Energy-Efficient Robot Configuration and Motion Planning Using Genetic Algorithm and Particle Swarm Optimization

The implementation of Industry 5.0 necessitates a decrease in the energy consumption of industrial robots. This research investigates energy optimization for optimal motion planning for a dual-arm industrial robot. The objective function for the energy minimization problem is stated based on the execution time and total energy consumption of the robot arm configurations in its workspace for pick-and-place operation. Firstly, the PID controller is being used to achieve the optimal parameters. The parameters of PID are then fine-tuned using metaheuristic algorithms such as Genetic Algorithms and Particle Swarm Optimization methods to create a more precise robot motion trajectory, resulting in an energy-efficient robot configuration. The results for different robot configurations were compared with both motion planning algorithms, which shows better compatibility in terms of both execution time and energy efficiency. The feasibility of the algorithms is demonstrated by conducting experiments on a dual-arm robot, named as duAro. In terms of energy efficiency, the results show that dual-arm motions can save more energy than single-arm motions for an industrial robot. Furthermore, combining the robot configuration problem with metaheuristic approaches saves energy consumption and robot execution time when compared to motion planning with PID controllers alone

Real-Time Robot Motion Planning Algorithms and Applications Under Uncertainty

Robot motion planning is an important problem for real-world robot applications. Recently, the separation of workspaces between humans and robots has been gradually fading, and there is strong interest in developing solutions where collaborative robots (cobots) can interact or work safely with humans in a shared space or in close proximity. When working with humans in real-world environments, the robots need to plan safe motions under uncertainty stemming from many sources such as noise of visual sensors, ambiguity of verbal instruction, and variety of human motions. In this thesis, we propose novel optimization-based and learning-based robot motion planning algorithms to deal with the uncertainties in real-world environments. To handle the input noise of visual cameras and the uncertainty of shape and pose estimation of surrounding objects, we present efficient probabilistic collision detection algorithms for Gaussian and non-Gaussian error distributions. By efficiently computing upper bounds of collision probability between an object and a robot, we present novel trajectory planning algorithms that guarantee that the collision probability at any trajectory point is less than a user-specified threshold. To enable human-robot interaction using natural language instructions, we present a mapping function from grounded linguistic semantics to the coefficients of the motion planning optimization problem. The mapping function considers task descriptions and motion-related constraints. For collaborative robots working with a human in close proximity, we present human intention and motion prediction algorithms for efficient task ordering and safe motion planning. The robot observes the human poses in real-time and predicts the future human motion based on the history of human poses. We also present an occlusion-aware robot motion planning algorithm that accounts for occlusion in the visual sensor data and uses learning-based techniques for trajectory planning. We highlight the benefits of our collision detection and robot motion planning algorithms with a 7-DOF Fetch robot arm in simulated and real-world environments.Doctor of Philosoph

Accelerating Motion Planning via Optimal Transport

Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder real-time, efficient decision-making. A class of methods striving to provide smooth solutions is gradient-based trajectory optimization. However, those methods usually suffer from bad local minima, while for many settings, they may be inapplicable due to the absence of easy-to-access gradients of the optimization objectives. In response to these issues, we introduce Motion Planning via Optimal Transport (MPOT) -- a \textit{gradient-free} method that optimizes a batch of smooth trajectories over highly nonlinear costs, even for high-dimensional tasks, while imposing smoothness through a Gaussian Process dynamics prior via the planning-as-inference perspective. To facilitate batch trajectory optimization, we introduce an original zero-order and highly-parallelizable update rule: the Sinkhorn Step, which uses the regular polytope family for its search directions. Each regular polytope, centered on trajectory waypoints, serves as a local cost-probing neighborhood, acting as a \textit{trust region} where the Sinkhorn Step "transports" local waypoints toward low-cost regions. We theoretically show that Sinkhorn Step guides the optimizing parameters toward local minima regions of non-convex objective functions. We then show the efficiency of MPOT in a range of problems from low-dimensional point-mass navigation to high-dimensional whole-body robot motion planning, evincing its superiority compared to popular motion planners, paving the way for new applications of optimal transport in motion planning.Comment: Published as a conference paper at NeurIPS 2023. Project website: https://sites.google.com/view/sinkhorn-step