Mobile robotics in agricultural operations: A narrative review on planning aspects

The advent of mobile robots in agriculture has signaled a digital transformation with new automation technologies optimize a range of labor-intensive, resources-demanding, and time-consuming agri-field operations. To that end a generally accepted technical lexicon for mobile robots is lacking as pertinent terms are often used interchangeably. This creates confusion among research and practice stakeholders. In addition, a consistent definition of planning attributes in automated agricultural operations is still missing as relevant research is sparse. In this regard, a “narrative” review was adopted (1) to provide the basic terminology over technical aspects of mobile robots used in autonomous operations and (2) assess fundamental planning aspects of mobile robots in agricultural environments. Based on the synthesized evidence from extant studies, seven planning attributes have been included: (i) high-level control-specific attributes, which include reasoning architecture, the world model, and planning level, (ii) operation-specific attributes, which include locomotion–task connection and capacity constraints, and (iii) physical robot-specific attributes, which include vehicle configuration and vehicle kinematics.</jats:p

Predictive and Multi-rate Sensor-Based Planning under Uncertainty

Email Print Request Permissions In this paper, a general formulation of a predictive and multirate (MR) reactive planning method for intelligent vehicles (IVs) is introduced. The method handles path planning and trajectory planning for IVs in dynamic environments with uncertainty, in which the kinodynamic vehicle constraints are also taken into account. It is based on the potential field projection method (PFP), which combines the classical potential field (PF) method with the MR Kalman filter estimation. PFP takes into account the future object trajectories and their associated uncertainties, which makes it different from other look-ahead approaches. Here, a new PF is included in the Lagrange-Euler formulation in a natural way, accounting for the vehicle dynamics. The resulting accelerations are translated into control inputs that are considered in the estimation process. This leads to the generation of a local trajectory in real time (RT) that fully meets the constraints imposed by the kinematic and dynamic models of the IV. The properties of the method are demonstrated by simulation with MATLAB and C++ applications. Very good performance and execution times are achieved, even in challenging situations. In a scenario with 100 obstacles, a local trajectory is obtained in less than 1 s, which is suitable for RT applications

Integrated Trajectory-Tracking and Vibration Control of Kinematically-Constrained Warehousing Cable Robots

With the explosion of e-commerce in recent years, there is a strong desire for automated material handling solutions including warehousing robots. Cable driven parallel robots (CDPRs) are a relatively new concept which has yet to be explored for high-speed pick-&-place applications in the industry. Compared to rigid-link parallel robots, a CDPR possesses significant advantages including: large workspace, low moving inertia, high-speed motion, low power consumption, and incurring minimal maintenance cost. On the other hand, the main disadvantages of the CDPRs are the cable’s unilateral force exerting capability and low rigidity which is resulting in undesired vibrations of their moving platform. Kinematically-constrained CDPRs (KC-CDPRs) include a special class of CDPRs which provide a considerably higher level of stiffness in undesired degrees of freedom (DOFs) via connecting a set of constrained cables to the same actuator. Nevertheless, undesired vibrations of the moving platform are still their main problem which request more attention and investigation. Dynamic modeling, stiffness optimization, vibration and trajectory-tracking control, and stiffness-based trajectory-planning of redundant KC-CDPRs are studied in this thesis. As a new technique, we separate the moving platform’s vibration equations from its desired (nominal) equations of motion. The obtained vibration model forms a linear parametric variable (LPV) dynamic system which is based for the following contributions: 1) Proposing a new tension optimization approach to minimize undesired perturbations under external disturbances in a desired direction; and demonstrating the effectiveness of kinematically-constrained actuation method in vibration attenuation of CDPRs in undesired DOFs. 2) Providing the opportunity of using a wide class of well-established robust and optimal LPV-based control methods, such as H∞ control techniques, for trajectory-tracking control of CDPRs to minimize the effect of disturbances on the robot operation; and showing the effectiveness of kinematically-constrained actuation method in control design simplification of such robots. 3) Proposing the concept of stiffness-based trajectory-planning to find the stiffness-optimum geometry of trajectories for KC-CDPRs; and designing a time-optimal zero-to-zero continuous-jerk motion to track such trajectories. All the proposed concepts are developed for a generic KC-CDPR and verified via numerical analysis and experimental tests of a real planar warehousing KC-CDPR

Experience-driven optimal motion synthesis in complex and shared environments

Optimal loco-manipulation planning and control for high-dimensional systems based on general, non-linear optimisation allows for the specification of versatile motion subject to complex constraints. However, complex, non-linear system and environment dynamics, switching contacts, and collision avoidance in cluttered environments introduce non-convexity and discontinuity in the optimisation space. This renders finding optimal solutions in complex and changing environments an open and challenging problem in robotics. Global optimisation methods can take a prohibitively long time to converge. Slow convergence makes them unsuitable for live deployment and online re-planning of motion policies in response to changes in the task or environment. Local optimisation techniques, in contrast, converge fast within the basin of attraction of a minimum but may not converge at all without a good initial guess as they can easily get stuck in local minima. Local methods are, therefore, a suitable choice provided we can supply a good initial guess. If a similarity between problems can be found and exploited, a memory of optimal solutions can be computed and compressed efficiently in an offline computation process. During runtime, we can query this memory to bootstrap motion synthesis by providing a good initial seed to the local optimisation solver. In order to realise such a system, we need to address several connected problems and questions: First, the formulation of the optimisation problem (and its parametrisation to allow solutions to transfer to new scenarios), and related, the type and granularity of user input, along with a strategy for recovery and feedback in case of unexpected changes or failure. Second, a sampling strategy during the database/memory generation that explores the parameter space efficiently without resorting to exhaustive measures---i.e., to balance storage size/memory with online runtime to adapt/repair the initial guess. Third, the question of how to represent the problem and environment to parametrise, compute, store, retrieve, and exploit the memory efficiently during pre-computation and runtime. One strategy to make the problem computationally tractable is to decompose planning into a series of sequential sub-problems, e.g., contact-before-motion approaches which sequentially perform goal state planning, contact planning, motion planning, and encoding. Here, subsequent stages operate within the null-space of the constraints of the prior problem, such as the contact mode or sequence. This doctoral thesis follows this line of work. It investigates general optimisation-based formulations for motion synthesis along with a strategy for exploration, encoding, and exploitation of a versatile memory-of-motion for providing an initial guess to optimisation solvers. In particular, we focus on manipulation in complex environments with high-dimensional robot systems such as humanoids and mobile manipulators. The first part of this thesis focuses on collision-free motion generation to reliably generate motions. We present a general, collision-free inverse kinematics method using a combination of gradient-based local optimisation with random/evolution strategy restarting to achieve high success rates and avoid local minima. We use formulations for discrete collision avoidance and introduce a novel, computationally fast continuous collision avoidance objective based on conservative advancement and harmonic potential fields. Using this, we can synthesise continuous-time collision-free motion plans in the presence of moving obstacles. It further enables to discretise trajectories with fewer waypoints, which in turn considerably reduces the optimisation problem complexity, and thus, time to solve. The second part focuses on problem representations and exploration. We first introduce an efficient solution encoding for trajectory library-based approaches. This representation, paired with an accompanying exploration strategy for offline pre-computation, permits the application of inexpensive distance metrics during runtime. We demonstrate how our method efficiently re-uses trajectory samples, increases planning success rates, and reduces planning time while being highly memory-efficient. We subsequently present a method to explore the topological features of the solution space using tools from computational homology. This enables us to cluster solutions according to their inherent structure which increases the success of warm-starting for problems with discontinuities and multi-modality. The third part focuses on real-world deployment in laboratory and field experiments as well as incorporating user input. We present a framework for robust shared autonomy with a focus on continuous scene monitoring for assured safety. This framework further supports interactive adjustment of autonomy levels from fully teleoperated to automatic execution of stored behaviour sequences. Finally, we present sensing and control for the integration and embodiment of the presented methodology in high-dimensional real-world platforms used in laboratory experiments and real-world deployment. We validate our presented methods using hardware experiments on a variety of robot platforms demonstrating generalisation to other robots and environments

UAS Flight Path Planning and Collision Avoidance Based on Markov Decision Process

The growing interest and trend for deploying unmanned aircraft systems (UAS) in civil applications require robust traffic management approaches that can safely integrate the unmanned platforms into the airspace. Although there have been significant advances in autonomous navigation, especially in the ground vehicles domain, there are still challenges to address for navigation in a dynamic 3D environment that airspace presents. An integrated approach that facilitates semi-autonomous operations in dynamic environments and also allows for operators to stay in the loop for intervention may provide a workable and practical solution for safe UAS integration in the airspace. This thesis research proposes a new path planning method for UAS flying in a dynamic 3D environment shared by multiple aerial vehicles posing potential conflict risks. This capability is referred to as de-confliction in drone traffic management. It primarily targets applications such as UAM [1] where multiple flying manned and/or unmanned aircraft may be present. A new multi-staged algorithm is designed that combines AFP method and Harmonic functions with AKF and MDP for dynamic path planning. It starts with the prediction of aircraft traffic density in the area and then generates the UAS flight path in a way to minimize the risk of encounters and potential conflicts. Hardware-in-the-loop simulations of the algorithm in various scenarios are presented, with an RGB-D camera and Pixhawk Autopilot to track the target. Numerical simulations show satisfactory results in various scenarios for path planning that considerably reduces the risk of conflict with other static and dynamic obstacles. A comparison with the potential field is provided that illustrates the robust and fast of the MDP algorithm