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.
The complexity of the physics-based motion planning is very high due to the high-dimensional state space, large search space and highly constrained solution set. A few physics-based motion planning approaches have been proposed that addressed the above mentioned issues, such as the Behavioral Kinodynamic Balanced Growth Trees (BK-BGT) and the Behavioral Kinodynamic Rapidly-Exploring Random Trees (BK-RRT) proposed in @cite_32 that use a strategy to reduce the search space based on a nondeterministic tactic modeled using a finite state machine, along with skills used to control the sampling. The propagation step is performed using PhysX @cite_24 . A hybrid approach is proposed in @cite_17 that equips the physics-based motion planner with knowledge (in the form of ontologies) about the robot's manipulation world. It uses a knowledge-based reasoning process to reduce the robot search space and guide the motion planner by defining the way objects can be manipulated. It uses RRT and KPIECE as kinodynamic motion planner and ODE as state propagator. This approach has also been used in task planning approaches @cite_0 @cite_11 for the physics-based reasoning process to determine the feasibility of the plan by evaluating the dynamic cost of each subaction in the task plan.
@cite_32 @cite_17 @cite_24 @cite_0 @cite_11
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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. We present Kinodynamic RRT*, an incremental sampling-based approach for asymptotically optimal motion planning for robots with linear dynamics. Our approach extends RRT*, which was introduced for holonomic robots [10], by using a fixed-final-state-free-final-time controller that optimally connects any pair of states, where the cost function is expressed as a trade-off between the duration of a trajectory and the expended control effort. Our approach generalizes earlier work on RRT* for kinodynamic systems, as it guarantees asymptotic optimality for any system with controllable linear dynamics, in state spaces of any dimension. In addition, we show that for the rich subclass of systems with a nilpotent dynamics matrix, closed-form solutions for optimal trajectories can be derived, which keeps the computational overhead of our algorithm compared to traditional RRT* at a minimum. We demonstrate the potential of our approach by computing asymptotically optimal trajectories in three challenging motion planning scenarios: (i) a planar robot with a 4-D state space and double integrator dynamics, (ii) an aerial vehicle with a 10-D state space and linearized quadrotor dynamics, and (iii) a car-like robot with a 5-D state space and non-linear dynamics. Planning motions to grasp an object in cluttered and uncertain environments is a challenging task, particularly when a collision-free trajectory does not exist and objects obstructing the way are required to be carefully grasped and moved out. This letter takes a different approach and proposes to address this problem by using a randomized physics-based motion planner that permits robot-object and object-object interactions. The main idea is to avoid an explicit high-level reasoning of the task by providing the motion planner with a physics engine to evaluate possible complex multibody dynamical interactions. The approach is able to solve the problem in complex scenarios, also considering uncertainty in the objects' pose and in the contact dynamics. The work enhances the state validity checker, the control sampler, and the tree exploration strategy of a kinodynamic motion planner called KPIECE. The enhanced algorithm, called p-KPIECE, has been validated in simulation and with real experiments. The results have been compared with an ontological physics-based motion planner and with task and motion planning approaches, resulting in a significant improvement in terms of planning time, success rate, and quality of the solution path. We consider motion planning problems for a vehicle with kinodynamic constraints, where there is partial knowledge about the environment and replanning is required. We present a new tree-based planner that explicitly deals with kinodynamic constraints and addresses the safety issues when planning under finite computation times, meaning that the vehicle avoids collisions in its evolving configuration space. In order to achieve good performance we incrementally update a tree data-structure by retaining information from previous steps and we bias the search of the planner with a greedy, yet probabilistically complete state space exploration strategy. Moreover, the number of collision checks required to guarantee safety is kept to a minimum. We compare our technique with alternative approaches as a standalone planner and show that it achieves favorable performance when planning with dynamics. We have applied the planner to solve a challenging replanning problem involving the mapping of an unknown workspace with a nonholonomic platform