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Safe deployment of autonomous robots in diverse scenarios requires agents that are capable of efficiently adapting to new environments while satisfying constraints. In this article, we propose a practical and theoretically justified approach to maintain safety in the presence of dynamics uncertainty. Our approach leverages Bayesian meta-learning with last-layer adaptation. The expressiveness of neural-network features trained offline, paired with efficient last-layer online adaptation, enables the derivation of tight confidence sets, which contract around the true dynamics as the model adapts online. We exploit these confidence sets to plan trajectories that guarantee the safety of the system. Our approach handles problems with high dynamics uncertainty, where reaching the goal safely is potentially initially infeasible, by first exploring to gather data and reduce uncertainty, before autonomously exploiting the acquired information to safely perform the task. Under reasonable assumptions, we prove that our framework guarantees the high-probability satisfaction of all constraints at all times jointly, i.e., over the total task duration. This theoretical analysis also motivates two regularizers of last-layer meta-learning models that improve online adaptation capabilities as well as performance by reducing the size of the confidence sets. We extensively demonstrate our approach in simulation and on hardware. [ABSTRACT FROM AUTHOR]

In this article, we present an optimization-based framework for multicopter trajectory planning subject to geometrical configuration constraints and user-defined dynamic constraints. The basis of the framework is a novel trajectory representation built upon our novel optimality conditions for unconstrained control effort minimization. We design linear-complexity operations on this representation to conduct spatial–temporal deformation under various planning requirements. Smooth maps are utilized to exactly eliminate geometrical constraints in a lightweight fashion. A variety of state-input constraints are supported by the decoupling of dense constraint evaluation from sparse parameterization and the backward differentiation of flatness map. As a result, this framework transforms a generally constrained multicopter planning problem into an unconstrained optimization that can be solved reliably and efficiently. Our framework bridges the gaps among solution quality, planning efficiency, and constraint fidelity for a multicopter with limited resources and maneuvering capability. Its generality and robustness are both demonstrated by applications to different flight tasks. Extensive simulations and benchmarks are also conducted to show its capability of generating high-quality solutions while retaining the computation speed against other specialized methods by orders of magnitude. [ABSTRACT FROM AUTHOR]

The objective of trajectory optimization algorithms is to achieve an optimal collision-free path between start and goal states. In real-world scenarios, where environments can be complex and nonhomogeneous, a robot needs to be able to gauge whether a state will be in collision with various objects in order to meet some safety metrics. The collision detector should be computationally efficient and, ideally, analytically differentiable to facilitate stable and rapid gradient descent during optimization. However, methods today lack an elegant approach to detect collision differentiably, relying rather on numerical gradients that can be unstable. We present DiffCo, the first, fully autodifferentiable, nonparametric model for collision detection. Its nonparametric behavior allows one to compute collision boundaries on the fly and update them, requiring no pretraining and allowing it to update continuously in dynamic environments. It provides robust gradients for trajectory optimization via backpropagation and is often 10–100 times faster to compute than its geometric counterparts. DiffCo also extends trivially to modeling different object collision classes for semantically informed trajectory optimization. [ABSTRACT FROM AUTHOR]

Policy search and model predictive control (MPC) are two different paradigms for robot control: policy search has the strength of automatically learning complex policies using experienced data, and MPC can offer optimal control performance using models and trajectory optimization. An open research question is how to leverage and combine the advantages of both approaches. In this article, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel policy-search-for-model-predictive-control framework. Specifically, we formulate the MPC as a parameterized controller, where the hard-to-optimize decision variables are represented as high-level policies. Such a formulation allows optimizing policies in a self-supervised fashion. We validate this framework by focusing on a challenging problem in agile drone flight: flying a quadrotor through fast-moving gates. Experiments show that our controller achieves robust and real-time control performance in both simulation and the real world. The proposed framework offers a new perspective for merging learning and control. [ABSTRACT FROM AUTHOR]

Placing robotsoutside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduction of obstacles or the placement of additional robots in the workspace and the modification of the joint range due to faults or range-of-motion constraints are typical cases, where the adaptation capabilities play a key role for safely performing the robot’s task. Probabilistic movement primitives (ProMPs) have been proposed for representing adaptable movement skills, which are modeled as Gaussian distributions over trajectories. These are analytically tractable and can be learned from a small number of demonstrations. However, both the original ProMP formulation and the subsequent approaches only provide solutions to specific movement adaptation problems, e.g., obstacle avoidance, and a generic, unifying, probabilistic approach to adaptation is missing. In this article, we develop a generic probabilistic framework for adapting ProMPs. We unify previous adaptation techniques, for example, various types of obstacle avoidance, via-points, and mutual avoidance, in one single framework and combine them to solve complex robotic problems. Additionally, we derive novel adaptation techniques such as temporally unbound via-points and mutual avoidance. We formulate adaptation as a constrained optimization problem, where we minimize the Kullback–Leibler divergence between the adapted distribution and the distribution of the original primitive,while we constrain the probability mass associated with undesired trajectories to be low. We demonstrate our approach on several adaptation problems on simulated planar robot arms and seven-degree-of-freedom Franka Emika robots in a dual-robot-arm setting. [ABSTRACT FROM AUTHOR]

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process. [ABSTRACT FROM AUTHOR]

Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a “stop” condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8 m/s, and experiments at the maximum speed of a skid-steer ground robot (2 m/s). [ABSTRACT FROM AUTHOR]

This article describes a topological approach to generating families of open- and closed-loop walking gaits for underactuated 2-D and 3-D biped walkers subject to configuration inequality constraints, physical holonomic constraints (e.g., closed-loop linkages), and virtual holonomic constraints (user-defined constraints enforced through feedback control). Our method constructs implicitly defined manifolds of feasible periodic gaits within a state-time-control space that parameterizes the biped’s hybrid trajectories. Since equilibrium configurations of the biped often belong to such manifolds, we use equilibria as “templates” from which to grow the gait families. Equilibria are reliable seeds for the construction of gait families, eliminating the need for random, intuited, or bio-inspired initial guesses at feasible trajectories in an optimization framework. We demonstrate the approach on several 2-D and 3-D biped walkers. [ABSTRACT FROM AUTHOR]

This article presents MADER, a 3-D decentralized and asynchronous trajectory planner for UAVs that generates collision-free trajectories in environments with static obstacles, dynamic obstacles, and other planning agents. Real-time collision avoidance with other dynamic obstacles or agents is done by performing outer polyhedral representations of every interval of the trajectories and then including the plane that separates each pair of polyhedra as a decision variable in the optimization problem. MADER uses our recently developed MINVO basis to obtain outer polyhedral representations with volumes 2.36 and 254.9 times, respectively, smaller than the Bernstein or B-Spline bases used extensively in the planning literature. Our decentralized and asynchronous algorithm guarantees safety with respect to other agents by including their committed trajectories as constraints in the optimization and then executing a collision check-recheck scheme. Finally, extensive simulations in challenging cluttered environments show up to a 33.9% reduction in the flight time, and a 88.8% reduction in the number of stops compared to the Bernstein and B-Spline bases, shorter flight distances than centralized approaches, and shorter total times on average than synchronous decentralized approaches. [ABSTRACT FROM AUTHOR]

In the article, we present an efficient optimization framework that solves trajectory optimization problems by decoupling state variables from timing variables, thereby decomposing a challenging nonlinear programming (NLP) problem into two easier subproblems. With timing fixed, the state variables can be optimized efficiently using convex optimization, and the timing variables can be optimized in a separate NLP, which forms a bilevel optimization problem. The challenge of obtaining the gradient of the timing variables is solved by sensitivity analysis of parametric NLPs. The exact analytic gradient is computed from the dual solution as a by-product, whereas existing finite-difference techniques require additional optimization. The bilevel optimization framework efficiently optimizes both timing and state variables which is demonstrated on generating trajectories for an UAV. Numerical experiments demonstrate that bilevel optimization converges significantly more reliably than a standard NLP solver, and analytical gradients outperform finite differences in terms of computation speed and accuracy. Physical experiments demonstrate its real-time applicability for reactive target tracking tasks. [ABSTRACT FROM AUTHOR]

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1 for the community. [ABSTRACT FROM AUTHOR]

We report on the methodology of developing a hybrid model and utilizing it as a template to initiate running behavior in a legged robot. The hybrid model is comprised of a physics-based, rolling, spring-loaded, inverted pendulum (R-SLIP) model, and a data-driven model that compensates for unmodeled dynamics using Gaussian process (GP) regression. The hybrid R-SLIP-GP model retains the R-SLIP's intrinsic running dynamics, as well as improves the template's accuracy without intensive data training and iteration efforts. The proposed hybrid model was evaluated via simulation and in empirical robot running experiments. The results confirm that the added GP-based model greatly improved the model's accuracy, especially discrepancies resulting from the originally difficult-to-model complex leg-ground interactions. In addition, the R-SLIP-GP model was utilized as a complete motion template to design and control the robot's running motion. The experimental results demonstrate that the hybrid template can initiate more stable and power-efficient running motion in the robot than using the R-SLIP template. [ABSTRACT FROM AUTHOR]

This article presents a novel recursive smooth trajectory (RST) generation algorithm for application in robotics and in particular for unmanned aerial vehicles (UAVs). RST builds the trajectory recursively as a smooth polynomial path, thus a closed form trajectory satisfying any arbitrary dynamic limitation that translates into kinematic constraints (e.g., position, velocity, acceleration, etc.). Uncertainties and perturbations in the constraints are also discussed, modeled, and finally included in the calculation of the polynomial trajectory coefficients. Moreover, due to its recursive formulation, RST enables an immediate extension toward the solution of a trajectory optimization problem. In particular, it is shown that the minimum-snap piecewise polynomial trajectory can be interpreted as a special case of RST. The effectiveness of the proposed algorithm is demonstrated numerically via two illustrative scenarios. Its application to a UAV structure is also discussed to highlight the advantage of a smooth path over a piecewise one. Finally, the computational complexity and memory requirements are analyzed. [ABSTRACT FROM AUTHOR]

In this article, we propose a nonlinear receding horizon game-theoretic planner for autonomous cars in competitive scenarios with other cars. The online planner is specifically formulated for a multiple-car autonomous racing game, in which each car tries to advance along a given track as far as possible with respect to the other cars. The algorithm extends previous work on game-theoretic planning for single-integrator agents to be suitable for autonomous cars in the following ways: 1) by representing the trajectory as a piecewise polynomial; 2) incorporating bicycle kinematics into the trajectory; and 3) enforcing constraints on path curvature and acceleration. The game-theoretic planner iteratively plans a trajectory for the ego vehicle and then the other vehicles in sequence until convergence. Crucially, the trajectory optimization includes a sensitivity term that allows the ego vehicle to reason about how much the other vehicles will yield to the ego vehicle to avoid collisions. The resulting trajectories for the ego vehicle exhibit rich game strategies such as blocking, faking, and opportunistic overtaking. The game-theoretic planner is shown to significantly outperform a baseline planner using model-predictive control, which does not take interaction into account. The performance is validated in high-fidelity numerical simulations with three cars, in experiments with two small-scale autonomous cars, and in experiments with a full-scale autonomous car racing against a simulated vehicle (video is available at https://youtube.com/playlist?list=PLmIcLEh8KMje4rYBqRANDuKvqFvj7LCRp). [ABSTRACT FROM AUTHOR]

The time-optimal trajectory planning problem involves minimizing the time required to follow a path defined in space, subject to kinematic and dynamic constraints. Here, we introduce a novel technique, called the bisection algorithm (BA), which is fully implemented in C++ and extends dynamic programming approaches to the problem. These approaches, which rely on dividing the global problem into a series of simpler subproblems, become increasingly advantageous compared to direct transcription methods as the number of problem constraints increases. In contrast to nearly all other dynamic programming approaches, BA does not rely on finding a maximum-velocity curve or explicitly finding acceleration switching points during the trajectory planning process. Additionally, only one forward and one backward integration are used, during which all constraints are imposed. This approach is made feasible through careful control of the numerical integration process and the use of a bisection algorithm to resolve constraint violations during integration. BA is shown to be significantly simpler, faster, and more robust than recently proposed algorithms: a direct comparison is made for a series of paths to be followed by a serial manipulator, subject to kinematic constraints. The wide applicability of BA is then established by solving the time-optimal problem for a parallel manipulator following a complex path, subject to both kinematic and dynamic constraints. [ABSTRACT FROM AUTHOR]

Planning whole-body motions while taking into account the terrain conditions is a challenging problem for legged robots since the terrain model might produce many local minima. Our coupled planning method uses stochastic and derivatives-free search to plan both foothold locations and horizontal motions due to the local minima produced by the terrain model. It jointly optimizes body motion, step duration and foothold selection, and it models the terrain as a cost-map. Due to the novel attitude planning method, the horizontal motion plans can be applied to various terrain conditions. The attitude planner ensures the robot stability by imposing limits to the angular acceleration. Our whole-body controller tracks compliantly trunk motions while avoiding slippage, as well as kinematic and torque limits. Despite the use of a simplified model, which is restricted to flat terrain, our approach shows remarkable capability to deal with a wide range of noncoplanar terrains. The results are validated by experimental trials and comparative evaluations in a series of terrains of progressively increasing complexity. [ABSTRACT FROM AUTHOR]

Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot. [ABSTRACT FROM AUTHOR]

The wheeled inverted pendulum (WIP) is an underactuated, nonholonomic mechatronic system, and has been popularized commercially as the Segway. Designing a control law for motion planning, that incorporates the state and control constraints, while respecting the configuration manifold, is a challenging problem. In this article, we derive a discrete-time model of the WIP system using discrete mechanics and generate optimal trajectories for the WIP system by solving a discrete-time constrained optimal control problem. Furthermore, we describe a nonlinear continuous-time model with parameters for designing a closed-loop linear–quadratic regulator (LQR). A dual control architecture is implemented in which the designed optimal trajectory is, then, provided as a reference to the robot with the optimal control trajectory as a feedforward control action, and an LQR in the feedback mode is employed to mitigate noise and disturbances for ensuing stable motion of the WIP system. While performing experiments on the WIP system involving aggressive maneuvers with fairly sharp turns, we found a high degree of congruence in the designed optimal trajectories and the path traced by the robot while tracking these trajectories. This corroborates the validity of the nonlinear model and the control scheme. Finally, these experiments demonstrate the highly nonlinear nature of the WIP system and robustness of the control scheme. [ABSTRACT FROM AUTHOR]

Nonprehensile manipulation produces underconstraint motions that are sensitive to environmental dynamics. Legged locomotion constitutes a floating-based movement, whose dynamic is underactuated with respect to the inertial frame. When these two tasks are combined, system motion planning and control are complex due to their inherent underactuated features. This article presents a motion planning framework for a legged robot that uses its limbs for nonprehensile manipulation, as well as locomoting motions. First, issues related to the description of the robot–object–environment system and the task are presented. The velocity constraint that prevents separation and the force constraint that restricts interactive forces are then integrated into the system dynamic model to produce bounds on the system acceleration as a function of the system state. Then, we solve the motion planning problem by reducing the system dimensions in operational space and programming feasible trajectories within the phase plane. This approach is employed to control the quadruped robot TITAN-VIII to manipulate objects and locomote itself using Drive Mode, Inchworm Mode, Scoot Mode, and Throw Mode. Experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we investigate energy-optimal control of robots with ultracapacitor-based regenerative drive systems. Based on a previously introduced framework, a fairly generic model is considered for the robot and the drive system. An optimal control problem is formulated to find point-to point trajectories maximizing the amount of energy regenerated and stored in the capacitor. The optimization problem, its numerical solution, and an experimental evaluation are demonstrated using a PUMA manipulator with custom regenerative drives. Power flows, stored regenerative energy, and efficiency are evaluated. Tracking of optimal trajectories is enforced on the robot using a standard robust passivity based control approach. Experimental results show that when following optimal trajectories, a reduction of about $\text{10}\text{--}\text{22}\%$ in energy consumption can be achieved for the conditions of the study, relative to the nonregenerative case. [ABSTRACT FROM AUTHOR]

Trajectory replanning for quadrotors is essential to enable fully autonomous flight in unknown environments. Hierarchical motion planning frameworks, which combine path planning with path parameterization, are popular due to their time efficiency. However, the path planning cannot properly deal with nonstatic initial states of the quadrotor, which may result in nonsmooth or even dynamically infeasible trajectories. In this article, we present an efficient kinodynamic replanning framework by exploiting the advantageous properties of the B-spline, which facilitates dealing with the nonstatic state and guarantees safety and dynamical feasibility. Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm, which finds a feasible trajectory with minimum control effort and time. To compensate for the discretization induced by the EBK search, an elastic optimization approach is proposed to refine the control point placement to the optimal location. Systematic comparisons against the state-of-the-art are conducted to validate the performance. Comprehensive onboard experiments using two different vision-based quadrotors are carried out showing the general applicability of the framework. [ABSTRACT FROM AUTHOR]

This paper considers the problem of two-dimensional constrained trajectory optimization of a point-mass aerial robot for constant-maneuvering target localization using bearing-only measurement. A performance metric that can be utilized in trajectory optimization to maximize target observability is proposed first based on geometric conditions. One-step optimal maneuver that maximizes the observability criterion is then derived analytically for moving targets. The heading angle constraint is also incorporated in the proposed optimal maneuver derivation to support practical application. Numerical simulations with some comparisons are presented to validate the analytical findings. [ABSTRACT FROM AUTHOR]

Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads. [ABSTRACT FROM AUTHOR]

We describe a method for multirobot trajectory planning in known, obstacle-rich environments. We demonstrate our approach on a quadrotor swarm navigating in a warehouse setting. Our method consists of following three stages: 1) roadmap generation that generates sparse roadmaps annotated with possible interrobot collisions; 2) discrete planning that finds valid execution schedules in discrete time and space; 3) continuous refinement that creates smooth trajectories. We account for the downwash effect of quadrotors, allowing safe flight in dense formations. We demonstrate computational efficiency in simulation with up to 200 robots and physical plausibility with an experiment on 32 nano-quadrotors. Our approach can compute safe and smooth trajectories for hundreds of quadrotors in dense environments with obstacles in a few minutes. [ABSTRACT FROM AUTHOR]

This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of feasible planning problems in a state–cost space, whose solutions approach the optimum. This transformation yields a meta-algorithm that produces an asymptotically optimal planner, given any feasible kinodynamic planner as a subroutine. The meta-algorithm is proven to be asymptotically optimal and a formula is derived relating expected running time and solution suboptimality. It is directly applicable to a wide range of optimal planning problems because it does not resort to the use of steering functions or numerical boundary-value problem solvers. On a set of benchmark problems, it is demonstrated to perform, using the expansive space tree (EST) and rapidly-exploring random tree (RRT) algorithms as subroutines, at a level that is superior or comparable to related planners. [ABSTRACT FROM AUTHOR]

Reaction wheel systems (RWSs) are beneficial in improving bipedal walking stability, and theoretical work has shown that they can also improve the efficiency of preplanned walking motions. This work provides the first demonstration of RWS-related efficiency improvements on a physical robot and identifies the energy-saving paradigm. Gaits for a five-link planar biped are generated via trajectory optimization with and without reaction wheels. Comparisons of the resulting system behavior show a 5–10% efficiency advantage for robots with an RWS under typical walking conditions and a larger advantage near regions of marginal dynamic feasibility. The savings are mainly accomplished by enabling trajectories with better joint motor utilization, not by minimizing impact losses or acting as a kinetic energy recovery system. The RWS also improves centroidal angular momentum regulation, with the reduction in centroidal angular momentum amplitude reaching 30% at high speeds. Simulations and experiments with a five-link planar biped validate the efficiency improvements seen in optimization. This demonstrates the viability of using efficient inertial actuators to improve the efficiency of complex walking robots. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including recent advances in aerial swarm robotics, trajectory optimization for aerial robots or unmanned aerial vehicles (UAVs) and a distributed formation control algorithm for a group of aerial robots.