyzing effects of loads and terrain on wheel shapes in order to reduce errors in position estimation of a mobile wheeled robot

October 10, 2019 15:09 ,

Smieszek, M., Dobrzanska, M. & Dobrzanski, P. , The impact of load on the wheel rolling radius and slip in a small mobile platform. Auton Robot (2019) 43: 2095, DOI: 10.1007/s10514-019-09857-0.

Automated guided vehicles are used in a variety of applications. Their major purpose is to replace humans in onerous, monotonous and sometimes dangerous operations. Such vehicles are controlled and navigated by application-specific software. In the case of vehicles used in multiple environments and operating conditions, such as the vehicles which are the subject of this study, a reasonable approach is required when selecting the navigation system. The vehicle may travel around an enclosed hall and around an open yard. The pavement surface may be smooth or uneven. Vehicle wheels should be flexible and facilitate the isolation and absorption of vibrations in order to reduce the effect of surface unevenness to the load. Another important factor affecting the operating conditions are changes to vehicle load resulting from the distribution of the load and the weight carried. Considering all of the factors previously mentioned, the vehicle’s navigation and control system is required to meet two opposing criteria. One of them is low price and simplicity, the other is ensuring the required accuracy when following the preset route. In the course of this study, a methodology was developed and tested which aims to obtain a satisfactory compromise between those two conflicting criteria. During the study a vehicle made in Technical University of Rzeszow was used. The results of the experimental research have been analysed. The results of the analysis provided a foundation for the development of a methodology leading to a reduction in navigation errors. Movement simulations for the proposed vehicle system demonstrated the potential for a significant reduction in the number of positioning errors.

Posted in: Mobile Robot Localization, Robot motion planning

Hardware efficient collision avoidance for mobile robots through the use of interval arithmetics and parallelism

September 16, 2019 16:08 ,

Pranjal Vyas, Leena Vachhani, K Sridharan, Hardware-efficient interval analysis based collision detection and avoidance for mobile robots. Mechatronics, Volume 62, 2019, DOI: 10.1016/j.mechatronics.2019.102258.

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.

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A related work with a nice taxonomy of robot navigation algorithms

June 17, 2019 12:18 ,

Eduardo J. Molinos, Ángel Llamazares, Manuel Ocaña Dynamic window based approaches for avoiding obstacles in moving, Robotics and Autonomous Systems,
Volume 118, 2019, Pages 112-130 DOI: 10.1016/j.robot.2019.05.003.

In recent years, Unmanned Ground Vehicles (UGVs) have been widely used as service robots. Unlike industrial robots, which are situated in fixed and controlled positions, UGVs work in dynamic environments, sharing the environment with other vehicles and humans. These robots should be able to move without colliding with any obstacle, assuring its integrity and the environment safety. In this paper, we propose two adaptations of the classical Dynamic Window algorithm for dealing with dynamic obstacles like Dynamic Window for Dynamic Obstacles (DW4DO) and Dynamic Window for Dynamic Obstacles Tree (DW4DOT). These new algorithms are compared with our previous algorithms based on Curvature Velocity Methods: Predicted Curvature Velocity Method (PCVM) and Dynamic Curvature Velocity Method (DCVM). Proposals have been validated in both simulated and real environment using several robotic platforms.

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A novel path planning method for both global and local planning with provable behavior, and a nice survey of existing navigation methods

May 24, 2019 11:06 ,

Sgorbissa, A., Integrated robot planning, path following, and obstacle avoidance in two and three dimensions: wheeled robots, underwater vehicles, and multicopters, The International Journal of Robotics Research, DOI: 10.1177/0278364919846910.

We propose an innovative, integrated solution to path planning, path following, and obstacle avoidance that is suitable both for 2D and 3D navigation. The proposed method takes as input a generic curve connecting a start and a goal position, and is able to find a corresponding path from start to goal in a maze-like environment even in the absence of global information, it guarantees convergence to the path with kinematic control, and finally avoids locally sensed obstacles without becoming trapped in deadlocks. This is achieved by computing a closed-form expression in which the control variables are a continuous function of the input curve, the robot’s state, and the distance of all the locally sensed obstacles. Specifically, we introduce a novel formalism for describing the path in two and three dimensions, as well as a computationally efficient method for path deformation (based only on local sensor readings) that is able to find a path to the goal even when such path cannot be produced through continuous deformations of the original. The article provides formal proofs of all the properties above, as well as simulated results in a simulated environment with a wheeled robot, an underwater vehicle, and a multicopter.

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Improving on-line Monte Carlo POMDP (DESTOP in particular) in discrete spaces through the use of importance sampling, and a nice summary of the problem and of current on-line POMDP approaches

March 6, 2019 16:36 ,

Luo, Y., Bai, H., Hsu, D., & Lee, W. S., Importance sampling for online planning under uncertainty, The International Journal of Robotics Research, 38(2–3), 162–181, 2019 DOI: 10.1177/0278364918780322.

The partially observable Markov decision process (POMDP) provides a principled general framework for robot planning under uncertainty. Leveraging the idea of Monte Carlo sampling, recent POMDP planning algorithms have scaled up to various challenging robotic tasks, including, real-time online planning for autonomous vehicles. To further improve online planning performance, this paper presents IS-DESPOT, which introduces importance sampling to DESPOT, a state-of-the-art sampling-based POMDP algorithm for planning under uncertainty. Importance sampling improves DESPOT’s performance when there are critical, but rare events, which are difficult to sample. We prove that IS-DESPOT retains the theoretical guarantee of DESPOT. We demonstrate empirically that importance sampling significantly improves the performance of online POMDP planning for suitable tasks. We also present a general method for learning the importance sampling distribution.

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RL and Inverse RL based on MDPs for autonomous vehicles, and a nice historical review of the topic of a.v.

March 4, 2019 10:25 ,

Changxi You, Jianbo Lu, Dimitar Filev, Panagiotis Tsiotras, Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning, Robotics and Autonomous Systems, Volume 114, 2019, Pages 1-18 DOI: 10.1016/j.robot.2019.01.003.

Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel efficiency and reduce congestion. They represent the main trend in future intelligent transportation systems. This paper concentrates on the planning problem of autonomous vehicles in traffic. We model the interaction between the autonomous vehicle and the environment as a stochastic Markov decision process (MDP) and consider the driving style of an expert driver as the target to be learned. The road geometry is taken into consideration in the MDP model in order to incorporate more diverse driving styles. The desired, expert-like driving behavior of the autonomous vehicle is obtained as follows: First, we design the reward function of the corresponding MDP and determine the optimal driving strategy for the autonomous vehicle using reinforcement learning techniques. Second, we collect a number of demonstrations from an expert driver and learn the optimal driving strategy based on data using inverse reinforcement learning. The unknown reward function of the expert driver is approximated using a deep neural-network (DNN). We clarify and validate the application of the maximum entropy principle (MEP) to learn the DNN reward function, and provide the necessary derivations for using the maximum entropy principle to learn a parameterized feature (reward) function. Simulated results demonstrate the desired driving behaviors of an autonomous vehicle using both the reinforcement learning and inverse reinforcement learning techniques.

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An application of MDPs to UAV collision-free navigation with an interesting taxonomy of the state-of-the-art

February 5, 2019 10:24 ,

Xiang Yu1, Xiaobin Zhou2, Youmin Zhang, Collision-Free Trajectory Generation and Tracking for UAVs Using Markov Decision Process in a Cluttered Environment, Journal of Intelligent & Robotic Systems, 2019, 93:17–32 DOI: 10.1007/s10846-018-0802-z.

A collision-free trajectory generation and tracking method capable of re-planning unmanned aerial vehicle (UAV) trajectories can increase flight safety and decrease the possibility of mission failures. In this paper, a Markov decision process (MDP) based algorithm combined with backtracking method is presented to create a safe trajectory in the case of hostile environments. Subsequently, a differential flatness method is adopted to smooth the profile of the rerouted trajectory for satisfying the UAV physical constraints. Lastly, a flight controller based on passivity-based control (PBC) is designed to maintain UAV’s stability and trajectory tracking performance. simulation results demonstrate that the UAV with the proposed strategy is capable of avoiding obstacles in a hostile environment.

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