Category Archives: Robot Motion Planning

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

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.

A novel paradigm for motion planning based on probabilistic inference

Mukadam, M., Dong, J., Yan, X., Dellaert, F., & Boots, B. , Continuous-time Gaussian process motion planning via probabilistic inference, The International Journal of Robotics Research, 37(11), 1319–1340, DOI: 10.1177/0278364918790369.

We introduce a novel formulation of motion planning, for continuous-time trajectories, as probabilistic inference. We first show how smooth continuous-time trajectories can be represented by a small number of states using sparse Gaussian process (GP) models. We next develop an efficient gradient-based optimization algorithm that exploits this sparsity and GP interpolation. We call this algorithm the Gaussian Process Motion Planner (GPMP). We then detail how motion planning problems can be formulated as probabilistic inference on a factor graph. This forms the basis for GPMP2, a very efficient algorithm that combines GP representations of trajectories with fast, structure-exploiting inference via numerical optimization. Finally, we extend GPMP2 to an incremental algorithm, iGPMP2, that can efficiently replan when conditions change. We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments. Our evaluation reveals that GPMP2 is several times faster than previous algorithms while retaining robustness. We also benchmark iGPMP2 on replanning problems, and show that it can find successful solutions in a fraction of the time required by GPMP2 to replan from scratch.

On the need to replanning in POMDPs when applied to real systems, due to imperfect sensing and computational cost of online planning

Ali-akbar Agha-mohammadi et al., SLAP: Simultaneous Localization and Planning Under Uncertainty via Dynamic Replanning in Belief Space, IEEE Transactions on Robotics, vol. 34, no. 5, DOI: 10.1109/TRO.2018.2838556.

Simultaneous localization and planning (SLAP) is a crucial ability for an autonomous robot operating under uncertainty. In its most general form, SLAP induces a continuous partially observable Markov decision process (POMDP), which needs to be repeatedly solved online. This paper addresses this problem and proposes a dynamic replanning scheme in belief space. The underlying POMDP, which is continuous in state, action, and observation space, is approximated offline via sampling-based methods, but operates in a replanning loop online to admit local improvements to the coarse offline policy. This construct enables the proposed method to combat changing environments and large localization errors, even when the change alters the homotopy class of the optimal trajectory. It further outperforms the state-of-the-art Feedback-based Information RoadMap (FIRM) method by eliminating unnecessary stabilization steps. Applying belief space planning to physical systems brings with it a plethora of challenges. A key focus of this paper is to implement the proposed planner on a physical robot and show the SLAP solution performance under uncertainty, in changing environments and in the presence of large disturbances, such as a kidnapped robot situation.

A performance metric for evaluating and comparing robot navigation algorithms

Yazhini Chitra Pradeep, Kendrick Amezquita-Semprun, Manuel Del Rosario, Peter C.Y. Chen, The Pc metric: A performance measure for collision avoidance algorithms, Robotics and Autonomous Systems, Volume 109, 2018, Pages 125-138, DOI: 10.1016/j.robot.2018.08.005.

Despite the comprehensive development in the field of navigation algorithms for mobile robots, the research on performance metrics and evaluation procedures for making standardized quantitative comparison between different algorithms has gained attention only recently. This work attempts to contribute with such effort by introducing a performance metric for the assessment of collision avoidance algorithms for mobile robots. The proposed metric comprehensively evaluates the actions taken by the objects and their consequences, in a given scenario of any given collision avoidance algorithm, based on the concept of probability of collision. The contribution of the paper encompasses the definition of the metric, the methodology to estimate the metric, and the framework to apply the metric for any given scenario. Experiments and numerical simulations are conducted to validate and demonstrate the effectiveness of the proposed metric in performance evaluation and comparison among different collision avoidance algorithms.

Using reasoning to improve low-level robot navigation

Muhayyuddin, Aliakbar AkbariJan Rosell, A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques, Journal of Intelligent & Robotic Systems, September 2018, Volume 91, Issue 3–4, pp 459–477, DOI: 10.1007/s10846-017-0698-z.

Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called κ-PMP can be used with any kinodynamic planner, thus giving rise to e.g. κ-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths.

A unifying framework for path planning in real-time (mainly for UAVs) and a nice summary of the state-of-the-art in modern path planning

M. Murillo, G. SánchezL. GenzelisL. Giovanini, A Real-Time Path-Planning Algorithm based on Receding Horizon Techniques, Journal of Intelligent & Robotic Systems, September 2018, Volume 91, Issue 3–4, pp 445–457, DOI: 10.1007/s10846-017-0740-1.

In this article we present a real-time path-planning algorithm that can be used to generate optimal and feasible paths for any kind of unmanned vehicle (UV). The proposed algorithm is based on the use of a simplified particle vehicle (PV) model, which includes the basic dynamics and constraints of the UV, and an iterated non-linear model predictive control (NMPC) technique that computes the optimal velocity vector (magnitude and orientation angles) that allows the PV to move toward desired targets. The computed paths are guaranteed to be feasible for any UV because: i) the PV is configured with similar characteristics (dynamics and physical constraints) as the UV, and ii) the feasibility of the optimization problem is guaranteed by the use of the iterated NMPC algorithm. As demonstration of the capabilities of the proposed path-planning algorithm, we explore several simulation examples in different scenarios. We consider the existence of static and dynamic obstacles and a follower condition.

Including the dynamics of the environment in robot motion planning (navigation)

María-Teresa Lorente, Eduardo Owen, and Luis Montano, Model-based robocentric planning and navigation for dynamic environments, The International Journal of Robotics Research Vol 37, Issue 8, pp. 867 – 889 DOI: 10.1177/0278364918775520.

This work addresses a new technique of motion planning and navigation for differential-drive robots in dynamic environments. Static and dynamic objects are represented directly on the control space of the robot, where decisions on the best motion are made. A new model representing the dynamism and the prediction of the future behavior of the environment is defined, the dynamic object velocity space (DOVS). A formal definition of this model is provided, establishing the properties for its characterization. An analysis of its complexity, compared with other methods, is performed. The model contains information about the future behavior of obstacles, mapped on the robot control space. It allows planning of near-time-optimal safe motions within the visibility space horizon, not only for the current sampling period. Navigation strategies are developed based on the identification of situations in the model. The planned strategy is applied and updated for each sampling time, adapting to changes occurring in the scenario. The technique is evaluated in randomly generated simulated scenarios, based on metrics defined using safety and time-to-goal criteria. An evaluation in real-world experiments is also presented.

A review on mobile robot navigation

Tzafestas, S.G. , Mobile Robot Control and Navigation: A Global Overview,J Intell Robot Syst (2018) 91: 35 DOI: 10.1007/s10846-018-0805-9.

The aim of this paper is to provide a global overview of mobile robot control and navigation methodologies developed over the last decades. Mobile robots have been a substantial contributor to the welfare of modern society over the years, including the industrial, service, medical, and socialization sectors. The paper starts with a list of books on autonomous mobile robots and an overview of survey papers that cover a wide range of decision, control and navigation areas. The organization of the material follows the structure of the author’s recent book on mobile robot control. Thus, the following aspects of wheeled mobile robots are considered: kinematic modeling, dynamic modeling, conventional control, affine model-based control, invariant manifold-based control, model reference adaptive control, sliding-mode control, fuzzy and neural control, vision-based control, path and motion planning, localization and mapping, and control and software architectures.

Evaluating the safeness of a motion plan for mobile robot navigation

Brian Axelrod, Leslie Pack Kaelbling, and Tomás Lozano-Pérez Provably safe robot navigation with obstacle uncertainty, The International Journal of Robotics Research Vol 37, Issue 7 DOI: 10.1177/0278364918778338.

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most ϵ collision probability (ϵ-safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.

A novel approach to avoid the minima problem in potential fields navigation

Fedele, G., D’Alfonso, L., Chiaravalloti, F. et al., Obstacles Avoidance Based on Switching Potential Functions, J Intell Robot Syst (2018) 90: 387. DOI: 10.1007/s10846-017-0687-2.

In this paper, a novel path planning and obstacles avoidance method for a mobile robot is proposed. This method makes use of a switching strategy between the attractive potential of the target and a new helicoidal potential field which allows to bypass an obstacle by driving the robot around it. The new technique aims at overcoming the local minima problems of the well-known artificial potentials method, caused by the summation of two (or more) potential fields. In fact, in the proposed approach, only a single potential is used at a time. The resulting proposed technique uses only local information and ensures high robustness, in terms of achieved performance and computational complexity, w.r.t. the number of obstacles. Numerical simulations, together with comparisons with existing methods, confirm a very robust behavior of the method, also in the case of a framework with multiple obstacles.